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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 18 Nov 2012 12:27:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/18/t1353259741qe8z961j70cqz24.htm/, Retrieved Mon, 29 Apr 2024 19:37:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190276, Retrieved Mon, 29 Apr 2024 19:37:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R       [Multiple Regression] [ws 7 berekening 2] [2012-11-18 17:27:26] [5948b95c00a54abd73f88aac58cf0e09] [Current]
-           [Multiple Regression] [ws 7 berekening 3] [2012-11-18 17:35:21] [4d16aec667f4ecb2462f7b90319797ea]
-    D      [Multiple Regression] [ws 7 berekening 4] [2012-11-18 18:08:14] [4d16aec667f4ecb2462f7b90319797ea]
-             [Multiple Regression] [ws 7 berekening 4] [2012-11-18 18:11:36] [4d16aec667f4ecb2462f7b90319797ea]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	83	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	9	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	9.5	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	12	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	31	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	10	7	16	8	71	44
9	29	31	10	9	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	15	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
9	32	32	9	5	12	11	63	36
9	37	35	14	6	15	14.5	76	47
9	39	34	16	12	16	13	74	45
9	29	34	16	12	15	9	67	42
9	37	36	15	11	12	10	73	43
9	35	34	16	10	12	15	70	43
9	30	28	12	7	8	20	53	32
9	38	34	16	12	13	12	77	45
9	34	35	16	14	11	12	80	48
9	31	35	14	11	14	14	52	31
9	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	12.5	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	85	52
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	67	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	36	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	85	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	11	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	37	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
9	38	34	14	8	9	19	69	43
10	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	38	12	8	12	18	84	50
10	34	36	13	8	13	16	69	46
11	27	26	13	10	10	24	66	43
11	31	26	12	8	14	14	81	47
11	38	33	17	14	16	20	82	50
11	34	39	15	10	10	18	72	43
11	24	30	10	8	11	23	54	33
11	30	33	14	11	14	12	78	48
11	26	25	11	12	12	14	74	44
11	34	38	13	12	9	16	82	50
11	27	37	16	12	9	18	73	41
11	37	31	12	5	11	20	55	34
11	36	37	16	12	16	12	72	44
11	41	35	12	10	9	12	78	47
11	29	25	9	7	13	17	59	35
11	36	28	12	12	16	13	72	44
11	32	35	15	11	13	9	78	44
11	37	33	12	8	9	16	68	43
11	30	30	12	9	12	18	69	41
11	31	31	14	10	16	10	67	41
11	38	37	12	9	11	14	74	42
11	36	36	16	12	14	11	54	33
11	35	30	11	6	13	9	67	41
11	31	36	19	15	15	11	70	44
11	38	32	15	12	14	10	80	48
11	22	28	8	12	16	11	89	55
11	32	36	16	12	13	19	76	44
11	36	34	17	11	14	14	74	43
11	39	31	12	7	15	12	87	52
11	28	28	11	7	13	14	54	30
11	32	36	11	5	11	21	61	39
11	32	36	14	12	11	13	38	11
11	38	40	16	12	14	10	75	44
11	32	33	12	3	15	15	69	42
11	35	37	16	11	11	16	62	41
11	32	32	13	10	15	14	72	44
11	37	38	15	12	12	12	70	44
11	34	31	16	9	14	19	79	48
11	33	37	16	12	14	15	87	53
11	33	33	14	9	8	19	62	37
11	26	32	16	12	13	13	77	44
11	30	30	16	12	9	17	69	44
11	24	30	14	10	15	12	69	40
11	34	31	11	9	17	11	75	42
11	34	32	12	12	13	14	54	35
11	33	34	15	8	15	11	72	43
11	34	36	15	11	15	13	74	45
11	35	37	16	11	14	12	85	55
11	35	36	16	12	16	15	52	31
11	36	33	11	10	13	14	70	44
11	34	33	15	10	16	12	84	50
11	34	33	12	12	9	17	64	40
11	41	44	12	12	16	11	84	53
11	32	39	15	11	11	18	87	54
11	30	32	15	8	10	13	79	49
11	35	35	16	12	11	17	67	40
11	28	25	14	10	15	13	65	41
11	33	35	17	11	17	11	85	52
11	39	34	14	10	14	12	83	52
11	36	35	13	8	8	22	61	36
11	36	39	15	12	15	14	82	52
11	35	33	13	12	11	12	76	46
11	38	36	14	10	16	12	58	31
11	33	32	15	12	10	17	72	44
11	31	32	12	9	15	9	72	44
11	34	36	13	9	9	21	38	11
11	32	36	8	6	16	10	78	46
11	31	32	14	10	19	11	54	33
11	33	34	14	9	12	12	63	34
11	34	33	11	9	8	23	66	42
11	34	35	12	9	11	13	70	43
11	34	30	13	6	14	12	71	43
11	33	38	10	10	9	16	67	44
11	32	34	16	6	15	9	58	36
11	41	33	18	14	13	17	72	46
11	34	32	13	10	16	9	72	44
11	36	31	11	10	11	14	70	43
11	37	30	4	6	12	17	76	50
11	36	27	13	12	13	13	50	33
11	29	31	16	12	10	11	72	43
11	37	30	10	7	11	12	72	44
11	27	32	12	8	12	10	88	53
11	35	35	12	11	8	19	53	34
11	28	28	10	3	12	16	58	35
11	35	33	13	6	12	16	66	40
11	37	31	15	10	15	14	82	53
11	29	35	12	8	11	20	69	42
11	32	35	14	9	13	15	68	43
11	36	32	10	9	14	23	44	29
11	19	21	12	8	10	20	56	36
11	21	20	12	9	12	16	53	30
11	31	34	11	7	15	14	70	42
11	33	32	10	7	13	17	78	47
11	36	34	12	6	13	11	71	44
11	33	32	16	9	13	13	72	45
11	37	33	12	10	12	17	68	44
11	34	33	14	11	12	15	67	43
11	35	37	16	12	9	21	75	43
11	31	32	14	8	9	18	62	40
11	37	34	13	11	15	15	67	41
11	35	30	4	3	10	8	83	52
11	27	30	15	11	14	12	64	38
11	34	38	11	12	15	12	68	41
11	40	36	11	7	7	22	62	39
11	29	32	14	9	14	12	72	43

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.29369158975446 -0.258721000939615month[t] -0.0175722821001457Connected[t] + 0.0386462716306383Separate[t] + 0.55286411850954Learning[t] -0.0164329402584209Happiness[t] -0.00235835272326144Depression[t] + 0.0214237034462335Belonging[t] -0.0228550090500412Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.29369158975446 -0.258721000939615month[t] -0.0175722821001457Connected[t] +  0.0386462716306383Separate[t] +  0.55286411850954Learning[t] -0.0164329402584209Happiness[t] -0.00235835272326144Depression[t] +  0.0214237034462335Belonging[t] -0.0228550090500412Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.29369158975446 -0.258721000939615month[t] -0.0175722821001457Connected[t] +  0.0386462716306383Separate[t] +  0.55286411850954Learning[t] -0.0164329402584209Happiness[t] -0.00235835272326144Depression[t] +  0.0214237034462335Belonging[t] -0.0228550090500412Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.29369158975446 -0.258721000939615month[t] -0.0175722821001457Connected[t] + 0.0386462716306383Separate[t] + 0.55286411850954Learning[t] -0.0164329402584209Happiness[t] -0.00235835272326144Depression[t] + 0.0214237034462335Belonging[t] -0.0228550090500412Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.293691589754462.6005651.65110.0999570.049979
month-0.2587210009396150.157399-1.64370.1014650.050733
Connected-0.01757228210014570.034098-0.51530.6067590.30338
Separate0.03864627163063830.034581.11760.2647930.132397
Learning0.552864118509540.05064610.916300
Happiness-0.01643294025842090.056869-0.2890.7728460.386423
Depression-0.002358352723261440.041604-0.05670.954840.47742
Belonging0.02142370344623350.0370150.57880.5632420.281621
Belonging_Final-0.02285500905004120.054974-0.41570.6779510.338976

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 4.29369158975446 & 2.600565 & 1.6511 & 0.099957 & 0.049979 \tabularnewline
month & -0.258721000939615 & 0.157399 & -1.6437 & 0.101465 & 0.050733 \tabularnewline
Connected & -0.0175722821001457 & 0.034098 & -0.5153 & 0.606759 & 0.30338 \tabularnewline
Separate & 0.0386462716306383 & 0.03458 & 1.1176 & 0.264793 & 0.132397 \tabularnewline
Learning & 0.55286411850954 & 0.050646 & 10.9163 & 0 & 0 \tabularnewline
Happiness & -0.0164329402584209 & 0.056869 & -0.289 & 0.772846 & 0.386423 \tabularnewline
Depression & -0.00235835272326144 & 0.041604 & -0.0567 & 0.95484 & 0.47742 \tabularnewline
Belonging & 0.0214237034462335 & 0.037015 & 0.5788 & 0.563242 & 0.281621 \tabularnewline
Belonging_Final & -0.0228550090500412 & 0.054974 & -0.4157 & 0.677951 & 0.338976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]4.29369158975446[/C][C]2.600565[/C][C]1.6511[/C][C]0.099957[/C][C]0.049979[/C][/ROW]
[ROW][C]month[/C][C]-0.258721000939615[/C][C]0.157399[/C][C]-1.6437[/C][C]0.101465[/C][C]0.050733[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0175722821001457[/C][C]0.034098[/C][C]-0.5153[/C][C]0.606759[/C][C]0.30338[/C][/ROW]
[ROW][C]Separate[/C][C]0.0386462716306383[/C][C]0.03458[/C][C]1.1176[/C][C]0.264793[/C][C]0.132397[/C][/ROW]
[ROW][C]Learning[/C][C]0.55286411850954[/C][C]0.050646[/C][C]10.9163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0164329402584209[/C][C]0.056869[/C][C]-0.289[/C][C]0.772846[/C][C]0.386423[/C][/ROW]
[ROW][C]Depression[/C][C]-0.00235835272326144[/C][C]0.041604[/C][C]-0.0567[/C][C]0.95484[/C][C]0.47742[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0214237034462335[/C][C]0.037015[/C][C]0.5788[/C][C]0.563242[/C][C]0.281621[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0228550090500412[/C][C]0.054974[/C][C]-0.4157[/C][C]0.677951[/C][C]0.338976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.293691589754462.6005651.65110.0999570.049979
month-0.2587210009396150.157399-1.64370.1014650.050733
Connected-0.01757228210014570.034098-0.51530.6067590.30338
Separate0.03864627163063830.034581.11760.2647930.132397
Learning0.552864118509540.05064610.916300
Happiness-0.01643294025842090.056869-0.2890.7728460.386423
Depression-0.002358352723261440.041604-0.05670.954840.47742
Belonging0.02142370344623350.0370150.57880.5632420.281621
Belonging_Final-0.02285500905004120.054974-0.41570.6779510.338976






Multiple Linear Regression - Regression Statistics
Multiple R0.631531259041089
R-squared0.398831731146023
Adjusted R-squared0.379971550162369
F-TEST (value)21.1467605476154
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.826844008971
Sum Squared Residuals851.026553443877

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.631531259041089 \tabularnewline
R-squared & 0.398831731146023 \tabularnewline
Adjusted R-squared & 0.379971550162369 \tabularnewline
F-TEST (value) & 21.1467605476154 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 255 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.826844008971 \tabularnewline
Sum Squared Residuals & 851.026553443877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.631531259041089[/C][/ROW]
[ROW][C]R-squared[/C][C]0.398831731146023[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.379971550162369[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.1467605476154[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]255[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.826844008971[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]851.026553443877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.631531259041089
R-squared0.398831731146023
Adjusted R-squared0.379971550162369
F-TEST (value)21.1467605476154
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.826844008971
Sum Squared Residuals851.026553443877






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.04626547453231.95373452546774
21111.6532172876031-0.653217287603136
31513.53534664342851.46465335657146
4611.2615878217133-5.26158782171334
51310.80217329364882.19782670635122
6109.930918411828920.0690815881710791
71212.9902122758925-0.990212275892494
81411.38997033491192.61002966508806
91210.71211860855911.28788139144092
10911.353411590483-2.35341159048299
111011.6109497953695-1.61094979536947
121211.56397155803620.436028441963818
131211.77167432208020.228325677919848
141111.700988502694-0.700988502693992
151512.54708960527492.45291039472514
161211.12789466788390.872105332116066
171011.1774671449883-1.17746714498831
181214.1691101038647-2.16911010386473
191112.880412993185-1.88041299318496
201211.5901812835230.409818716476979
211111.6827541949341-0.682754194934128
221211.70173430806960.298265691930389
231313.397163444186-0.397163444185976
241111.7919384702509-0.791938470250908
251212.2639304476598-0.263930447659756
261312.35069893554620.649301064453841
271011.887612977589-1.88761297758902
281411.31536978509132.68463021490868
291211.86953255150920.130467448490794
301010.6015164172884-0.601516417288399
311211.22130664798890.77869335201113
3289.51546225212575-1.51546225212575
331010.4885342671809-0.488534267180919
341211.93646755332840.0635324466715714
351210.63195472384491.36804527615506
3678.44052638355967-1.44052638355967
3798.491276471509060.508723528490942
381210.61154826761271.38845173238734
391011.7733942510822-1.77339425108221
401011.7524389748528-1.75243897485282
411011.6866941013094-1.68669410130936
421210.68838299084291.3116170091571
431513.7122897453881.28771025461197
441010.5409124081636-0.540912408163603
451010.36553830014-0.365538300140013
46129.080343498367022.91965650163298
471310.68681417068862.31318582931142
481111.2752636434113-0.275263643411348
491111.957693043487-0.957693043487047
501210.61296646070671.38703353929331
511411.96764945076982.03235054923019
521010.5604850292984-0.560485029298416
53129.593431230418662.40656876958134
541311.72885618414771.27114381585226
5557.91912314111385-2.91912314111385
56610.6810711279966-4.68107112799664
571211.70297572921890.297024270781123
581211.82316400439830.176835995601711
591111.3596418520282-0.359641852028222
601011.7942951175218-1.79429511752177
6179.37996456258237-2.37996456258237
621211.83647629515620.163523704843753
631411.97398365889292.02601634110711
641110.65562819930860.344371800691379
651211.53711730476190.462882695238065
661312.32378332155560.676216678444417
671412.61125277320871.38874722679127
681112.5756960492961-1.57569604929606
69129.405433149725072.59456685027493
701211.60600376902590.393996230974126
7188.12768980802669-0.127689808026694
721110.47475916881320.525240831186798
731412.92890957118421.07109042881581
741412.86874367526021.1312563247398
751211.41556975279940.584430247200564
76911.9312375894376-2.9312375894376
771311.53892037142821.4610796285718
781111.3717792255892-0.371779225589165
79129.898012693457372.10198730654263
801211.11339366240920.886606337590783
811211.73239411802750.267605881972518
821211.59207791238450.407922087615475
831210.91453356971651.08546643028351
841111.01319547961-0.0131954796099983
851011.4851765787437-1.48517657874368
86910.5871867752182-1.58718677521817
871211.54635916403920.453640835960805
881211.4330531610240.566946838975995
891211.04486452287150.955135477128459
9099.38995740154885-0.38995740154885
911511.86291130483253.13708869516747
921211.84291679964140.157083200358607
931210.9134927301771.08650726982302
94129.940110306943762.05988969305624
951011.6090961795644-1.60909617956438
961311.5152937678431.48470623215699
97911.4990274324862-2.49902743248619
981211.54908395464120.450916045358835
991010.5823254054931-0.58232540549311
1001411.57260907844452.42739092155549
1011111.5552785635279-0.555278563527856
1021513.90240089079131.09759910920869
1031110.76657576100580.233424238994205
1041111.813686160253-0.813686160253049
105129.754555828381362.24544417161864
1061212.0551914947585-0.0551914947584923
1071211.59313831208220.406861687917821
1081111.4752848219902-0.475284821990163
10979.28729649973635-2.28729649973635
1101211.6921555292740.307844470725987
1111411.64648971834542.35351028165459
1121112.5160460068729-1.51604600687293
113119.903988234528341.09601176547166
114109.23457267944020.765427320559802
1151312.58915441336160.410845586638388
1161310.55213138111432.44786861888571
117810.564020496988-2.56402049698799
118119.9334881383111.066511861689
1191211.68149789335030.318502106649673
120119.913835975506421.08616402449358
1211311.3648090093021.63519099069804
1221210.13327612895211.86672387104794
1231411.65934647166922.34065352833078
1241310.8278945834372.17210541656305
1251511.66161073880273.33838926119734
1261010.9236860545057-0.923686054505656
1271112.1159859214027-1.11598592140267
128910.9966605820264-1.99666058202644
129119.260881653427041.73911834657296
1301011.5869238743829-1.58692387438285
131118.28448010748882.7155198925112
132811.6075453607879-3.6075453607879
133119.22225850196631.7777414980337
1341210.52419242591471.47580757408533
1351210.87162753910251.12837246089747
13699.4195694300199-0.4195694300199
1371110.88131010893620.118689891063776
138108.751896610961581.24810338903843
13989.2242750391345-1.2242750391345
14098.895117356630890.104882643369111
141811.4365547410843-3.43655474108431
142911.2402285910018-2.24022859100178
1431512.30338425951532.69661574048467
1441111.2199313914137-0.219931391413748
14588.09108108686958-0.0910810868695771
1461312.40196078010050.598039219899461
147129.857889678707482.14211032129252
1481211.52033904896480.479660951035235
14999.83526071086305-0.835260710863052
15078.39305267570686-1.39305267570686
1511311.05588648737451.9441135126255
152911.7232835620571-2.72328356205705
153611.7558729307476-5.75587293074755
154810.6542917406382-2.65429174063824
15588.10263671157463-0.102636711574625
1561511.86291130483253.13708869516747
15769.99370619100607-3.99370619100607
158910.9966605820264-1.99666058202644
1591111.5906628073491-0.59066280734911
16089.66429130209418-1.66429130209418
16189.86306656283692-1.86306656283692
162109.375614736092530.624385263907467
16388.94024877087468-0.94024877087468
1641411.75792996957552.24207003042447
1651011.0034308666259-1.00343086662586
16687.881715374997350.118284625002645
1671110.25166377746620.748336222533846
168128.388064774778563.61193522522144
169129.934457974793132.06554202520687
1701211.68557507837990.314424921620121
17158.80329396891115-3.80329396891115
1721211.33655534341280.663444656587167
173109.134952690948890.865047309051106
17477.09045122278771-0.0904512227877074
175128.774924071975673.22507592802433
1761110.9616039096650.0383960903349516
17788.99569886693298-0.995698866932984
17899.01588422206662-0.0158842220666194
1791010.0529741024761-0.052974102476132
18099.25595972601251-0.255959726012514
1811211.19891174254050.801088257459452
18268.33710352041482-2.33710352041482
1831513.02030672390081.97969327609924
1841210.67286747988291.32713252011712
185126.926994111821595.07300588817841
1861211.48668336568010.513316634319855
1871111.8673322380433-0.867332238043285
18878.995452812842-1.995452812842
18978.34392214298767-1.34392214298767
19058.54343144175947-3.54343144175947
1911210.3680856932121.63191430678804
1921211.51920329040650.480796709593478
19339.03159970110272-6.03159970110272
1941111.2811869085998-0.281186908599826
195109.56673699294350.433263007056496
1961210.8276495685751.172350431425
197911.2147255772068-2.21472557720678
1981211.53072348230350.46927651769654
199910.1896619480641-1.18966194806413
2001211.47310579154430.526894208455732
2011211.21043284245320.789567157546822
2021010.2147524563009-0.21475245630092
20398.471408226185220.528591773814779
204128.831662610168683.16833738983132
205810.7621155583435-2.76211555834354
2061110.81425650285050.185743497149468
2071111.4140965512804-0.414096551280404
2081211.17704734443840.822952655561579
209108.419384372778851.58061562722115
2101010.7842050896356-0.784205089635588
211129.028927573875352.97107242612465
212129.361509072916112.63849092708389
2131111.0920929427108-0.0920929427107802
214810.8278237270517-2.82782372705167
2151211.33150953889650.668490461103503
216109.840323794188910.159676205811091
2171111.9464372498271-0.94643724982708
2181010.1478579912265-0.147857991226483
21989.65572977394945-1.65572977394945
2201210.90409696503851.09590303496153
221129.663099680438762.33690031956124
2221010.1542195399661-0.154219539966091
2231210.72998259098481.27001740901524
22499.04323692015042-0.0432369201504175
22599.79407606923288-0.79407606923288
22667.0328341601303-1.0328341601303
227109.944295128207540.0557048717924601
228910.2690736583203-1.26907365832027
22998.475483668077050.524516331922954
23099.14276484104011-0.142764841040107
23169.47688083679069-3.47688083679069
232108.10921240397141.8907875960286
233611.1973218795028-5.19732187950277
2341412.19163412246751.8083658775325
235109.52695125210110.473048747898901
236108.397812719084461.60218728091554
23764.416594494685811.58340550531419
238129.118524363363152.88147563663685
2391211.35149469165390.648505308346149
24077.81343915013309-0.813439150133087
24189.29755069029237-1.29755069029237
242119.001833386240061.99816661375994
24337.77419402782476-4.77419402782476
24469.56012634912521-3.56012634912521
2451010.5544995008432-0.554499500843181
24689.21554908808181-1.21554908808181
247910.2032076494036-1.20320764940365
24897.576024714019451.42397528598055
24988.72527895601197-0.725278956011966
25098.700914594518790.299085405481213
25178.55873619249346-1.55873619249346
25277.97634037118907-0.976340371189074
25369.03939352453505-3.03939352453505
254911.220126290562-2.22012629056204
255108.921186684384421.07881331561558
2561110.08577977875430.914220221245735
2571211.53505915220130.464940847798686
258810.1035206259485-2.1035206259485
259119.515256282899751.48474371710025
26034.61008601993585-1.61008601993585
2611110.66992416963740.330075830362642
262128.645328740319683.35467125968032
26378.48765029671496-1.48765029671496
264910.2163226125085-1.21632261250847

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 10.0462654745323 & 1.95373452546774 \tabularnewline
2 & 11 & 11.6532172876031 & -0.653217287603136 \tabularnewline
3 & 15 & 13.5353466434285 & 1.46465335657146 \tabularnewline
4 & 6 & 11.2615878217133 & -5.26158782171334 \tabularnewline
5 & 13 & 10.8021732936488 & 2.19782670635122 \tabularnewline
6 & 10 & 9.93091841182892 & 0.0690815881710791 \tabularnewline
7 & 12 & 12.9902122758925 & -0.990212275892494 \tabularnewline
8 & 14 & 11.3899703349119 & 2.61002966508806 \tabularnewline
9 & 12 & 10.7121186085591 & 1.28788139144092 \tabularnewline
10 & 9 & 11.353411590483 & -2.35341159048299 \tabularnewline
11 & 10 & 11.6109497953695 & -1.61094979536947 \tabularnewline
12 & 12 & 11.5639715580362 & 0.436028441963818 \tabularnewline
13 & 12 & 11.7716743220802 & 0.228325677919848 \tabularnewline
14 & 11 & 11.700988502694 & -0.700988502693992 \tabularnewline
15 & 15 & 12.5470896052749 & 2.45291039472514 \tabularnewline
16 & 12 & 11.1278946678839 & 0.872105332116066 \tabularnewline
17 & 10 & 11.1774671449883 & -1.17746714498831 \tabularnewline
18 & 12 & 14.1691101038647 & -2.16911010386473 \tabularnewline
19 & 11 & 12.880412993185 & -1.88041299318496 \tabularnewline
20 & 12 & 11.590181283523 & 0.409818716476979 \tabularnewline
21 & 11 & 11.6827541949341 & -0.682754194934128 \tabularnewline
22 & 12 & 11.7017343080696 & 0.298265691930389 \tabularnewline
23 & 13 & 13.397163444186 & -0.397163444185976 \tabularnewline
24 & 11 & 11.7919384702509 & -0.791938470250908 \tabularnewline
25 & 12 & 12.2639304476598 & -0.263930447659756 \tabularnewline
26 & 13 & 12.3506989355462 & 0.649301064453841 \tabularnewline
27 & 10 & 11.887612977589 & -1.88761297758902 \tabularnewline
28 & 14 & 11.3153697850913 & 2.68463021490868 \tabularnewline
29 & 12 & 11.8695325515092 & 0.130467448490794 \tabularnewline
30 & 10 & 10.6015164172884 & -0.601516417288399 \tabularnewline
31 & 12 & 11.2213066479889 & 0.77869335201113 \tabularnewline
32 & 8 & 9.51546225212575 & -1.51546225212575 \tabularnewline
33 & 10 & 10.4885342671809 & -0.488534267180919 \tabularnewline
34 & 12 & 11.9364675533284 & 0.0635324466715714 \tabularnewline
35 & 12 & 10.6319547238449 & 1.36804527615506 \tabularnewline
36 & 7 & 8.44052638355967 & -1.44052638355967 \tabularnewline
37 & 9 & 8.49127647150906 & 0.508723528490942 \tabularnewline
38 & 12 & 10.6115482676127 & 1.38845173238734 \tabularnewline
39 & 10 & 11.7733942510822 & -1.77339425108221 \tabularnewline
40 & 10 & 11.7524389748528 & -1.75243897485282 \tabularnewline
41 & 10 & 11.6866941013094 & -1.68669410130936 \tabularnewline
42 & 12 & 10.6883829908429 & 1.3116170091571 \tabularnewline
43 & 15 & 13.712289745388 & 1.28771025461197 \tabularnewline
44 & 10 & 10.5409124081636 & -0.540912408163603 \tabularnewline
45 & 10 & 10.36553830014 & -0.365538300140013 \tabularnewline
46 & 12 & 9.08034349836702 & 2.91965650163298 \tabularnewline
47 & 13 & 10.6868141706886 & 2.31318582931142 \tabularnewline
48 & 11 & 11.2752636434113 & -0.275263643411348 \tabularnewline
49 & 11 & 11.957693043487 & -0.957693043487047 \tabularnewline
50 & 12 & 10.6129664607067 & 1.38703353929331 \tabularnewline
51 & 14 & 11.9676494507698 & 2.03235054923019 \tabularnewline
52 & 10 & 10.5604850292984 & -0.560485029298416 \tabularnewline
53 & 12 & 9.59343123041866 & 2.40656876958134 \tabularnewline
54 & 13 & 11.7288561841477 & 1.27114381585226 \tabularnewline
55 & 5 & 7.91912314111385 & -2.91912314111385 \tabularnewline
56 & 6 & 10.6810711279966 & -4.68107112799664 \tabularnewline
57 & 12 & 11.7029757292189 & 0.297024270781123 \tabularnewline
58 & 12 & 11.8231640043983 & 0.176835995601711 \tabularnewline
59 & 11 & 11.3596418520282 & -0.359641852028222 \tabularnewline
60 & 10 & 11.7942951175218 & -1.79429511752177 \tabularnewline
61 & 7 & 9.37996456258237 & -2.37996456258237 \tabularnewline
62 & 12 & 11.8364762951562 & 0.163523704843753 \tabularnewline
63 & 14 & 11.9739836588929 & 2.02601634110711 \tabularnewline
64 & 11 & 10.6556281993086 & 0.344371800691379 \tabularnewline
65 & 12 & 11.5371173047619 & 0.462882695238065 \tabularnewline
66 & 13 & 12.3237833215556 & 0.676216678444417 \tabularnewline
67 & 14 & 12.6112527732087 & 1.38874722679127 \tabularnewline
68 & 11 & 12.5756960492961 & -1.57569604929606 \tabularnewline
69 & 12 & 9.40543314972507 & 2.59456685027493 \tabularnewline
70 & 12 & 11.6060037690259 & 0.393996230974126 \tabularnewline
71 & 8 & 8.12768980802669 & -0.127689808026694 \tabularnewline
72 & 11 & 10.4747591688132 & 0.525240831186798 \tabularnewline
73 & 14 & 12.9289095711842 & 1.07109042881581 \tabularnewline
74 & 14 & 12.8687436752602 & 1.1312563247398 \tabularnewline
75 & 12 & 11.4155697527994 & 0.584430247200564 \tabularnewline
76 & 9 & 11.9312375894376 & -2.9312375894376 \tabularnewline
77 & 13 & 11.5389203714282 & 1.4610796285718 \tabularnewline
78 & 11 & 11.3717792255892 & -0.371779225589165 \tabularnewline
79 & 12 & 9.89801269345737 & 2.10198730654263 \tabularnewline
80 & 12 & 11.1133936624092 & 0.886606337590783 \tabularnewline
81 & 12 & 11.7323941180275 & 0.267605881972518 \tabularnewline
82 & 12 & 11.5920779123845 & 0.407922087615475 \tabularnewline
83 & 12 & 10.9145335697165 & 1.08546643028351 \tabularnewline
84 & 11 & 11.01319547961 & -0.0131954796099983 \tabularnewline
85 & 10 & 11.4851765787437 & -1.48517657874368 \tabularnewline
86 & 9 & 10.5871867752182 & -1.58718677521817 \tabularnewline
87 & 12 & 11.5463591640392 & 0.453640835960805 \tabularnewline
88 & 12 & 11.433053161024 & 0.566946838975995 \tabularnewline
89 & 12 & 11.0448645228715 & 0.955135477128459 \tabularnewline
90 & 9 & 9.38995740154885 & -0.38995740154885 \tabularnewline
91 & 15 & 11.8629113048325 & 3.13708869516747 \tabularnewline
92 & 12 & 11.8429167996414 & 0.157083200358607 \tabularnewline
93 & 12 & 10.913492730177 & 1.08650726982302 \tabularnewline
94 & 12 & 9.94011030694376 & 2.05988969305624 \tabularnewline
95 & 10 & 11.6090961795644 & -1.60909617956438 \tabularnewline
96 & 13 & 11.515293767843 & 1.48470623215699 \tabularnewline
97 & 9 & 11.4990274324862 & -2.49902743248619 \tabularnewline
98 & 12 & 11.5490839546412 & 0.450916045358835 \tabularnewline
99 & 10 & 10.5823254054931 & -0.58232540549311 \tabularnewline
100 & 14 & 11.5726090784445 & 2.42739092155549 \tabularnewline
101 & 11 & 11.5552785635279 & -0.555278563527856 \tabularnewline
102 & 15 & 13.9024008907913 & 1.09759910920869 \tabularnewline
103 & 11 & 10.7665757610058 & 0.233424238994205 \tabularnewline
104 & 11 & 11.813686160253 & -0.813686160253049 \tabularnewline
105 & 12 & 9.75455582838136 & 2.24544417161864 \tabularnewline
106 & 12 & 12.0551914947585 & -0.0551914947584923 \tabularnewline
107 & 12 & 11.5931383120822 & 0.406861687917821 \tabularnewline
108 & 11 & 11.4752848219902 & -0.475284821990163 \tabularnewline
109 & 7 & 9.28729649973635 & -2.28729649973635 \tabularnewline
110 & 12 & 11.692155529274 & 0.307844470725987 \tabularnewline
111 & 14 & 11.6464897183454 & 2.35351028165459 \tabularnewline
112 & 11 & 12.5160460068729 & -1.51604600687293 \tabularnewline
113 & 11 & 9.90398823452834 & 1.09601176547166 \tabularnewline
114 & 10 & 9.2345726794402 & 0.765427320559802 \tabularnewline
115 & 13 & 12.5891544133616 & 0.410845586638388 \tabularnewline
116 & 13 & 10.5521313811143 & 2.44786861888571 \tabularnewline
117 & 8 & 10.564020496988 & -2.56402049698799 \tabularnewline
118 & 11 & 9.933488138311 & 1.066511861689 \tabularnewline
119 & 12 & 11.6814978933503 & 0.318502106649673 \tabularnewline
120 & 11 & 9.91383597550642 & 1.08616402449358 \tabularnewline
121 & 13 & 11.364809009302 & 1.63519099069804 \tabularnewline
122 & 12 & 10.1332761289521 & 1.86672387104794 \tabularnewline
123 & 14 & 11.6593464716692 & 2.34065352833078 \tabularnewline
124 & 13 & 10.827894583437 & 2.17210541656305 \tabularnewline
125 & 15 & 11.6616107388027 & 3.33838926119734 \tabularnewline
126 & 10 & 10.9236860545057 & -0.923686054505656 \tabularnewline
127 & 11 & 12.1159859214027 & -1.11598592140267 \tabularnewline
128 & 9 & 10.9966605820264 & -1.99666058202644 \tabularnewline
129 & 11 & 9.26088165342704 & 1.73911834657296 \tabularnewline
130 & 10 & 11.5869238743829 & -1.58692387438285 \tabularnewline
131 & 11 & 8.2844801074888 & 2.7155198925112 \tabularnewline
132 & 8 & 11.6075453607879 & -3.6075453607879 \tabularnewline
133 & 11 & 9.2222585019663 & 1.7777414980337 \tabularnewline
134 & 12 & 10.5241924259147 & 1.47580757408533 \tabularnewline
135 & 12 & 10.8716275391025 & 1.12837246089747 \tabularnewline
136 & 9 & 9.4195694300199 & -0.4195694300199 \tabularnewline
137 & 11 & 10.8813101089362 & 0.118689891063776 \tabularnewline
138 & 10 & 8.75189661096158 & 1.24810338903843 \tabularnewline
139 & 8 & 9.2242750391345 & -1.2242750391345 \tabularnewline
140 & 9 & 8.89511735663089 & 0.104882643369111 \tabularnewline
141 & 8 & 11.4365547410843 & -3.43655474108431 \tabularnewline
142 & 9 & 11.2402285910018 & -2.24022859100178 \tabularnewline
143 & 15 & 12.3033842595153 & 2.69661574048467 \tabularnewline
144 & 11 & 11.2199313914137 & -0.219931391413748 \tabularnewline
145 & 8 & 8.09108108686958 & -0.0910810868695771 \tabularnewline
146 & 13 & 12.4019607801005 & 0.598039219899461 \tabularnewline
147 & 12 & 9.85788967870748 & 2.14211032129252 \tabularnewline
148 & 12 & 11.5203390489648 & 0.479660951035235 \tabularnewline
149 & 9 & 9.83526071086305 & -0.835260710863052 \tabularnewline
150 & 7 & 8.39305267570686 & -1.39305267570686 \tabularnewline
151 & 13 & 11.0558864873745 & 1.9441135126255 \tabularnewline
152 & 9 & 11.7232835620571 & -2.72328356205705 \tabularnewline
153 & 6 & 11.7558729307476 & -5.75587293074755 \tabularnewline
154 & 8 & 10.6542917406382 & -2.65429174063824 \tabularnewline
155 & 8 & 8.10263671157463 & -0.102636711574625 \tabularnewline
156 & 15 & 11.8629113048325 & 3.13708869516747 \tabularnewline
157 & 6 & 9.99370619100607 & -3.99370619100607 \tabularnewline
158 & 9 & 10.9966605820264 & -1.99666058202644 \tabularnewline
159 & 11 & 11.5906628073491 & -0.59066280734911 \tabularnewline
160 & 8 & 9.66429130209418 & -1.66429130209418 \tabularnewline
161 & 8 & 9.86306656283692 & -1.86306656283692 \tabularnewline
162 & 10 & 9.37561473609253 & 0.624385263907467 \tabularnewline
163 & 8 & 8.94024877087468 & -0.94024877087468 \tabularnewline
164 & 14 & 11.7579299695755 & 2.24207003042447 \tabularnewline
165 & 10 & 11.0034308666259 & -1.00343086662586 \tabularnewline
166 & 8 & 7.88171537499735 & 0.118284625002645 \tabularnewline
167 & 11 & 10.2516637774662 & 0.748336222533846 \tabularnewline
168 & 12 & 8.38806477477856 & 3.61193522522144 \tabularnewline
169 & 12 & 9.93445797479313 & 2.06554202520687 \tabularnewline
170 & 12 & 11.6855750783799 & 0.314424921620121 \tabularnewline
171 & 5 & 8.80329396891115 & -3.80329396891115 \tabularnewline
172 & 12 & 11.3365553434128 & 0.663444656587167 \tabularnewline
173 & 10 & 9.13495269094889 & 0.865047309051106 \tabularnewline
174 & 7 & 7.09045122278771 & -0.0904512227877074 \tabularnewline
175 & 12 & 8.77492407197567 & 3.22507592802433 \tabularnewline
176 & 11 & 10.961603909665 & 0.0383960903349516 \tabularnewline
177 & 8 & 8.99569886693298 & -0.995698866932984 \tabularnewline
178 & 9 & 9.01588422206662 & -0.0158842220666194 \tabularnewline
179 & 10 & 10.0529741024761 & -0.052974102476132 \tabularnewline
180 & 9 & 9.25595972601251 & -0.255959726012514 \tabularnewline
181 & 12 & 11.1989117425405 & 0.801088257459452 \tabularnewline
182 & 6 & 8.33710352041482 & -2.33710352041482 \tabularnewline
183 & 15 & 13.0203067239008 & 1.97969327609924 \tabularnewline
184 & 12 & 10.6728674798829 & 1.32713252011712 \tabularnewline
185 & 12 & 6.92699411182159 & 5.07300588817841 \tabularnewline
186 & 12 & 11.4866833656801 & 0.513316634319855 \tabularnewline
187 & 11 & 11.8673322380433 & -0.867332238043285 \tabularnewline
188 & 7 & 8.995452812842 & -1.995452812842 \tabularnewline
189 & 7 & 8.34392214298767 & -1.34392214298767 \tabularnewline
190 & 5 & 8.54343144175947 & -3.54343144175947 \tabularnewline
191 & 12 & 10.368085693212 & 1.63191430678804 \tabularnewline
192 & 12 & 11.5192032904065 & 0.480796709593478 \tabularnewline
193 & 3 & 9.03159970110272 & -6.03159970110272 \tabularnewline
194 & 11 & 11.2811869085998 & -0.281186908599826 \tabularnewline
195 & 10 & 9.5667369929435 & 0.433263007056496 \tabularnewline
196 & 12 & 10.827649568575 & 1.172350431425 \tabularnewline
197 & 9 & 11.2147255772068 & -2.21472557720678 \tabularnewline
198 & 12 & 11.5307234823035 & 0.46927651769654 \tabularnewline
199 & 9 & 10.1896619480641 & -1.18966194806413 \tabularnewline
200 & 12 & 11.4731057915443 & 0.526894208455732 \tabularnewline
201 & 12 & 11.2104328424532 & 0.789567157546822 \tabularnewline
202 & 10 & 10.2147524563009 & -0.21475245630092 \tabularnewline
203 & 9 & 8.47140822618522 & 0.528591773814779 \tabularnewline
204 & 12 & 8.83166261016868 & 3.16833738983132 \tabularnewline
205 & 8 & 10.7621155583435 & -2.76211555834354 \tabularnewline
206 & 11 & 10.8142565028505 & 0.185743497149468 \tabularnewline
207 & 11 & 11.4140965512804 & -0.414096551280404 \tabularnewline
208 & 12 & 11.1770473444384 & 0.822952655561579 \tabularnewline
209 & 10 & 8.41938437277885 & 1.58061562722115 \tabularnewline
210 & 10 & 10.7842050896356 & -0.784205089635588 \tabularnewline
211 & 12 & 9.02892757387535 & 2.97107242612465 \tabularnewline
212 & 12 & 9.36150907291611 & 2.63849092708389 \tabularnewline
213 & 11 & 11.0920929427108 & -0.0920929427107802 \tabularnewline
214 & 8 & 10.8278237270517 & -2.82782372705167 \tabularnewline
215 & 12 & 11.3315095388965 & 0.668490461103503 \tabularnewline
216 & 10 & 9.84032379418891 & 0.159676205811091 \tabularnewline
217 & 11 & 11.9464372498271 & -0.94643724982708 \tabularnewline
218 & 10 & 10.1478579912265 & -0.147857991226483 \tabularnewline
219 & 8 & 9.65572977394945 & -1.65572977394945 \tabularnewline
220 & 12 & 10.9040969650385 & 1.09590303496153 \tabularnewline
221 & 12 & 9.66309968043876 & 2.33690031956124 \tabularnewline
222 & 10 & 10.1542195399661 & -0.154219539966091 \tabularnewline
223 & 12 & 10.7299825909848 & 1.27001740901524 \tabularnewline
224 & 9 & 9.04323692015042 & -0.0432369201504175 \tabularnewline
225 & 9 & 9.79407606923288 & -0.79407606923288 \tabularnewline
226 & 6 & 7.0328341601303 & -1.0328341601303 \tabularnewline
227 & 10 & 9.94429512820754 & 0.0557048717924601 \tabularnewline
228 & 9 & 10.2690736583203 & -1.26907365832027 \tabularnewline
229 & 9 & 8.47548366807705 & 0.524516331922954 \tabularnewline
230 & 9 & 9.14276484104011 & -0.142764841040107 \tabularnewline
231 & 6 & 9.47688083679069 & -3.47688083679069 \tabularnewline
232 & 10 & 8.1092124039714 & 1.8907875960286 \tabularnewline
233 & 6 & 11.1973218795028 & -5.19732187950277 \tabularnewline
234 & 14 & 12.1916341224675 & 1.8083658775325 \tabularnewline
235 & 10 & 9.5269512521011 & 0.473048747898901 \tabularnewline
236 & 10 & 8.39781271908446 & 1.60218728091554 \tabularnewline
237 & 6 & 4.41659449468581 & 1.58340550531419 \tabularnewline
238 & 12 & 9.11852436336315 & 2.88147563663685 \tabularnewline
239 & 12 & 11.3514946916539 & 0.648505308346149 \tabularnewline
240 & 7 & 7.81343915013309 & -0.813439150133087 \tabularnewline
241 & 8 & 9.29755069029237 & -1.29755069029237 \tabularnewline
242 & 11 & 9.00183338624006 & 1.99816661375994 \tabularnewline
243 & 3 & 7.77419402782476 & -4.77419402782476 \tabularnewline
244 & 6 & 9.56012634912521 & -3.56012634912521 \tabularnewline
245 & 10 & 10.5544995008432 & -0.554499500843181 \tabularnewline
246 & 8 & 9.21554908808181 & -1.21554908808181 \tabularnewline
247 & 9 & 10.2032076494036 & -1.20320764940365 \tabularnewline
248 & 9 & 7.57602471401945 & 1.42397528598055 \tabularnewline
249 & 8 & 8.72527895601197 & -0.725278956011966 \tabularnewline
250 & 9 & 8.70091459451879 & 0.299085405481213 \tabularnewline
251 & 7 & 8.55873619249346 & -1.55873619249346 \tabularnewline
252 & 7 & 7.97634037118907 & -0.976340371189074 \tabularnewline
253 & 6 & 9.03939352453505 & -3.03939352453505 \tabularnewline
254 & 9 & 11.220126290562 & -2.22012629056204 \tabularnewline
255 & 10 & 8.92118668438442 & 1.07881331561558 \tabularnewline
256 & 11 & 10.0857797787543 & 0.914220221245735 \tabularnewline
257 & 12 & 11.5350591522013 & 0.464940847798686 \tabularnewline
258 & 8 & 10.1035206259485 & -2.1035206259485 \tabularnewline
259 & 11 & 9.51525628289975 & 1.48474371710025 \tabularnewline
260 & 3 & 4.61008601993585 & -1.61008601993585 \tabularnewline
261 & 11 & 10.6699241696374 & 0.330075830362642 \tabularnewline
262 & 12 & 8.64532874031968 & 3.35467125968032 \tabularnewline
263 & 7 & 8.48765029671496 & -1.48765029671496 \tabularnewline
264 & 9 & 10.2163226125085 & -1.21632261250847 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]10.0462654745323[/C][C]1.95373452546774[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.6532172876031[/C][C]-0.653217287603136[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5353466434285[/C][C]1.46465335657146[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.2615878217133[/C][C]-5.26158782171334[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.8021732936488[/C][C]2.19782670635122[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.93091841182892[/C][C]0.0690815881710791[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.9902122758925[/C][C]-0.990212275892494[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.3899703349119[/C][C]2.61002966508806[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.7121186085591[/C][C]1.28788139144092[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]11.353411590483[/C][C]-2.35341159048299[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.6109497953695[/C][C]-1.61094979536947[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.5639715580362[/C][C]0.436028441963818[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.7716743220802[/C][C]0.228325677919848[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.700988502694[/C][C]-0.700988502693992[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.5470896052749[/C][C]2.45291039472514[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.1278946678839[/C][C]0.872105332116066[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.1774671449883[/C][C]-1.17746714498831[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]14.1691101038647[/C][C]-2.16911010386473[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.880412993185[/C][C]-1.88041299318496[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.590181283523[/C][C]0.409818716476979[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6827541949341[/C][C]-0.682754194934128[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.7017343080696[/C][C]0.298265691930389[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.397163444186[/C][C]-0.397163444185976[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.7919384702509[/C][C]-0.791938470250908[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]12.2639304476598[/C][C]-0.263930447659756[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.3506989355462[/C][C]0.649301064453841[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.887612977589[/C][C]-1.88761297758902[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.3153697850913[/C][C]2.68463021490868[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.8695325515092[/C][C]0.130467448490794[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.6015164172884[/C][C]-0.601516417288399[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.2213066479889[/C][C]0.77869335201113[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.51546225212575[/C][C]-1.51546225212575[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.4885342671809[/C][C]-0.488534267180919[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.9364675533284[/C][C]0.0635324466715714[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.6319547238449[/C][C]1.36804527615506[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]8.44052638355967[/C][C]-1.44052638355967[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]8.49127647150906[/C][C]0.508723528490942[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.6115482676127[/C][C]1.38845173238734[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.7733942510822[/C][C]-1.77339425108221[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.7524389748528[/C][C]-1.75243897485282[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.6866941013094[/C][C]-1.68669410130936[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.6883829908429[/C][C]1.3116170091571[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.712289745388[/C][C]1.28771025461197[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5409124081636[/C][C]-0.540912408163603[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.36553830014[/C][C]-0.365538300140013[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.08034349836702[/C][C]2.91965650163298[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.6868141706886[/C][C]2.31318582931142[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.2752636434113[/C][C]-0.275263643411348[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.957693043487[/C][C]-0.957693043487047[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.6129664607067[/C][C]1.38703353929331[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.9676494507698[/C][C]2.03235054923019[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5604850292984[/C][C]-0.560485029298416[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.59343123041866[/C][C]2.40656876958134[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7288561841477[/C][C]1.27114381585226[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]7.91912314111385[/C][C]-2.91912314111385[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.6810711279966[/C][C]-4.68107112799664[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.7029757292189[/C][C]0.297024270781123[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.8231640043983[/C][C]0.176835995601711[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.3596418520282[/C][C]-0.359641852028222[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.7942951175218[/C][C]-1.79429511752177[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.37996456258237[/C][C]-2.37996456258237[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.8364762951562[/C][C]0.163523704843753[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.9739836588929[/C][C]2.02601634110711[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.6556281993086[/C][C]0.344371800691379[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.5371173047619[/C][C]0.462882695238065[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.3237833215556[/C][C]0.676216678444417[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.6112527732087[/C][C]1.38874722679127[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.5756960492961[/C][C]-1.57569604929606[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.40543314972507[/C][C]2.59456685027493[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6060037690259[/C][C]0.393996230974126[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.12768980802669[/C][C]-0.127689808026694[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.4747591688132[/C][C]0.525240831186798[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.9289095711842[/C][C]1.07109042881581[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.8687436752602[/C][C]1.1312563247398[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4155697527994[/C][C]0.584430247200564[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.9312375894376[/C][C]-2.9312375894376[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5389203714282[/C][C]1.4610796285718[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.3717792255892[/C][C]-0.371779225589165[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]9.89801269345737[/C][C]2.10198730654263[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.1133936624092[/C][C]0.886606337590783[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7323941180275[/C][C]0.267605881972518[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.5920779123845[/C][C]0.407922087615475[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]10.9145335697165[/C][C]1.08546643028351[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]11.01319547961[/C][C]-0.0131954796099983[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]11.4851765787437[/C][C]-1.48517657874368[/C][/ROW]
[ROW][C]86[/C][C]9[/C][C]10.5871867752182[/C][C]-1.58718677521817[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.5463591640392[/C][C]0.453640835960805[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.433053161024[/C][C]0.566946838975995[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.0448645228715[/C][C]0.955135477128459[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]9.38995740154885[/C][C]-0.38995740154885[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]11.8629113048325[/C][C]3.13708869516747[/C][/ROW]
[ROW][C]92[/C][C]12[/C][C]11.8429167996414[/C][C]0.157083200358607[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.913492730177[/C][C]1.08650726982302[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]9.94011030694376[/C][C]2.05988969305624[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]11.6090961795644[/C][C]-1.60909617956438[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]11.515293767843[/C][C]1.48470623215699[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]11.4990274324862[/C][C]-2.49902743248619[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]11.5490839546412[/C][C]0.450916045358835[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]10.5823254054931[/C][C]-0.58232540549311[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]11.5726090784445[/C][C]2.42739092155549[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]11.5552785635279[/C][C]-0.555278563527856[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]13.9024008907913[/C][C]1.09759910920869[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]10.7665757610058[/C][C]0.233424238994205[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.813686160253[/C][C]-0.813686160253049[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]9.75455582838136[/C][C]2.24544417161864[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]12.0551914947585[/C][C]-0.0551914947584923[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.5931383120822[/C][C]0.406861687917821[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]11.4752848219902[/C][C]-0.475284821990163[/C][/ROW]
[ROW][C]109[/C][C]7[/C][C]9.28729649973635[/C][C]-2.28729649973635[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]11.692155529274[/C][C]0.307844470725987[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.6464897183454[/C][C]2.35351028165459[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]12.5160460068729[/C][C]-1.51604600687293[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]9.90398823452834[/C][C]1.09601176547166[/C][/ROW]
[ROW][C]114[/C][C]10[/C][C]9.2345726794402[/C][C]0.765427320559802[/C][/ROW]
[ROW][C]115[/C][C]13[/C][C]12.5891544133616[/C][C]0.410845586638388[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]10.5521313811143[/C][C]2.44786861888571[/C][/ROW]
[ROW][C]117[/C][C]8[/C][C]10.564020496988[/C][C]-2.56402049698799[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]9.933488138311[/C][C]1.066511861689[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]11.6814978933503[/C][C]0.318502106649673[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]9.91383597550642[/C][C]1.08616402449358[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]11.364809009302[/C][C]1.63519099069804[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]10.1332761289521[/C][C]1.86672387104794[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]11.6593464716692[/C][C]2.34065352833078[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]10.827894583437[/C][C]2.17210541656305[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]11.6616107388027[/C][C]3.33838926119734[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]10.9236860545057[/C][C]-0.923686054505656[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]12.1159859214027[/C][C]-1.11598592140267[/C][/ROW]
[ROW][C]128[/C][C]9[/C][C]10.9966605820264[/C][C]-1.99666058202644[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.26088165342704[/C][C]1.73911834657296[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]11.5869238743829[/C][C]-1.58692387438285[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]8.2844801074888[/C][C]2.7155198925112[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]11.6075453607879[/C][C]-3.6075453607879[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]9.2222585019663[/C][C]1.7777414980337[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]10.5241924259147[/C][C]1.47580757408533[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.8716275391025[/C][C]1.12837246089747[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]9.4195694300199[/C][C]-0.4195694300199[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.8813101089362[/C][C]0.118689891063776[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]8.75189661096158[/C][C]1.24810338903843[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]9.2242750391345[/C][C]-1.2242750391345[/C][/ROW]
[ROW][C]140[/C][C]9[/C][C]8.89511735663089[/C][C]0.104882643369111[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]11.4365547410843[/C][C]-3.43655474108431[/C][/ROW]
[ROW][C]142[/C][C]9[/C][C]11.2402285910018[/C][C]-2.24022859100178[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.3033842595153[/C][C]2.69661574048467[/C][/ROW]
[ROW][C]144[/C][C]11[/C][C]11.2199313914137[/C][C]-0.219931391413748[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]8.09108108686958[/C][C]-0.0910810868695771[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]12.4019607801005[/C][C]0.598039219899461[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]9.85788967870748[/C][C]2.14211032129252[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]11.5203390489648[/C][C]0.479660951035235[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]9.83526071086305[/C][C]-0.835260710863052[/C][/ROW]
[ROW][C]150[/C][C]7[/C][C]8.39305267570686[/C][C]-1.39305267570686[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]11.0558864873745[/C][C]1.9441135126255[/C][/ROW]
[ROW][C]152[/C][C]9[/C][C]11.7232835620571[/C][C]-2.72328356205705[/C][/ROW]
[ROW][C]153[/C][C]6[/C][C]11.7558729307476[/C][C]-5.75587293074755[/C][/ROW]
[ROW][C]154[/C][C]8[/C][C]10.6542917406382[/C][C]-2.65429174063824[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]8.10263671157463[/C][C]-0.102636711574625[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]11.8629113048325[/C][C]3.13708869516747[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]9.99370619100607[/C][C]-3.99370619100607[/C][/ROW]
[ROW][C]158[/C][C]9[/C][C]10.9966605820264[/C][C]-1.99666058202644[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]11.5906628073491[/C][C]-0.59066280734911[/C][/ROW]
[ROW][C]160[/C][C]8[/C][C]9.66429130209418[/C][C]-1.66429130209418[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.86306656283692[/C][C]-1.86306656283692[/C][/ROW]
[ROW][C]162[/C][C]10[/C][C]9.37561473609253[/C][C]0.624385263907467[/C][/ROW]
[ROW][C]163[/C][C]8[/C][C]8.94024877087468[/C][C]-0.94024877087468[/C][/ROW]
[ROW][C]164[/C][C]14[/C][C]11.7579299695755[/C][C]2.24207003042447[/C][/ROW]
[ROW][C]165[/C][C]10[/C][C]11.0034308666259[/C][C]-1.00343086662586[/C][/ROW]
[ROW][C]166[/C][C]8[/C][C]7.88171537499735[/C][C]0.118284625002645[/C][/ROW]
[ROW][C]167[/C][C]11[/C][C]10.2516637774662[/C][C]0.748336222533846[/C][/ROW]
[ROW][C]168[/C][C]12[/C][C]8.38806477477856[/C][C]3.61193522522144[/C][/ROW]
[ROW][C]169[/C][C]12[/C][C]9.93445797479313[/C][C]2.06554202520687[/C][/ROW]
[ROW][C]170[/C][C]12[/C][C]11.6855750783799[/C][C]0.314424921620121[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]8.80329396891115[/C][C]-3.80329396891115[/C][/ROW]
[ROW][C]172[/C][C]12[/C][C]11.3365553434128[/C][C]0.663444656587167[/C][/ROW]
[ROW][C]173[/C][C]10[/C][C]9.13495269094889[/C][C]0.865047309051106[/C][/ROW]
[ROW][C]174[/C][C]7[/C][C]7.09045122278771[/C][C]-0.0904512227877074[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]8.77492407197567[/C][C]3.22507592802433[/C][/ROW]
[ROW][C]176[/C][C]11[/C][C]10.961603909665[/C][C]0.0383960903349516[/C][/ROW]
[ROW][C]177[/C][C]8[/C][C]8.99569886693298[/C][C]-0.995698866932984[/C][/ROW]
[ROW][C]178[/C][C]9[/C][C]9.01588422206662[/C][C]-0.0158842220666194[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]10.0529741024761[/C][C]-0.052974102476132[/C][/ROW]
[ROW][C]180[/C][C]9[/C][C]9.25595972601251[/C][C]-0.255959726012514[/C][/ROW]
[ROW][C]181[/C][C]12[/C][C]11.1989117425405[/C][C]0.801088257459452[/C][/ROW]
[ROW][C]182[/C][C]6[/C][C]8.33710352041482[/C][C]-2.33710352041482[/C][/ROW]
[ROW][C]183[/C][C]15[/C][C]13.0203067239008[/C][C]1.97969327609924[/C][/ROW]
[ROW][C]184[/C][C]12[/C][C]10.6728674798829[/C][C]1.32713252011712[/C][/ROW]
[ROW][C]185[/C][C]12[/C][C]6.92699411182159[/C][C]5.07300588817841[/C][/ROW]
[ROW][C]186[/C][C]12[/C][C]11.4866833656801[/C][C]0.513316634319855[/C][/ROW]
[ROW][C]187[/C][C]11[/C][C]11.8673322380433[/C][C]-0.867332238043285[/C][/ROW]
[ROW][C]188[/C][C]7[/C][C]8.995452812842[/C][C]-1.995452812842[/C][/ROW]
[ROW][C]189[/C][C]7[/C][C]8.34392214298767[/C][C]-1.34392214298767[/C][/ROW]
[ROW][C]190[/C][C]5[/C][C]8.54343144175947[/C][C]-3.54343144175947[/C][/ROW]
[ROW][C]191[/C][C]12[/C][C]10.368085693212[/C][C]1.63191430678804[/C][/ROW]
[ROW][C]192[/C][C]12[/C][C]11.5192032904065[/C][C]0.480796709593478[/C][/ROW]
[ROW][C]193[/C][C]3[/C][C]9.03159970110272[/C][C]-6.03159970110272[/C][/ROW]
[ROW][C]194[/C][C]11[/C][C]11.2811869085998[/C][C]-0.281186908599826[/C][/ROW]
[ROW][C]195[/C][C]10[/C][C]9.5667369929435[/C][C]0.433263007056496[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]10.827649568575[/C][C]1.172350431425[/C][/ROW]
[ROW][C]197[/C][C]9[/C][C]11.2147255772068[/C][C]-2.21472557720678[/C][/ROW]
[ROW][C]198[/C][C]12[/C][C]11.5307234823035[/C][C]0.46927651769654[/C][/ROW]
[ROW][C]199[/C][C]9[/C][C]10.1896619480641[/C][C]-1.18966194806413[/C][/ROW]
[ROW][C]200[/C][C]12[/C][C]11.4731057915443[/C][C]0.526894208455732[/C][/ROW]
[ROW][C]201[/C][C]12[/C][C]11.2104328424532[/C][C]0.789567157546822[/C][/ROW]
[ROW][C]202[/C][C]10[/C][C]10.2147524563009[/C][C]-0.21475245630092[/C][/ROW]
[ROW][C]203[/C][C]9[/C][C]8.47140822618522[/C][C]0.528591773814779[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]8.83166261016868[/C][C]3.16833738983132[/C][/ROW]
[ROW][C]205[/C][C]8[/C][C]10.7621155583435[/C][C]-2.76211555834354[/C][/ROW]
[ROW][C]206[/C][C]11[/C][C]10.8142565028505[/C][C]0.185743497149468[/C][/ROW]
[ROW][C]207[/C][C]11[/C][C]11.4140965512804[/C][C]-0.414096551280404[/C][/ROW]
[ROW][C]208[/C][C]12[/C][C]11.1770473444384[/C][C]0.822952655561579[/C][/ROW]
[ROW][C]209[/C][C]10[/C][C]8.41938437277885[/C][C]1.58061562722115[/C][/ROW]
[ROW][C]210[/C][C]10[/C][C]10.7842050896356[/C][C]-0.784205089635588[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]9.02892757387535[/C][C]2.97107242612465[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]9.36150907291611[/C][C]2.63849092708389[/C][/ROW]
[ROW][C]213[/C][C]11[/C][C]11.0920929427108[/C][C]-0.0920929427107802[/C][/ROW]
[ROW][C]214[/C][C]8[/C][C]10.8278237270517[/C][C]-2.82782372705167[/C][/ROW]
[ROW][C]215[/C][C]12[/C][C]11.3315095388965[/C][C]0.668490461103503[/C][/ROW]
[ROW][C]216[/C][C]10[/C][C]9.84032379418891[/C][C]0.159676205811091[/C][/ROW]
[ROW][C]217[/C][C]11[/C][C]11.9464372498271[/C][C]-0.94643724982708[/C][/ROW]
[ROW][C]218[/C][C]10[/C][C]10.1478579912265[/C][C]-0.147857991226483[/C][/ROW]
[ROW][C]219[/C][C]8[/C][C]9.65572977394945[/C][C]-1.65572977394945[/C][/ROW]
[ROW][C]220[/C][C]12[/C][C]10.9040969650385[/C][C]1.09590303496153[/C][/ROW]
[ROW][C]221[/C][C]12[/C][C]9.66309968043876[/C][C]2.33690031956124[/C][/ROW]
[ROW][C]222[/C][C]10[/C][C]10.1542195399661[/C][C]-0.154219539966091[/C][/ROW]
[ROW][C]223[/C][C]12[/C][C]10.7299825909848[/C][C]1.27001740901524[/C][/ROW]
[ROW][C]224[/C][C]9[/C][C]9.04323692015042[/C][C]-0.0432369201504175[/C][/ROW]
[ROW][C]225[/C][C]9[/C][C]9.79407606923288[/C][C]-0.79407606923288[/C][/ROW]
[ROW][C]226[/C][C]6[/C][C]7.0328341601303[/C][C]-1.0328341601303[/C][/ROW]
[ROW][C]227[/C][C]10[/C][C]9.94429512820754[/C][C]0.0557048717924601[/C][/ROW]
[ROW][C]228[/C][C]9[/C][C]10.2690736583203[/C][C]-1.26907365832027[/C][/ROW]
[ROW][C]229[/C][C]9[/C][C]8.47548366807705[/C][C]0.524516331922954[/C][/ROW]
[ROW][C]230[/C][C]9[/C][C]9.14276484104011[/C][C]-0.142764841040107[/C][/ROW]
[ROW][C]231[/C][C]6[/C][C]9.47688083679069[/C][C]-3.47688083679069[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]8.1092124039714[/C][C]1.8907875960286[/C][/ROW]
[ROW][C]233[/C][C]6[/C][C]11.1973218795028[/C][C]-5.19732187950277[/C][/ROW]
[ROW][C]234[/C][C]14[/C][C]12.1916341224675[/C][C]1.8083658775325[/C][/ROW]
[ROW][C]235[/C][C]10[/C][C]9.5269512521011[/C][C]0.473048747898901[/C][/ROW]
[ROW][C]236[/C][C]10[/C][C]8.39781271908446[/C][C]1.60218728091554[/C][/ROW]
[ROW][C]237[/C][C]6[/C][C]4.41659449468581[/C][C]1.58340550531419[/C][/ROW]
[ROW][C]238[/C][C]12[/C][C]9.11852436336315[/C][C]2.88147563663685[/C][/ROW]
[ROW][C]239[/C][C]12[/C][C]11.3514946916539[/C][C]0.648505308346149[/C][/ROW]
[ROW][C]240[/C][C]7[/C][C]7.81343915013309[/C][C]-0.813439150133087[/C][/ROW]
[ROW][C]241[/C][C]8[/C][C]9.29755069029237[/C][C]-1.29755069029237[/C][/ROW]
[ROW][C]242[/C][C]11[/C][C]9.00183338624006[/C][C]1.99816661375994[/C][/ROW]
[ROW][C]243[/C][C]3[/C][C]7.77419402782476[/C][C]-4.77419402782476[/C][/ROW]
[ROW][C]244[/C][C]6[/C][C]9.56012634912521[/C][C]-3.56012634912521[/C][/ROW]
[ROW][C]245[/C][C]10[/C][C]10.5544995008432[/C][C]-0.554499500843181[/C][/ROW]
[ROW][C]246[/C][C]8[/C][C]9.21554908808181[/C][C]-1.21554908808181[/C][/ROW]
[ROW][C]247[/C][C]9[/C][C]10.2032076494036[/C][C]-1.20320764940365[/C][/ROW]
[ROW][C]248[/C][C]9[/C][C]7.57602471401945[/C][C]1.42397528598055[/C][/ROW]
[ROW][C]249[/C][C]8[/C][C]8.72527895601197[/C][C]-0.725278956011966[/C][/ROW]
[ROW][C]250[/C][C]9[/C][C]8.70091459451879[/C][C]0.299085405481213[/C][/ROW]
[ROW][C]251[/C][C]7[/C][C]8.55873619249346[/C][C]-1.55873619249346[/C][/ROW]
[ROW][C]252[/C][C]7[/C][C]7.97634037118907[/C][C]-0.976340371189074[/C][/ROW]
[ROW][C]253[/C][C]6[/C][C]9.03939352453505[/C][C]-3.03939352453505[/C][/ROW]
[ROW][C]254[/C][C]9[/C][C]11.220126290562[/C][C]-2.22012629056204[/C][/ROW]
[ROW][C]255[/C][C]10[/C][C]8.92118668438442[/C][C]1.07881331561558[/C][/ROW]
[ROW][C]256[/C][C]11[/C][C]10.0857797787543[/C][C]0.914220221245735[/C][/ROW]
[ROW][C]257[/C][C]12[/C][C]11.5350591522013[/C][C]0.464940847798686[/C][/ROW]
[ROW][C]258[/C][C]8[/C][C]10.1035206259485[/C][C]-2.1035206259485[/C][/ROW]
[ROW][C]259[/C][C]11[/C][C]9.51525628289975[/C][C]1.48474371710025[/C][/ROW]
[ROW][C]260[/C][C]3[/C][C]4.61008601993585[/C][C]-1.61008601993585[/C][/ROW]
[ROW][C]261[/C][C]11[/C][C]10.6699241696374[/C][C]0.330075830362642[/C][/ROW]
[ROW][C]262[/C][C]12[/C][C]8.64532874031968[/C][C]3.35467125968032[/C][/ROW]
[ROW][C]263[/C][C]7[/C][C]8.48765029671496[/C][C]-1.48765029671496[/C][/ROW]
[ROW][C]264[/C][C]9[/C][C]10.2163226125085[/C][C]-1.21632261250847[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.04626547453231.95373452546774
21111.6532172876031-0.653217287603136
31513.53534664342851.46465335657146
4611.2615878217133-5.26158782171334
51310.80217329364882.19782670635122
6109.930918411828920.0690815881710791
71212.9902122758925-0.990212275892494
81411.38997033491192.61002966508806
91210.71211860855911.28788139144092
10911.353411590483-2.35341159048299
111011.6109497953695-1.61094979536947
121211.56397155803620.436028441963818
131211.77167432208020.228325677919848
141111.700988502694-0.700988502693992
151512.54708960527492.45291039472514
161211.12789466788390.872105332116066
171011.1774671449883-1.17746714498831
181214.1691101038647-2.16911010386473
191112.880412993185-1.88041299318496
201211.5901812835230.409818716476979
211111.6827541949341-0.682754194934128
221211.70173430806960.298265691930389
231313.397163444186-0.397163444185976
241111.7919384702509-0.791938470250908
251212.2639304476598-0.263930447659756
261312.35069893554620.649301064453841
271011.887612977589-1.88761297758902
281411.31536978509132.68463021490868
291211.86953255150920.130467448490794
301010.6015164172884-0.601516417288399
311211.22130664798890.77869335201113
3289.51546225212575-1.51546225212575
331010.4885342671809-0.488534267180919
341211.93646755332840.0635324466715714
351210.63195472384491.36804527615506
3678.44052638355967-1.44052638355967
3798.491276471509060.508723528490942
381210.61154826761271.38845173238734
391011.7733942510822-1.77339425108221
401011.7524389748528-1.75243897485282
411011.6866941013094-1.68669410130936
421210.68838299084291.3116170091571
431513.7122897453881.28771025461197
441010.5409124081636-0.540912408163603
451010.36553830014-0.365538300140013
46129.080343498367022.91965650163298
471310.68681417068862.31318582931142
481111.2752636434113-0.275263643411348
491111.957693043487-0.957693043487047
501210.61296646070671.38703353929331
511411.96764945076982.03235054923019
521010.5604850292984-0.560485029298416
53129.593431230418662.40656876958134
541311.72885618414771.27114381585226
5557.91912314111385-2.91912314111385
56610.6810711279966-4.68107112799664
571211.70297572921890.297024270781123
581211.82316400439830.176835995601711
591111.3596418520282-0.359641852028222
601011.7942951175218-1.79429511752177
6179.37996456258237-2.37996456258237
621211.83647629515620.163523704843753
631411.97398365889292.02601634110711
641110.65562819930860.344371800691379
651211.53711730476190.462882695238065
661312.32378332155560.676216678444417
671412.61125277320871.38874722679127
681112.5756960492961-1.57569604929606
69129.405433149725072.59456685027493
701211.60600376902590.393996230974126
7188.12768980802669-0.127689808026694
721110.47475916881320.525240831186798
731412.92890957118421.07109042881581
741412.86874367526021.1312563247398
751211.41556975279940.584430247200564
76911.9312375894376-2.9312375894376
771311.53892037142821.4610796285718
781111.3717792255892-0.371779225589165
79129.898012693457372.10198730654263
801211.11339366240920.886606337590783
811211.73239411802750.267605881972518
821211.59207791238450.407922087615475
831210.91453356971651.08546643028351
841111.01319547961-0.0131954796099983
851011.4851765787437-1.48517657874368
86910.5871867752182-1.58718677521817
871211.54635916403920.453640835960805
881211.4330531610240.566946838975995
891211.04486452287150.955135477128459
9099.38995740154885-0.38995740154885
911511.86291130483253.13708869516747
921211.84291679964140.157083200358607
931210.9134927301771.08650726982302
94129.940110306943762.05988969305624
951011.6090961795644-1.60909617956438
961311.5152937678431.48470623215699
97911.4990274324862-2.49902743248619
981211.54908395464120.450916045358835
991010.5823254054931-0.58232540549311
1001411.57260907844452.42739092155549
1011111.5552785635279-0.555278563527856
1021513.90240089079131.09759910920869
1031110.76657576100580.233424238994205
1041111.813686160253-0.813686160253049
105129.754555828381362.24544417161864
1061212.0551914947585-0.0551914947584923
1071211.59313831208220.406861687917821
1081111.4752848219902-0.475284821990163
10979.28729649973635-2.28729649973635
1101211.6921555292740.307844470725987
1111411.64648971834542.35351028165459
1121112.5160460068729-1.51604600687293
113119.903988234528341.09601176547166
114109.23457267944020.765427320559802
1151312.58915441336160.410845586638388
1161310.55213138111432.44786861888571
117810.564020496988-2.56402049698799
118119.9334881383111.066511861689
1191211.68149789335030.318502106649673
120119.913835975506421.08616402449358
1211311.3648090093021.63519099069804
1221210.13327612895211.86672387104794
1231411.65934647166922.34065352833078
1241310.8278945834372.17210541656305
1251511.66161073880273.33838926119734
1261010.9236860545057-0.923686054505656
1271112.1159859214027-1.11598592140267
128910.9966605820264-1.99666058202644
129119.260881653427041.73911834657296
1301011.5869238743829-1.58692387438285
131118.28448010748882.7155198925112
132811.6075453607879-3.6075453607879
133119.22225850196631.7777414980337
1341210.52419242591471.47580757408533
1351210.87162753910251.12837246089747
13699.4195694300199-0.4195694300199
1371110.88131010893620.118689891063776
138108.751896610961581.24810338903843
13989.2242750391345-1.2242750391345
14098.895117356630890.104882643369111
141811.4365547410843-3.43655474108431
142911.2402285910018-2.24022859100178
1431512.30338425951532.69661574048467
1441111.2199313914137-0.219931391413748
14588.09108108686958-0.0910810868695771
1461312.40196078010050.598039219899461
147129.857889678707482.14211032129252
1481211.52033904896480.479660951035235
14999.83526071086305-0.835260710863052
15078.39305267570686-1.39305267570686
1511311.05588648737451.9441135126255
152911.7232835620571-2.72328356205705
153611.7558729307476-5.75587293074755
154810.6542917406382-2.65429174063824
15588.10263671157463-0.102636711574625
1561511.86291130483253.13708869516747
15769.99370619100607-3.99370619100607
158910.9966605820264-1.99666058202644
1591111.5906628073491-0.59066280734911
16089.66429130209418-1.66429130209418
16189.86306656283692-1.86306656283692
162109.375614736092530.624385263907467
16388.94024877087468-0.94024877087468
1641411.75792996957552.24207003042447
1651011.0034308666259-1.00343086662586
16687.881715374997350.118284625002645
1671110.25166377746620.748336222533846
168128.388064774778563.61193522522144
169129.934457974793132.06554202520687
1701211.68557507837990.314424921620121
17158.80329396891115-3.80329396891115
1721211.33655534341280.663444656587167
173109.134952690948890.865047309051106
17477.09045122278771-0.0904512227877074
175128.774924071975673.22507592802433
1761110.9616039096650.0383960903349516
17788.99569886693298-0.995698866932984
17899.01588422206662-0.0158842220666194
1791010.0529741024761-0.052974102476132
18099.25595972601251-0.255959726012514
1811211.19891174254050.801088257459452
18268.33710352041482-2.33710352041482
1831513.02030672390081.97969327609924
1841210.67286747988291.32713252011712
185126.926994111821595.07300588817841
1861211.48668336568010.513316634319855
1871111.8673322380433-0.867332238043285
18878.995452812842-1.995452812842
18978.34392214298767-1.34392214298767
19058.54343144175947-3.54343144175947
1911210.3680856932121.63191430678804
1921211.51920329040650.480796709593478
19339.03159970110272-6.03159970110272
1941111.2811869085998-0.281186908599826
195109.56673699294350.433263007056496
1961210.8276495685751.172350431425
197911.2147255772068-2.21472557720678
1981211.53072348230350.46927651769654
199910.1896619480641-1.18966194806413
2001211.47310579154430.526894208455732
2011211.21043284245320.789567157546822
2021010.2147524563009-0.21475245630092
20398.471408226185220.528591773814779
204128.831662610168683.16833738983132
205810.7621155583435-2.76211555834354
2061110.81425650285050.185743497149468
2071111.4140965512804-0.414096551280404
2081211.17704734443840.822952655561579
209108.419384372778851.58061562722115
2101010.7842050896356-0.784205089635588
211129.028927573875352.97107242612465
212129.361509072916112.63849092708389
2131111.0920929427108-0.0920929427107802
214810.8278237270517-2.82782372705167
2151211.33150953889650.668490461103503
216109.840323794188910.159676205811091
2171111.9464372498271-0.94643724982708
2181010.1478579912265-0.147857991226483
21989.65572977394945-1.65572977394945
2201210.90409696503851.09590303496153
221129.663099680438762.33690031956124
2221010.1542195399661-0.154219539966091
2231210.72998259098481.27001740901524
22499.04323692015042-0.0432369201504175
22599.79407606923288-0.79407606923288
22667.0328341601303-1.0328341601303
227109.944295128207540.0557048717924601
228910.2690736583203-1.26907365832027
22998.475483668077050.524516331922954
23099.14276484104011-0.142764841040107
23169.47688083679069-3.47688083679069
232108.10921240397141.8907875960286
233611.1973218795028-5.19732187950277
2341412.19163412246751.8083658775325
235109.52695125210110.473048747898901
236108.397812719084461.60218728091554
23764.416594494685811.58340550531419
238129.118524363363152.88147563663685
2391211.35149469165390.648505308346149
24077.81343915013309-0.813439150133087
24189.29755069029237-1.29755069029237
242119.001833386240061.99816661375994
24337.77419402782476-4.77419402782476
24469.56012634912521-3.56012634912521
2451010.5544995008432-0.554499500843181
24689.21554908808181-1.21554908808181
247910.2032076494036-1.20320764940365
24897.576024714019451.42397528598055
24988.72527895601197-0.725278956011966
25098.700914594518790.299085405481213
25178.55873619249346-1.55873619249346
25277.97634037118907-0.976340371189074
25369.03939352453505-3.03939352453505
254911.220126290562-2.22012629056204
255108.921186684384421.07881331561558
2561110.08577977875430.914220221245735
2571211.53505915220130.464940847798686
258810.1035206259485-2.1035206259485
259119.515256282899751.48474371710025
26034.61008601993585-1.61008601993585
2611110.66992416963740.330075830362642
262128.645328740319683.35467125968032
26378.48765029671496-1.48765029671496
264910.2163226125085-1.21632261250847






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9952881186898060.009423762620388470.00471188131019423
130.9886289345091050.02274213098178930.0113710654908946
140.9874437764473790.02511244710524170.0125562235526208
150.9791371194226490.04172576115470140.0208628805773507
160.9699136885885210.06017262282295750.0300863114114788
170.9503537385410050.09929252291799030.0496462614589951
180.9470331869027620.1059336261944770.0529668130972383
190.9340559494116440.1318881011767110.0659440505883555
200.9026076654567320.1947846690865360.0973923345432682
210.8745121469995360.2509757060009280.125487853000464
220.8337874758575880.3324250482848250.166212524142412
230.7938054856893970.4123890286212070.206194514310603
240.7584272847954740.4831454304090510.241572715204526
250.7104043026424780.5791913947150430.289595697357521
260.7073453934036050.585309213192790.292654606596395
270.7236984548175170.5526030903649660.276301545182483
280.7293443711433330.5413112577133330.270655628856667
290.6724181232836740.6551637534326520.327581876716326
300.626112960503210.7477740789935810.37388703949679
310.5753353920166250.849329215966750.424664607983375
320.5915770544536510.8168458910926970.408422945546349
330.5314002685413130.9371994629173750.468599731458687
340.474741500968480.949483001936960.52525849903152
350.4540071741844260.9080143483688510.545992825815574
360.4412806544840060.8825613089680120.558719345515994
370.3865535317986320.7731070635972650.613446468201368
380.3671798387898860.7343596775797710.632820161210114
390.3477790261595620.6955580523191230.652220973840439
400.3221605393129170.6443210786258340.677839460687083
410.291593621280880.5831872425617610.70840637871912
420.2592889380834050.5185778761668110.740711061916595
430.3002759698483210.6005519396966410.699724030151679
440.2589303409691210.5178606819382420.741069659030879
450.2193711194176380.4387422388352760.780628880582362
460.256113419812420.512226839624840.74388658018758
470.2739087886496960.5478175772993920.726091211350304
480.2337860409927550.4675720819855090.766213959007245
490.2221781241626760.4443562483253520.777821875837324
500.2044103312139770.4088206624279540.795589668786023
510.2100206428513120.4200412857026240.789979357148688
520.1776268324369870.3552536648739740.822373167563013
530.1814032834095610.3628065668191210.818596716590439
540.1596591324220550.319318264844110.840340867577945
550.2387875845169470.4775751690338940.761212415483053
560.519353261355850.9612934772883010.48064673864415
570.4755446067929730.9510892135859450.524455393207027
580.4316811497189690.8633622994379380.568318850281031
590.3904067155624340.7808134311248680.609593284437566
600.3900238680264570.7800477360529130.609976131973543
610.4027327443823430.8054654887646860.597267255617657
620.3618239829134160.7236479658268330.638176017086584
630.3653309327970940.7306618655941890.634669067202906
640.3258254206386710.6516508412773420.674174579361329
650.2979061675862370.5958123351724740.702093832413763
660.2622639681191980.5245279362383950.737736031880802
670.2362433198977790.4724866397955580.763756680102221
680.2202187470930590.4404374941861180.779781252906941
690.2101571551732390.4203143103464780.789842844826761
700.1841680629551560.3683361259103130.815831937044844
710.1666399110691190.3332798221382370.833360088930881
720.1432121843151860.2864243686303710.856787815684814
730.1226922891810440.2453845783620880.877307710818956
740.1047333168013350.2094666336026690.895266683198665
750.09253773479524080.1850754695904820.907462265204759
760.1194200795299250.238840159059850.880579920470075
770.1067529110844420.2135058221688830.893247088915558
780.08941045404159750.1788209080831950.910589545958402
790.09206339198110820.1841267839622160.907936608018892
800.08445985626033970.1689197125206790.91554014373966
810.07094588656001710.1418917731200340.929054113439983
820.05932409624252560.1186481924850510.940675903757474
830.05039867578397610.1007973515679520.949601324216024
840.04158904005623830.08317808011247660.958410959943762
850.03957007081822060.07914014163644110.960429929181779
860.04639170909384720.09278341818769450.953608290906153
870.0376412603554220.0752825207108440.962358739644578
880.03058366003452050.06116732006904110.969416339965479
890.02566332813969170.05132665627938330.974336671860308
900.02180773051238090.04361546102476180.978192269487619
910.03358013677206370.06716027354412750.966419863227936
920.02810961923071940.05621923846143880.971890380769281
930.02500891395655440.05001782791310880.974991086043446
940.02556048071963420.05112096143926840.974439519280366
950.02661260610059780.05322521220119560.973387393899402
960.02354139528881690.04708279057763390.976458604711183
970.03035577055895770.06071154111791550.969644229441042
980.02451160533627640.04902321067255280.975488394663724
990.02109857908741080.04219715817482160.978901420912589
1000.02370168750464460.04740337500928920.976298312495355
1010.01984765135861870.03969530271723750.980152348641381
1020.01669531681912980.03339063363825960.98330468318087
1030.01317346794122590.02634693588245180.986826532058774
1040.01181098744026250.0236219748805250.988189012559737
1050.01262018060634350.0252403612126870.987379819393656
1060.00991913050989010.01983826101978020.99008086949011
1070.007793085179513580.01558617035902720.992206914820486
1080.006504457347973140.01300891469594630.993495542652027
1090.009465833510912770.01893166702182550.990534166489087
1100.007367105825059110.01473421165011820.992632894174941
1110.008197941007450760.01639588201490150.991802058992549
1120.008699171588525010.017398343177050.991300828411475
1130.007100267579533060.01420053515906610.992899732420467
1140.005670646941905130.01134129388381030.994329353058095
1150.004388910547967880.008777821095935770.995611089452032
1160.004986206552661190.009972413105322370.995013793447339
1170.007449118783216890.01489823756643380.992550881216783
1180.006154546907378790.01230909381475760.993845453092621
1190.004762102076998470.009524204153996940.995237897923002
1200.003956611559595980.007913223119191960.996043388440404
1210.003725206766150280.007450413532300570.99627479323385
1220.003700956058160020.007401912116320030.99629904394184
1230.004291389418977880.008582778837955770.995708610581022
1240.004770365324663630.009540730649327260.995229634675336
1250.008568541352901850.01713708270580370.991431458647098
1260.00723709457657060.01447418915314120.992762905423429
1270.006294189784326150.01258837956865230.993705810215674
1280.006697998443738330.01339599688747670.993302001556262
1290.006612234782313460.01322446956462690.993387765217687
1300.006704053679139180.01340810735827840.993295946320861
1310.008853805017461910.01770761003492380.991146194982538
1320.01774141432789390.03548282865578780.982258585672106
1330.01773920628900150.03547841257800290.982260793710999
1340.01699905665680520.03399811331361030.983000943343195
1350.01505094826746420.03010189653492850.984949051732536
1360.01236142173450910.02472284346901820.987638578265491
1370.009989884821233420.01997976964246680.990010115178767
1380.009595933669577420.01919186733915480.990404066330423
1390.008605343744381990.0172106874887640.991394656255618
1400.007371290104698430.01474258020939690.992628709895302
1410.01303520176184730.02607040352369470.986964798238153
1420.01470596981987680.02941193963975350.985294030180123
1430.02128863237349490.04257726474698980.978711367626505
1440.01717468960126110.03434937920252210.982825310398739
1450.01375325615536120.02750651231072240.986246743844639
1460.01164351256320310.02328702512640620.988356487436797
1470.01487453863088150.0297490772617630.985125461369118
1480.01321055677037030.02642111354074060.98678944322963
1490.01118973637053150.0223794727410630.988810263629468
1500.009866584436029180.01973316887205840.990133415563971
1510.01220803228549440.02441606457098890.987791967714506
1520.01443035555284990.02886071110569970.98556964444715
1530.06496711843933280.1299342368786660.935032881560667
1540.06511036573593590.1302207314718720.934889634264064
1550.0559834681909590.1119669363819180.944016531809041
1560.1060700443864420.2121400887728840.893929955613558
1570.1476997715470640.2953995430941270.852300228452936
1580.1387832870807550.277566574161510.861216712919245
1590.1246550391682190.2493100783364380.875344960831781
1600.1147224126402920.2294448252805840.885277587359708
1610.1047856422606110.2095712845212220.895214357739389
1620.09079042275940230.1815808455188050.909209577240598
1630.08163350948302320.1632670189660460.918366490516977
1640.08650149394227990.173002987884560.91349850605772
1650.07827568247253620.1565513649450720.921724317527464
1660.06642878820661320.1328575764132260.933571211793387
1670.05639055636090620.1127811127218120.943609443639094
1680.09353706336199540.1870741267239910.906462936638005
1690.09439222616461380.1887844523292280.905607773835386
1700.08200526297004830.1640105259400970.917994737029952
1710.143740430101980.2874808602039610.85625956989802
1720.1245078424567780.2490156849135550.875492157543223
1730.109399887671450.21879977534290.89060011232855
1740.09378267944359780.1875653588871960.906217320556402
1750.1294147369235310.2588294738470630.870585263076469
1760.111975806793260.2239516135865190.88802419320674
1770.1004981093507850.200996218701570.899501890649215
1780.08593027014693120.1718605402938620.914069729853069
1790.07232989525671860.1446597905134370.927670104743281
1800.06045004127035680.1209000825407140.939549958729643
1810.0503705306685270.1007410613370540.949629469331473
1820.05723125877134080.1144625175426820.942768741228659
1830.05743615233012240.1148723046602450.942563847669878
1840.05315621537176960.1063124307435390.94684378462823
1850.2152851244964510.4305702489929030.784714875503549
1860.1977817329200750.395563465840150.802218267079925
1870.1766400887098330.3532801774196650.823359911290167
1880.1757939684288080.3515879368576160.824206031571192
1890.1624643750438950.324928750087790.837535624956105
1900.2408438751471070.4816877502942140.759156124852893
1910.2458532603437310.4917065206874620.754146739656269
1920.2165735984627150.433147196925430.783426401537285
1930.5807229111152790.8385541777694430.419277088884721
1940.5489822052418470.9020355895163070.451017794758153
1950.5118990234986330.9762019530027350.488100976501367
1960.4801579652040430.9603159304080850.519842034795957
1970.492110529314170.984221058628340.50788947068583
1980.4560777059172510.9121554118345020.543922294082749
1990.4286476410400020.8572952820800050.571352358959998
2000.4298156365060210.8596312730120420.570184363493979
2010.4041881825701290.8083763651402580.595811817429871
2020.382750819978740.765501639957480.61724918002126
2030.3613523902787240.7227047805574470.638647609721276
2040.4144830407442560.8289660814885110.585516959255744
2050.4443795478848990.8887590957697980.555620452115101
2060.4018351971260830.8036703942521660.598164802873917
2070.3617094792819620.7234189585639240.638290520718038
2080.3235882932338050.6471765864676090.676411706766195
2090.3062886313613180.6125772627226370.693711368638682
2100.2692885495369690.5385770990739380.730711450463031
2110.3332808241971040.6665616483942080.666719175802896
2120.3508496999842310.7016993999684610.649150300015769
2130.3097135331463790.6194270662927580.690286466853621
2140.3276249221303550.6552498442607110.672375077869645
2150.2930042192809310.5860084385618620.706995780719069
2160.2578117526537160.5156235053074310.742188247346284
2170.2231427365816050.446285473163210.776857263418395
2180.1885511047943440.3771022095886880.811448895205656
2190.1861300973962060.3722601947924110.813869902603794
2200.1641809904956120.3283619809912240.835819009504388
2210.2034664649681910.4069329299363810.796533535031809
2220.1698606039684760.3397212079369530.830139396031524
2230.162483953487390.324967906974780.83751604651261
2240.1391497912288440.2782995824576870.860850208771156
2250.1134351757115540.2268703514231080.886564824288446
2260.09143403243129990.18286806486260.9085659675687
2270.07244137339605890.1448827467921180.927558626603941
2280.05705165910731280.1141033182146260.942948340892687
2290.04297087842277750.08594175684555510.957029121577222
2300.03221773487643230.06443546975286460.967782265123568
2310.04700987409168460.09401974818336930.952990125908315
2320.05792399112480810.1158479822496160.942076008875192
2330.2251396172576770.4502792345153530.774860382742323
2340.1982994317038110.3965988634076230.801700568296189
2350.1580498719613950.316099743922790.841950128038605
2360.1566708046633290.3133416093266570.843329195336671
2370.1885084040063270.3770168080126530.811491595993673
2380.2037175637647380.4074351275294760.796282436235262
2390.1947671884349280.3895343768698560.805232811565072
2400.1549969058790090.3099938117580180.845003094120991
2410.1222736974496430.2445473948992870.877726302550357
2420.1544172772813610.3088345545627220.845582722718639
2430.4055149841959340.8110299683918680.594485015804066
2440.5963068133169050.8073863733661890.403693186683095
2450.5123118360074780.9753763279850450.487688163992522
2460.4372116377141450.874423275428290.562788362285855
2470.3599985045439480.7199970090878950.640001495456052
2480.2953321479192560.5906642958385120.704667852080744
2490.211514199650620.4230283993012410.78848580034938
2500.1602205742236090.3204411484472180.839779425776391
2510.2375607972936870.4751215945873750.762439202706313
2520.4109213658181130.8218427316362260.589078634181887

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.995288118689806 & 0.00942376262038847 & 0.00471188131019423 \tabularnewline
13 & 0.988628934509105 & 0.0227421309817893 & 0.0113710654908946 \tabularnewline
14 & 0.987443776447379 & 0.0251124471052417 & 0.0125562235526208 \tabularnewline
15 & 0.979137119422649 & 0.0417257611547014 & 0.0208628805773507 \tabularnewline
16 & 0.969913688588521 & 0.0601726228229575 & 0.0300863114114788 \tabularnewline
17 & 0.950353738541005 & 0.0992925229179903 & 0.0496462614589951 \tabularnewline
18 & 0.947033186902762 & 0.105933626194477 & 0.0529668130972383 \tabularnewline
19 & 0.934055949411644 & 0.131888101176711 & 0.0659440505883555 \tabularnewline
20 & 0.902607665456732 & 0.194784669086536 & 0.0973923345432682 \tabularnewline
21 & 0.874512146999536 & 0.250975706000928 & 0.125487853000464 \tabularnewline
22 & 0.833787475857588 & 0.332425048284825 & 0.166212524142412 \tabularnewline
23 & 0.793805485689397 & 0.412389028621207 & 0.206194514310603 \tabularnewline
24 & 0.758427284795474 & 0.483145430409051 & 0.241572715204526 \tabularnewline
25 & 0.710404302642478 & 0.579191394715043 & 0.289595697357521 \tabularnewline
26 & 0.707345393403605 & 0.58530921319279 & 0.292654606596395 \tabularnewline
27 & 0.723698454817517 & 0.552603090364966 & 0.276301545182483 \tabularnewline
28 & 0.729344371143333 & 0.541311257713333 & 0.270655628856667 \tabularnewline
29 & 0.672418123283674 & 0.655163753432652 & 0.327581876716326 \tabularnewline
30 & 0.62611296050321 & 0.747774078993581 & 0.37388703949679 \tabularnewline
31 & 0.575335392016625 & 0.84932921596675 & 0.424664607983375 \tabularnewline
32 & 0.591577054453651 & 0.816845891092697 & 0.408422945546349 \tabularnewline
33 & 0.531400268541313 & 0.937199462917375 & 0.468599731458687 \tabularnewline
34 & 0.47474150096848 & 0.94948300193696 & 0.52525849903152 \tabularnewline
35 & 0.454007174184426 & 0.908014348368851 & 0.545992825815574 \tabularnewline
36 & 0.441280654484006 & 0.882561308968012 & 0.558719345515994 \tabularnewline
37 & 0.386553531798632 & 0.773107063597265 & 0.613446468201368 \tabularnewline
38 & 0.367179838789886 & 0.734359677579771 & 0.632820161210114 \tabularnewline
39 & 0.347779026159562 & 0.695558052319123 & 0.652220973840439 \tabularnewline
40 & 0.322160539312917 & 0.644321078625834 & 0.677839460687083 \tabularnewline
41 & 0.29159362128088 & 0.583187242561761 & 0.70840637871912 \tabularnewline
42 & 0.259288938083405 & 0.518577876166811 & 0.740711061916595 \tabularnewline
43 & 0.300275969848321 & 0.600551939696641 & 0.699724030151679 \tabularnewline
44 & 0.258930340969121 & 0.517860681938242 & 0.741069659030879 \tabularnewline
45 & 0.219371119417638 & 0.438742238835276 & 0.780628880582362 \tabularnewline
46 & 0.25611341981242 & 0.51222683962484 & 0.74388658018758 \tabularnewline
47 & 0.273908788649696 & 0.547817577299392 & 0.726091211350304 \tabularnewline
48 & 0.233786040992755 & 0.467572081985509 & 0.766213959007245 \tabularnewline
49 & 0.222178124162676 & 0.444356248325352 & 0.777821875837324 \tabularnewline
50 & 0.204410331213977 & 0.408820662427954 & 0.795589668786023 \tabularnewline
51 & 0.210020642851312 & 0.420041285702624 & 0.789979357148688 \tabularnewline
52 & 0.177626832436987 & 0.355253664873974 & 0.822373167563013 \tabularnewline
53 & 0.181403283409561 & 0.362806566819121 & 0.818596716590439 \tabularnewline
54 & 0.159659132422055 & 0.31931826484411 & 0.840340867577945 \tabularnewline
55 & 0.238787584516947 & 0.477575169033894 & 0.761212415483053 \tabularnewline
56 & 0.51935326135585 & 0.961293477288301 & 0.48064673864415 \tabularnewline
57 & 0.475544606792973 & 0.951089213585945 & 0.524455393207027 \tabularnewline
58 & 0.431681149718969 & 0.863362299437938 & 0.568318850281031 \tabularnewline
59 & 0.390406715562434 & 0.780813431124868 & 0.609593284437566 \tabularnewline
60 & 0.390023868026457 & 0.780047736052913 & 0.609976131973543 \tabularnewline
61 & 0.402732744382343 & 0.805465488764686 & 0.597267255617657 \tabularnewline
62 & 0.361823982913416 & 0.723647965826833 & 0.638176017086584 \tabularnewline
63 & 0.365330932797094 & 0.730661865594189 & 0.634669067202906 \tabularnewline
64 & 0.325825420638671 & 0.651650841277342 & 0.674174579361329 \tabularnewline
65 & 0.297906167586237 & 0.595812335172474 & 0.702093832413763 \tabularnewline
66 & 0.262263968119198 & 0.524527936238395 & 0.737736031880802 \tabularnewline
67 & 0.236243319897779 & 0.472486639795558 & 0.763756680102221 \tabularnewline
68 & 0.220218747093059 & 0.440437494186118 & 0.779781252906941 \tabularnewline
69 & 0.210157155173239 & 0.420314310346478 & 0.789842844826761 \tabularnewline
70 & 0.184168062955156 & 0.368336125910313 & 0.815831937044844 \tabularnewline
71 & 0.166639911069119 & 0.333279822138237 & 0.833360088930881 \tabularnewline
72 & 0.143212184315186 & 0.286424368630371 & 0.856787815684814 \tabularnewline
73 & 0.122692289181044 & 0.245384578362088 & 0.877307710818956 \tabularnewline
74 & 0.104733316801335 & 0.209466633602669 & 0.895266683198665 \tabularnewline
75 & 0.0925377347952408 & 0.185075469590482 & 0.907462265204759 \tabularnewline
76 & 0.119420079529925 & 0.23884015905985 & 0.880579920470075 \tabularnewline
77 & 0.106752911084442 & 0.213505822168883 & 0.893247088915558 \tabularnewline
78 & 0.0894104540415975 & 0.178820908083195 & 0.910589545958402 \tabularnewline
79 & 0.0920633919811082 & 0.184126783962216 & 0.907936608018892 \tabularnewline
80 & 0.0844598562603397 & 0.168919712520679 & 0.91554014373966 \tabularnewline
81 & 0.0709458865600171 & 0.141891773120034 & 0.929054113439983 \tabularnewline
82 & 0.0593240962425256 & 0.118648192485051 & 0.940675903757474 \tabularnewline
83 & 0.0503986757839761 & 0.100797351567952 & 0.949601324216024 \tabularnewline
84 & 0.0415890400562383 & 0.0831780801124766 & 0.958410959943762 \tabularnewline
85 & 0.0395700708182206 & 0.0791401416364411 & 0.960429929181779 \tabularnewline
86 & 0.0463917090938472 & 0.0927834181876945 & 0.953608290906153 \tabularnewline
87 & 0.037641260355422 & 0.075282520710844 & 0.962358739644578 \tabularnewline
88 & 0.0305836600345205 & 0.0611673200690411 & 0.969416339965479 \tabularnewline
89 & 0.0256633281396917 & 0.0513266562793833 & 0.974336671860308 \tabularnewline
90 & 0.0218077305123809 & 0.0436154610247618 & 0.978192269487619 \tabularnewline
91 & 0.0335801367720637 & 0.0671602735441275 & 0.966419863227936 \tabularnewline
92 & 0.0281096192307194 & 0.0562192384614388 & 0.971890380769281 \tabularnewline
93 & 0.0250089139565544 & 0.0500178279131088 & 0.974991086043446 \tabularnewline
94 & 0.0255604807196342 & 0.0511209614392684 & 0.974439519280366 \tabularnewline
95 & 0.0266126061005978 & 0.0532252122011956 & 0.973387393899402 \tabularnewline
96 & 0.0235413952888169 & 0.0470827905776339 & 0.976458604711183 \tabularnewline
97 & 0.0303557705589577 & 0.0607115411179155 & 0.969644229441042 \tabularnewline
98 & 0.0245116053362764 & 0.0490232106725528 & 0.975488394663724 \tabularnewline
99 & 0.0210985790874108 & 0.0421971581748216 & 0.978901420912589 \tabularnewline
100 & 0.0237016875046446 & 0.0474033750092892 & 0.976298312495355 \tabularnewline
101 & 0.0198476513586187 & 0.0396953027172375 & 0.980152348641381 \tabularnewline
102 & 0.0166953168191298 & 0.0333906336382596 & 0.98330468318087 \tabularnewline
103 & 0.0131734679412259 & 0.0263469358824518 & 0.986826532058774 \tabularnewline
104 & 0.0118109874402625 & 0.023621974880525 & 0.988189012559737 \tabularnewline
105 & 0.0126201806063435 & 0.025240361212687 & 0.987379819393656 \tabularnewline
106 & 0.0099191305098901 & 0.0198382610197802 & 0.99008086949011 \tabularnewline
107 & 0.00779308517951358 & 0.0155861703590272 & 0.992206914820486 \tabularnewline
108 & 0.00650445734797314 & 0.0130089146959463 & 0.993495542652027 \tabularnewline
109 & 0.00946583351091277 & 0.0189316670218255 & 0.990534166489087 \tabularnewline
110 & 0.00736710582505911 & 0.0147342116501182 & 0.992632894174941 \tabularnewline
111 & 0.00819794100745076 & 0.0163958820149015 & 0.991802058992549 \tabularnewline
112 & 0.00869917158852501 & 0.01739834317705 & 0.991300828411475 \tabularnewline
113 & 0.00710026757953306 & 0.0142005351590661 & 0.992899732420467 \tabularnewline
114 & 0.00567064694190513 & 0.0113412938838103 & 0.994329353058095 \tabularnewline
115 & 0.00438891054796788 & 0.00877782109593577 & 0.995611089452032 \tabularnewline
116 & 0.00498620655266119 & 0.00997241310532237 & 0.995013793447339 \tabularnewline
117 & 0.00744911878321689 & 0.0148982375664338 & 0.992550881216783 \tabularnewline
118 & 0.00615454690737879 & 0.0123090938147576 & 0.993845453092621 \tabularnewline
119 & 0.00476210207699847 & 0.00952420415399694 & 0.995237897923002 \tabularnewline
120 & 0.00395661155959598 & 0.00791322311919196 & 0.996043388440404 \tabularnewline
121 & 0.00372520676615028 & 0.00745041353230057 & 0.99627479323385 \tabularnewline
122 & 0.00370095605816002 & 0.00740191211632003 & 0.99629904394184 \tabularnewline
123 & 0.00429138941897788 & 0.00858277883795577 & 0.995708610581022 \tabularnewline
124 & 0.00477036532466363 & 0.00954073064932726 & 0.995229634675336 \tabularnewline
125 & 0.00856854135290185 & 0.0171370827058037 & 0.991431458647098 \tabularnewline
126 & 0.0072370945765706 & 0.0144741891531412 & 0.992762905423429 \tabularnewline
127 & 0.00629418978432615 & 0.0125883795686523 & 0.993705810215674 \tabularnewline
128 & 0.00669799844373833 & 0.0133959968874767 & 0.993302001556262 \tabularnewline
129 & 0.00661223478231346 & 0.0132244695646269 & 0.993387765217687 \tabularnewline
130 & 0.00670405367913918 & 0.0134081073582784 & 0.993295946320861 \tabularnewline
131 & 0.00885380501746191 & 0.0177076100349238 & 0.991146194982538 \tabularnewline
132 & 0.0177414143278939 & 0.0354828286557878 & 0.982258585672106 \tabularnewline
133 & 0.0177392062890015 & 0.0354784125780029 & 0.982260793710999 \tabularnewline
134 & 0.0169990566568052 & 0.0339981133136103 & 0.983000943343195 \tabularnewline
135 & 0.0150509482674642 & 0.0301018965349285 & 0.984949051732536 \tabularnewline
136 & 0.0123614217345091 & 0.0247228434690182 & 0.987638578265491 \tabularnewline
137 & 0.00998988482123342 & 0.0199797696424668 & 0.990010115178767 \tabularnewline
138 & 0.00959593366957742 & 0.0191918673391548 & 0.990404066330423 \tabularnewline
139 & 0.00860534374438199 & 0.017210687488764 & 0.991394656255618 \tabularnewline
140 & 0.00737129010469843 & 0.0147425802093969 & 0.992628709895302 \tabularnewline
141 & 0.0130352017618473 & 0.0260704035236947 & 0.986964798238153 \tabularnewline
142 & 0.0147059698198768 & 0.0294119396397535 & 0.985294030180123 \tabularnewline
143 & 0.0212886323734949 & 0.0425772647469898 & 0.978711367626505 \tabularnewline
144 & 0.0171746896012611 & 0.0343493792025221 & 0.982825310398739 \tabularnewline
145 & 0.0137532561553612 & 0.0275065123107224 & 0.986246743844639 \tabularnewline
146 & 0.0116435125632031 & 0.0232870251264062 & 0.988356487436797 \tabularnewline
147 & 0.0148745386308815 & 0.029749077261763 & 0.985125461369118 \tabularnewline
148 & 0.0132105567703703 & 0.0264211135407406 & 0.98678944322963 \tabularnewline
149 & 0.0111897363705315 & 0.022379472741063 & 0.988810263629468 \tabularnewline
150 & 0.00986658443602918 & 0.0197331688720584 & 0.990133415563971 \tabularnewline
151 & 0.0122080322854944 & 0.0244160645709889 & 0.987791967714506 \tabularnewline
152 & 0.0144303555528499 & 0.0288607111056997 & 0.98556964444715 \tabularnewline
153 & 0.0649671184393328 & 0.129934236878666 & 0.935032881560667 \tabularnewline
154 & 0.0651103657359359 & 0.130220731471872 & 0.934889634264064 \tabularnewline
155 & 0.055983468190959 & 0.111966936381918 & 0.944016531809041 \tabularnewline
156 & 0.106070044386442 & 0.212140088772884 & 0.893929955613558 \tabularnewline
157 & 0.147699771547064 & 0.295399543094127 & 0.852300228452936 \tabularnewline
158 & 0.138783287080755 & 0.27756657416151 & 0.861216712919245 \tabularnewline
159 & 0.124655039168219 & 0.249310078336438 & 0.875344960831781 \tabularnewline
160 & 0.114722412640292 & 0.229444825280584 & 0.885277587359708 \tabularnewline
161 & 0.104785642260611 & 0.209571284521222 & 0.895214357739389 \tabularnewline
162 & 0.0907904227594023 & 0.181580845518805 & 0.909209577240598 \tabularnewline
163 & 0.0816335094830232 & 0.163267018966046 & 0.918366490516977 \tabularnewline
164 & 0.0865014939422799 & 0.17300298788456 & 0.91349850605772 \tabularnewline
165 & 0.0782756824725362 & 0.156551364945072 & 0.921724317527464 \tabularnewline
166 & 0.0664287882066132 & 0.132857576413226 & 0.933571211793387 \tabularnewline
167 & 0.0563905563609062 & 0.112781112721812 & 0.943609443639094 \tabularnewline
168 & 0.0935370633619954 & 0.187074126723991 & 0.906462936638005 \tabularnewline
169 & 0.0943922261646138 & 0.188784452329228 & 0.905607773835386 \tabularnewline
170 & 0.0820052629700483 & 0.164010525940097 & 0.917994737029952 \tabularnewline
171 & 0.14374043010198 & 0.287480860203961 & 0.85625956989802 \tabularnewline
172 & 0.124507842456778 & 0.249015684913555 & 0.875492157543223 \tabularnewline
173 & 0.10939988767145 & 0.2187997753429 & 0.89060011232855 \tabularnewline
174 & 0.0937826794435978 & 0.187565358887196 & 0.906217320556402 \tabularnewline
175 & 0.129414736923531 & 0.258829473847063 & 0.870585263076469 \tabularnewline
176 & 0.11197580679326 & 0.223951613586519 & 0.88802419320674 \tabularnewline
177 & 0.100498109350785 & 0.20099621870157 & 0.899501890649215 \tabularnewline
178 & 0.0859302701469312 & 0.171860540293862 & 0.914069729853069 \tabularnewline
179 & 0.0723298952567186 & 0.144659790513437 & 0.927670104743281 \tabularnewline
180 & 0.0604500412703568 & 0.120900082540714 & 0.939549958729643 \tabularnewline
181 & 0.050370530668527 & 0.100741061337054 & 0.949629469331473 \tabularnewline
182 & 0.0572312587713408 & 0.114462517542682 & 0.942768741228659 \tabularnewline
183 & 0.0574361523301224 & 0.114872304660245 & 0.942563847669878 \tabularnewline
184 & 0.0531562153717696 & 0.106312430743539 & 0.94684378462823 \tabularnewline
185 & 0.215285124496451 & 0.430570248992903 & 0.784714875503549 \tabularnewline
186 & 0.197781732920075 & 0.39556346584015 & 0.802218267079925 \tabularnewline
187 & 0.176640088709833 & 0.353280177419665 & 0.823359911290167 \tabularnewline
188 & 0.175793968428808 & 0.351587936857616 & 0.824206031571192 \tabularnewline
189 & 0.162464375043895 & 0.32492875008779 & 0.837535624956105 \tabularnewline
190 & 0.240843875147107 & 0.481687750294214 & 0.759156124852893 \tabularnewline
191 & 0.245853260343731 & 0.491706520687462 & 0.754146739656269 \tabularnewline
192 & 0.216573598462715 & 0.43314719692543 & 0.783426401537285 \tabularnewline
193 & 0.580722911115279 & 0.838554177769443 & 0.419277088884721 \tabularnewline
194 & 0.548982205241847 & 0.902035589516307 & 0.451017794758153 \tabularnewline
195 & 0.511899023498633 & 0.976201953002735 & 0.488100976501367 \tabularnewline
196 & 0.480157965204043 & 0.960315930408085 & 0.519842034795957 \tabularnewline
197 & 0.49211052931417 & 0.98422105862834 & 0.50788947068583 \tabularnewline
198 & 0.456077705917251 & 0.912155411834502 & 0.543922294082749 \tabularnewline
199 & 0.428647641040002 & 0.857295282080005 & 0.571352358959998 \tabularnewline
200 & 0.429815636506021 & 0.859631273012042 & 0.570184363493979 \tabularnewline
201 & 0.404188182570129 & 0.808376365140258 & 0.595811817429871 \tabularnewline
202 & 0.38275081997874 & 0.76550163995748 & 0.61724918002126 \tabularnewline
203 & 0.361352390278724 & 0.722704780557447 & 0.638647609721276 \tabularnewline
204 & 0.414483040744256 & 0.828966081488511 & 0.585516959255744 \tabularnewline
205 & 0.444379547884899 & 0.888759095769798 & 0.555620452115101 \tabularnewline
206 & 0.401835197126083 & 0.803670394252166 & 0.598164802873917 \tabularnewline
207 & 0.361709479281962 & 0.723418958563924 & 0.638290520718038 \tabularnewline
208 & 0.323588293233805 & 0.647176586467609 & 0.676411706766195 \tabularnewline
209 & 0.306288631361318 & 0.612577262722637 & 0.693711368638682 \tabularnewline
210 & 0.269288549536969 & 0.538577099073938 & 0.730711450463031 \tabularnewline
211 & 0.333280824197104 & 0.666561648394208 & 0.666719175802896 \tabularnewline
212 & 0.350849699984231 & 0.701699399968461 & 0.649150300015769 \tabularnewline
213 & 0.309713533146379 & 0.619427066292758 & 0.690286466853621 \tabularnewline
214 & 0.327624922130355 & 0.655249844260711 & 0.672375077869645 \tabularnewline
215 & 0.293004219280931 & 0.586008438561862 & 0.706995780719069 \tabularnewline
216 & 0.257811752653716 & 0.515623505307431 & 0.742188247346284 \tabularnewline
217 & 0.223142736581605 & 0.44628547316321 & 0.776857263418395 \tabularnewline
218 & 0.188551104794344 & 0.377102209588688 & 0.811448895205656 \tabularnewline
219 & 0.186130097396206 & 0.372260194792411 & 0.813869902603794 \tabularnewline
220 & 0.164180990495612 & 0.328361980991224 & 0.835819009504388 \tabularnewline
221 & 0.203466464968191 & 0.406932929936381 & 0.796533535031809 \tabularnewline
222 & 0.169860603968476 & 0.339721207936953 & 0.830139396031524 \tabularnewline
223 & 0.16248395348739 & 0.32496790697478 & 0.83751604651261 \tabularnewline
224 & 0.139149791228844 & 0.278299582457687 & 0.860850208771156 \tabularnewline
225 & 0.113435175711554 & 0.226870351423108 & 0.886564824288446 \tabularnewline
226 & 0.0914340324312999 & 0.1828680648626 & 0.9085659675687 \tabularnewline
227 & 0.0724413733960589 & 0.144882746792118 & 0.927558626603941 \tabularnewline
228 & 0.0570516591073128 & 0.114103318214626 & 0.942948340892687 \tabularnewline
229 & 0.0429708784227775 & 0.0859417568455551 & 0.957029121577222 \tabularnewline
230 & 0.0322177348764323 & 0.0644354697528646 & 0.967782265123568 \tabularnewline
231 & 0.0470098740916846 & 0.0940197481833693 & 0.952990125908315 \tabularnewline
232 & 0.0579239911248081 & 0.115847982249616 & 0.942076008875192 \tabularnewline
233 & 0.225139617257677 & 0.450279234515353 & 0.774860382742323 \tabularnewline
234 & 0.198299431703811 & 0.396598863407623 & 0.801700568296189 \tabularnewline
235 & 0.158049871961395 & 0.31609974392279 & 0.841950128038605 \tabularnewline
236 & 0.156670804663329 & 0.313341609326657 & 0.843329195336671 \tabularnewline
237 & 0.188508404006327 & 0.377016808012653 & 0.811491595993673 \tabularnewline
238 & 0.203717563764738 & 0.407435127529476 & 0.796282436235262 \tabularnewline
239 & 0.194767188434928 & 0.389534376869856 & 0.805232811565072 \tabularnewline
240 & 0.154996905879009 & 0.309993811758018 & 0.845003094120991 \tabularnewline
241 & 0.122273697449643 & 0.244547394899287 & 0.877726302550357 \tabularnewline
242 & 0.154417277281361 & 0.308834554562722 & 0.845582722718639 \tabularnewline
243 & 0.405514984195934 & 0.811029968391868 & 0.594485015804066 \tabularnewline
244 & 0.596306813316905 & 0.807386373366189 & 0.403693186683095 \tabularnewline
245 & 0.512311836007478 & 0.975376327985045 & 0.487688163992522 \tabularnewline
246 & 0.437211637714145 & 0.87442327542829 & 0.562788362285855 \tabularnewline
247 & 0.359998504543948 & 0.719997009087895 & 0.640001495456052 \tabularnewline
248 & 0.295332147919256 & 0.590664295838512 & 0.704667852080744 \tabularnewline
249 & 0.21151419965062 & 0.423028399301241 & 0.78848580034938 \tabularnewline
250 & 0.160220574223609 & 0.320441148447218 & 0.839779425776391 \tabularnewline
251 & 0.237560797293687 & 0.475121594587375 & 0.762439202706313 \tabularnewline
252 & 0.410921365818113 & 0.821842731636226 & 0.589078634181887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.995288118689806[/C][C]0.00942376262038847[/C][C]0.00471188131019423[/C][/ROW]
[ROW][C]13[/C][C]0.988628934509105[/C][C]0.0227421309817893[/C][C]0.0113710654908946[/C][/ROW]
[ROW][C]14[/C][C]0.987443776447379[/C][C]0.0251124471052417[/C][C]0.0125562235526208[/C][/ROW]
[ROW][C]15[/C][C]0.979137119422649[/C][C]0.0417257611547014[/C][C]0.0208628805773507[/C][/ROW]
[ROW][C]16[/C][C]0.969913688588521[/C][C]0.0601726228229575[/C][C]0.0300863114114788[/C][/ROW]
[ROW][C]17[/C][C]0.950353738541005[/C][C]0.0992925229179903[/C][C]0.0496462614589951[/C][/ROW]
[ROW][C]18[/C][C]0.947033186902762[/C][C]0.105933626194477[/C][C]0.0529668130972383[/C][/ROW]
[ROW][C]19[/C][C]0.934055949411644[/C][C]0.131888101176711[/C][C]0.0659440505883555[/C][/ROW]
[ROW][C]20[/C][C]0.902607665456732[/C][C]0.194784669086536[/C][C]0.0973923345432682[/C][/ROW]
[ROW][C]21[/C][C]0.874512146999536[/C][C]0.250975706000928[/C][C]0.125487853000464[/C][/ROW]
[ROW][C]22[/C][C]0.833787475857588[/C][C]0.332425048284825[/C][C]0.166212524142412[/C][/ROW]
[ROW][C]23[/C][C]0.793805485689397[/C][C]0.412389028621207[/C][C]0.206194514310603[/C][/ROW]
[ROW][C]24[/C][C]0.758427284795474[/C][C]0.483145430409051[/C][C]0.241572715204526[/C][/ROW]
[ROW][C]25[/C][C]0.710404302642478[/C][C]0.579191394715043[/C][C]0.289595697357521[/C][/ROW]
[ROW][C]26[/C][C]0.707345393403605[/C][C]0.58530921319279[/C][C]0.292654606596395[/C][/ROW]
[ROW][C]27[/C][C]0.723698454817517[/C][C]0.552603090364966[/C][C]0.276301545182483[/C][/ROW]
[ROW][C]28[/C][C]0.729344371143333[/C][C]0.541311257713333[/C][C]0.270655628856667[/C][/ROW]
[ROW][C]29[/C][C]0.672418123283674[/C][C]0.655163753432652[/C][C]0.327581876716326[/C][/ROW]
[ROW][C]30[/C][C]0.62611296050321[/C][C]0.747774078993581[/C][C]0.37388703949679[/C][/ROW]
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[ROW][C]167[/C][C]0.0563905563609062[/C][C]0.112781112721812[/C][C]0.943609443639094[/C][/ROW]
[ROW][C]168[/C][C]0.0935370633619954[/C][C]0.187074126723991[/C][C]0.906462936638005[/C][/ROW]
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[ROW][C]171[/C][C]0.14374043010198[/C][C]0.287480860203961[/C][C]0.85625956989802[/C][/ROW]
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[ROW][C]174[/C][C]0.0937826794435978[/C][C]0.187565358887196[/C][C]0.906217320556402[/C][/ROW]
[ROW][C]175[/C][C]0.129414736923531[/C][C]0.258829473847063[/C][C]0.870585263076469[/C][/ROW]
[ROW][C]176[/C][C]0.11197580679326[/C][C]0.223951613586519[/C][C]0.88802419320674[/C][/ROW]
[ROW][C]177[/C][C]0.100498109350785[/C][C]0.20099621870157[/C][C]0.899501890649215[/C][/ROW]
[ROW][C]178[/C][C]0.0859302701469312[/C][C]0.171860540293862[/C][C]0.914069729853069[/C][/ROW]
[ROW][C]179[/C][C]0.0723298952567186[/C][C]0.144659790513437[/C][C]0.927670104743281[/C][/ROW]
[ROW][C]180[/C][C]0.0604500412703568[/C][C]0.120900082540714[/C][C]0.939549958729643[/C][/ROW]
[ROW][C]181[/C][C]0.050370530668527[/C][C]0.100741061337054[/C][C]0.949629469331473[/C][/ROW]
[ROW][C]182[/C][C]0.0572312587713408[/C][C]0.114462517542682[/C][C]0.942768741228659[/C][/ROW]
[ROW][C]183[/C][C]0.0574361523301224[/C][C]0.114872304660245[/C][C]0.942563847669878[/C][/ROW]
[ROW][C]184[/C][C]0.0531562153717696[/C][C]0.106312430743539[/C][C]0.94684378462823[/C][/ROW]
[ROW][C]185[/C][C]0.215285124496451[/C][C]0.430570248992903[/C][C]0.784714875503549[/C][/ROW]
[ROW][C]186[/C][C]0.197781732920075[/C][C]0.39556346584015[/C][C]0.802218267079925[/C][/ROW]
[ROW][C]187[/C][C]0.176640088709833[/C][C]0.353280177419665[/C][C]0.823359911290167[/C][/ROW]
[ROW][C]188[/C][C]0.175793968428808[/C][C]0.351587936857616[/C][C]0.824206031571192[/C][/ROW]
[ROW][C]189[/C][C]0.162464375043895[/C][C]0.32492875008779[/C][C]0.837535624956105[/C][/ROW]
[ROW][C]190[/C][C]0.240843875147107[/C][C]0.481687750294214[/C][C]0.759156124852893[/C][/ROW]
[ROW][C]191[/C][C]0.245853260343731[/C][C]0.491706520687462[/C][C]0.754146739656269[/C][/ROW]
[ROW][C]192[/C][C]0.216573598462715[/C][C]0.43314719692543[/C][C]0.783426401537285[/C][/ROW]
[ROW][C]193[/C][C]0.580722911115279[/C][C]0.838554177769443[/C][C]0.419277088884721[/C][/ROW]
[ROW][C]194[/C][C]0.548982205241847[/C][C]0.902035589516307[/C][C]0.451017794758153[/C][/ROW]
[ROW][C]195[/C][C]0.511899023498633[/C][C]0.976201953002735[/C][C]0.488100976501367[/C][/ROW]
[ROW][C]196[/C][C]0.480157965204043[/C][C]0.960315930408085[/C][C]0.519842034795957[/C][/ROW]
[ROW][C]197[/C][C]0.49211052931417[/C][C]0.98422105862834[/C][C]0.50788947068583[/C][/ROW]
[ROW][C]198[/C][C]0.456077705917251[/C][C]0.912155411834502[/C][C]0.543922294082749[/C][/ROW]
[ROW][C]199[/C][C]0.428647641040002[/C][C]0.857295282080005[/C][C]0.571352358959998[/C][/ROW]
[ROW][C]200[/C][C]0.429815636506021[/C][C]0.859631273012042[/C][C]0.570184363493979[/C][/ROW]
[ROW][C]201[/C][C]0.404188182570129[/C][C]0.808376365140258[/C][C]0.595811817429871[/C][/ROW]
[ROW][C]202[/C][C]0.38275081997874[/C][C]0.76550163995748[/C][C]0.61724918002126[/C][/ROW]
[ROW][C]203[/C][C]0.361352390278724[/C][C]0.722704780557447[/C][C]0.638647609721276[/C][/ROW]
[ROW][C]204[/C][C]0.414483040744256[/C][C]0.828966081488511[/C][C]0.585516959255744[/C][/ROW]
[ROW][C]205[/C][C]0.444379547884899[/C][C]0.888759095769798[/C][C]0.555620452115101[/C][/ROW]
[ROW][C]206[/C][C]0.401835197126083[/C][C]0.803670394252166[/C][C]0.598164802873917[/C][/ROW]
[ROW][C]207[/C][C]0.361709479281962[/C][C]0.723418958563924[/C][C]0.638290520718038[/C][/ROW]
[ROW][C]208[/C][C]0.323588293233805[/C][C]0.647176586467609[/C][C]0.676411706766195[/C][/ROW]
[ROW][C]209[/C][C]0.306288631361318[/C][C]0.612577262722637[/C][C]0.693711368638682[/C][/ROW]
[ROW][C]210[/C][C]0.269288549536969[/C][C]0.538577099073938[/C][C]0.730711450463031[/C][/ROW]
[ROW][C]211[/C][C]0.333280824197104[/C][C]0.666561648394208[/C][C]0.666719175802896[/C][/ROW]
[ROW][C]212[/C][C]0.350849699984231[/C][C]0.701699399968461[/C][C]0.649150300015769[/C][/ROW]
[ROW][C]213[/C][C]0.309713533146379[/C][C]0.619427066292758[/C][C]0.690286466853621[/C][/ROW]
[ROW][C]214[/C][C]0.327624922130355[/C][C]0.655249844260711[/C][C]0.672375077869645[/C][/ROW]
[ROW][C]215[/C][C]0.293004219280931[/C][C]0.586008438561862[/C][C]0.706995780719069[/C][/ROW]
[ROW][C]216[/C][C]0.257811752653716[/C][C]0.515623505307431[/C][C]0.742188247346284[/C][/ROW]
[ROW][C]217[/C][C]0.223142736581605[/C][C]0.44628547316321[/C][C]0.776857263418395[/C][/ROW]
[ROW][C]218[/C][C]0.188551104794344[/C][C]0.377102209588688[/C][C]0.811448895205656[/C][/ROW]
[ROW][C]219[/C][C]0.186130097396206[/C][C]0.372260194792411[/C][C]0.813869902603794[/C][/ROW]
[ROW][C]220[/C][C]0.164180990495612[/C][C]0.328361980991224[/C][C]0.835819009504388[/C][/ROW]
[ROW][C]221[/C][C]0.203466464968191[/C][C]0.406932929936381[/C][C]0.796533535031809[/C][/ROW]
[ROW][C]222[/C][C]0.169860603968476[/C][C]0.339721207936953[/C][C]0.830139396031524[/C][/ROW]
[ROW][C]223[/C][C]0.16248395348739[/C][C]0.32496790697478[/C][C]0.83751604651261[/C][/ROW]
[ROW][C]224[/C][C]0.139149791228844[/C][C]0.278299582457687[/C][C]0.860850208771156[/C][/ROW]
[ROW][C]225[/C][C]0.113435175711554[/C][C]0.226870351423108[/C][C]0.886564824288446[/C][/ROW]
[ROW][C]226[/C][C]0.0914340324312999[/C][C]0.1828680648626[/C][C]0.9085659675687[/C][/ROW]
[ROW][C]227[/C][C]0.0724413733960589[/C][C]0.144882746792118[/C][C]0.927558626603941[/C][/ROW]
[ROW][C]228[/C][C]0.0570516591073128[/C][C]0.114103318214626[/C][C]0.942948340892687[/C][/ROW]
[ROW][C]229[/C][C]0.0429708784227775[/C][C]0.0859417568455551[/C][C]0.957029121577222[/C][/ROW]
[ROW][C]230[/C][C]0.0322177348764323[/C][C]0.0644354697528646[/C][C]0.967782265123568[/C][/ROW]
[ROW][C]231[/C][C]0.0470098740916846[/C][C]0.0940197481833693[/C][C]0.952990125908315[/C][/ROW]
[ROW][C]232[/C][C]0.0579239911248081[/C][C]0.115847982249616[/C][C]0.942076008875192[/C][/ROW]
[ROW][C]233[/C][C]0.225139617257677[/C][C]0.450279234515353[/C][C]0.774860382742323[/C][/ROW]
[ROW][C]234[/C][C]0.198299431703811[/C][C]0.396598863407623[/C][C]0.801700568296189[/C][/ROW]
[ROW][C]235[/C][C]0.158049871961395[/C][C]0.31609974392279[/C][C]0.841950128038605[/C][/ROW]
[ROW][C]236[/C][C]0.156670804663329[/C][C]0.313341609326657[/C][C]0.843329195336671[/C][/ROW]
[ROW][C]237[/C][C]0.188508404006327[/C][C]0.377016808012653[/C][C]0.811491595993673[/C][/ROW]
[ROW][C]238[/C][C]0.203717563764738[/C][C]0.407435127529476[/C][C]0.796282436235262[/C][/ROW]
[ROW][C]239[/C][C]0.194767188434928[/C][C]0.389534376869856[/C][C]0.805232811565072[/C][/ROW]
[ROW][C]240[/C][C]0.154996905879009[/C][C]0.309993811758018[/C][C]0.845003094120991[/C][/ROW]
[ROW][C]241[/C][C]0.122273697449643[/C][C]0.244547394899287[/C][C]0.877726302550357[/C][/ROW]
[ROW][C]242[/C][C]0.154417277281361[/C][C]0.308834554562722[/C][C]0.845582722718639[/C][/ROW]
[ROW][C]243[/C][C]0.405514984195934[/C][C]0.811029968391868[/C][C]0.594485015804066[/C][/ROW]
[ROW][C]244[/C][C]0.596306813316905[/C][C]0.807386373366189[/C][C]0.403693186683095[/C][/ROW]
[ROW][C]245[/C][C]0.512311836007478[/C][C]0.975376327985045[/C][C]0.487688163992522[/C][/ROW]
[ROW][C]246[/C][C]0.437211637714145[/C][C]0.87442327542829[/C][C]0.562788362285855[/C][/ROW]
[ROW][C]247[/C][C]0.359998504543948[/C][C]0.719997009087895[/C][C]0.640001495456052[/C][/ROW]
[ROW][C]248[/C][C]0.295332147919256[/C][C]0.590664295838512[/C][C]0.704667852080744[/C][/ROW]
[ROW][C]249[/C][C]0.21151419965062[/C][C]0.423028399301241[/C][C]0.78848580034938[/C][/ROW]
[ROW][C]250[/C][C]0.160220574223609[/C][C]0.320441148447218[/C][C]0.839779425776391[/C][/ROW]
[ROW][C]251[/C][C]0.237560797293687[/C][C]0.475121594587375[/C][C]0.762439202706313[/C][/ROW]
[ROW][C]252[/C][C]0.410921365818113[/C][C]0.821842731636226[/C][C]0.589078634181887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9952881186898060.009423762620388470.00471188131019423
130.9886289345091050.02274213098178930.0113710654908946
140.9874437764473790.02511244710524170.0125562235526208
150.9791371194226490.04172576115470140.0208628805773507
160.9699136885885210.06017262282295750.0300863114114788
170.9503537385410050.09929252291799030.0496462614589951
180.9470331869027620.1059336261944770.0529668130972383
190.9340559494116440.1318881011767110.0659440505883555
200.9026076654567320.1947846690865360.0973923345432682
210.8745121469995360.2509757060009280.125487853000464
220.8337874758575880.3324250482848250.166212524142412
230.7938054856893970.4123890286212070.206194514310603
240.7584272847954740.4831454304090510.241572715204526
250.7104043026424780.5791913947150430.289595697357521
260.7073453934036050.585309213192790.292654606596395
270.7236984548175170.5526030903649660.276301545182483
280.7293443711433330.5413112577133330.270655628856667
290.6724181232836740.6551637534326520.327581876716326
300.626112960503210.7477740789935810.37388703949679
310.5753353920166250.849329215966750.424664607983375
320.5915770544536510.8168458910926970.408422945546349
330.5314002685413130.9371994629173750.468599731458687
340.474741500968480.949483001936960.52525849903152
350.4540071741844260.9080143483688510.545992825815574
360.4412806544840060.8825613089680120.558719345515994
370.3865535317986320.7731070635972650.613446468201368
380.3671798387898860.7343596775797710.632820161210114
390.3477790261595620.6955580523191230.652220973840439
400.3221605393129170.6443210786258340.677839460687083
410.291593621280880.5831872425617610.70840637871912
420.2592889380834050.5185778761668110.740711061916595
430.3002759698483210.6005519396966410.699724030151679
440.2589303409691210.5178606819382420.741069659030879
450.2193711194176380.4387422388352760.780628880582362
460.256113419812420.512226839624840.74388658018758
470.2739087886496960.5478175772993920.726091211350304
480.2337860409927550.4675720819855090.766213959007245
490.2221781241626760.4443562483253520.777821875837324
500.2044103312139770.4088206624279540.795589668786023
510.2100206428513120.4200412857026240.789979357148688
520.1776268324369870.3552536648739740.822373167563013
530.1814032834095610.3628065668191210.818596716590439
540.1596591324220550.319318264844110.840340867577945
550.2387875845169470.4775751690338940.761212415483053
560.519353261355850.9612934772883010.48064673864415
570.4755446067929730.9510892135859450.524455393207027
580.4316811497189690.8633622994379380.568318850281031
590.3904067155624340.7808134311248680.609593284437566
600.3900238680264570.7800477360529130.609976131973543
610.4027327443823430.8054654887646860.597267255617657
620.3618239829134160.7236479658268330.638176017086584
630.3653309327970940.7306618655941890.634669067202906
640.3258254206386710.6516508412773420.674174579361329
650.2979061675862370.5958123351724740.702093832413763
660.2622639681191980.5245279362383950.737736031880802
670.2362433198977790.4724866397955580.763756680102221
680.2202187470930590.4404374941861180.779781252906941
690.2101571551732390.4203143103464780.789842844826761
700.1841680629551560.3683361259103130.815831937044844
710.1666399110691190.3332798221382370.833360088930881
720.1432121843151860.2864243686303710.856787815684814
730.1226922891810440.2453845783620880.877307710818956
740.1047333168013350.2094666336026690.895266683198665
750.09253773479524080.1850754695904820.907462265204759
760.1194200795299250.238840159059850.880579920470075
770.1067529110844420.2135058221688830.893247088915558
780.08941045404159750.1788209080831950.910589545958402
790.09206339198110820.1841267839622160.907936608018892
800.08445985626033970.1689197125206790.91554014373966
810.07094588656001710.1418917731200340.929054113439983
820.05932409624252560.1186481924850510.940675903757474
830.05039867578397610.1007973515679520.949601324216024
840.04158904005623830.08317808011247660.958410959943762
850.03957007081822060.07914014163644110.960429929181779
860.04639170909384720.09278341818769450.953608290906153
870.0376412603554220.0752825207108440.962358739644578
880.03058366003452050.06116732006904110.969416339965479
890.02566332813969170.05132665627938330.974336671860308
900.02180773051238090.04361546102476180.978192269487619
910.03358013677206370.06716027354412750.966419863227936
920.02810961923071940.05621923846143880.971890380769281
930.02500891395655440.05001782791310880.974991086043446
940.02556048071963420.05112096143926840.974439519280366
950.02661260610059780.05322521220119560.973387393899402
960.02354139528881690.04708279057763390.976458604711183
970.03035577055895770.06071154111791550.969644229441042
980.02451160533627640.04902321067255280.975488394663724
990.02109857908741080.04219715817482160.978901420912589
1000.02370168750464460.04740337500928920.976298312495355
1010.01984765135861870.03969530271723750.980152348641381
1020.01669531681912980.03339063363825960.98330468318087
1030.01317346794122590.02634693588245180.986826532058774
1040.01181098744026250.0236219748805250.988189012559737
1050.01262018060634350.0252403612126870.987379819393656
1060.00991913050989010.01983826101978020.99008086949011
1070.007793085179513580.01558617035902720.992206914820486
1080.006504457347973140.01300891469594630.993495542652027
1090.009465833510912770.01893166702182550.990534166489087
1100.007367105825059110.01473421165011820.992632894174941
1110.008197941007450760.01639588201490150.991802058992549
1120.008699171588525010.017398343177050.991300828411475
1130.007100267579533060.01420053515906610.992899732420467
1140.005670646941905130.01134129388381030.994329353058095
1150.004388910547967880.008777821095935770.995611089452032
1160.004986206552661190.009972413105322370.995013793447339
1170.007449118783216890.01489823756643380.992550881216783
1180.006154546907378790.01230909381475760.993845453092621
1190.004762102076998470.009524204153996940.995237897923002
1200.003956611559595980.007913223119191960.996043388440404
1210.003725206766150280.007450413532300570.99627479323385
1220.003700956058160020.007401912116320030.99629904394184
1230.004291389418977880.008582778837955770.995708610581022
1240.004770365324663630.009540730649327260.995229634675336
1250.008568541352901850.01713708270580370.991431458647098
1260.00723709457657060.01447418915314120.992762905423429
1270.006294189784326150.01258837956865230.993705810215674
1280.006697998443738330.01339599688747670.993302001556262
1290.006612234782313460.01322446956462690.993387765217687
1300.006704053679139180.01340810735827840.993295946320861
1310.008853805017461910.01770761003492380.991146194982538
1320.01774141432789390.03548282865578780.982258585672106
1330.01773920628900150.03547841257800290.982260793710999
1340.01699905665680520.03399811331361030.983000943343195
1350.01505094826746420.03010189653492850.984949051732536
1360.01236142173450910.02472284346901820.987638578265491
1370.009989884821233420.01997976964246680.990010115178767
1380.009595933669577420.01919186733915480.990404066330423
1390.008605343744381990.0172106874887640.991394656255618
1400.007371290104698430.01474258020939690.992628709895302
1410.01303520176184730.02607040352369470.986964798238153
1420.01470596981987680.02941193963975350.985294030180123
1430.02128863237349490.04257726474698980.978711367626505
1440.01717468960126110.03434937920252210.982825310398739
1450.01375325615536120.02750651231072240.986246743844639
1460.01164351256320310.02328702512640620.988356487436797
1470.01487453863088150.0297490772617630.985125461369118
1480.01321055677037030.02642111354074060.98678944322963
1490.01118973637053150.0223794727410630.988810263629468
1500.009866584436029180.01973316887205840.990133415563971
1510.01220803228549440.02441606457098890.987791967714506
1520.01443035555284990.02886071110569970.98556964444715
1530.06496711843933280.1299342368786660.935032881560667
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1580.1387832870807550.277566574161510.861216712919245
1590.1246550391682190.2493100783364380.875344960831781
1600.1147224126402920.2294448252805840.885277587359708
1610.1047856422606110.2095712845212220.895214357739389
1620.09079042275940230.1815808455188050.909209577240598
1630.08163350948302320.1632670189660460.918366490516977
1640.08650149394227990.173002987884560.91349850605772
1650.07827568247253620.1565513649450720.921724317527464
1660.06642878820661320.1328575764132260.933571211793387
1670.05639055636090620.1127811127218120.943609443639094
1680.09353706336199540.1870741267239910.906462936638005
1690.09439222616461380.1887844523292280.905607773835386
1700.08200526297004830.1640105259400970.917994737029952
1710.143740430101980.2874808602039610.85625956989802
1720.1245078424567780.2490156849135550.875492157543223
1730.109399887671450.21879977534290.89060011232855
1740.09378267944359780.1875653588871960.906217320556402
1750.1294147369235310.2588294738470630.870585263076469
1760.111975806793260.2239516135865190.88802419320674
1770.1004981093507850.200996218701570.899501890649215
1780.08593027014693120.1718605402938620.914069729853069
1790.07232989525671860.1446597905134370.927670104743281
1800.06045004127035680.1209000825407140.939549958729643
1810.0503705306685270.1007410613370540.949629469331473
1820.05723125877134080.1144625175426820.942768741228659
1830.05743615233012240.1148723046602450.942563847669878
1840.05315621537176960.1063124307435390.94684378462823
1850.2152851244964510.4305702489929030.784714875503549
1860.1977817329200750.395563465840150.802218267079925
1870.1766400887098330.3532801774196650.823359911290167
1880.1757939684288080.3515879368576160.824206031571192
1890.1624643750438950.324928750087790.837535624956105
1900.2408438751471070.4816877502942140.759156124852893
1910.2458532603437310.4917065206874620.754146739656269
1920.2165735984627150.433147196925430.783426401537285
1930.5807229111152790.8385541777694430.419277088884721
1940.5489822052418470.9020355895163070.451017794758153
1950.5118990234986330.9762019530027350.488100976501367
1960.4801579652040430.9603159304080850.519842034795957
1970.492110529314170.984221058628340.50788947068583
1980.4560777059172510.9121554118345020.543922294082749
1990.4286476410400020.8572952820800050.571352358959998
2000.4298156365060210.8596312730120420.570184363493979
2010.4041881825701290.8083763651402580.595811817429871
2020.382750819978740.765501639957480.61724918002126
2030.3613523902787240.7227047805574470.638647609721276
2040.4144830407442560.8289660814885110.585516959255744
2050.4443795478848990.8887590957697980.555620452115101
2060.4018351971260830.8036703942521660.598164802873917
2070.3617094792819620.7234189585639240.638290520718038
2080.3235882932338050.6471765864676090.676411706766195
2090.3062886313613180.6125772627226370.693711368638682
2100.2692885495369690.5385770990739380.730711450463031
2110.3332808241971040.6665616483942080.666719175802896
2120.3508496999842310.7016993999684610.649150300015769
2130.3097135331463790.6194270662927580.690286466853621
2140.3276249221303550.6552498442607110.672375077869645
2150.2930042192809310.5860084385618620.706995780719069
2160.2578117526537160.5156235053074310.742188247346284
2170.2231427365816050.446285473163210.776857263418395
2180.1885511047943440.3771022095886880.811448895205656
2190.1861300973962060.3722601947924110.813869902603794
2200.1641809904956120.3283619809912240.835819009504388
2210.2034664649681910.4069329299363810.796533535031809
2220.1698606039684760.3397212079369530.830139396031524
2230.162483953487390.324967906974780.83751604651261
2240.1391497912288440.2782995824576870.860850208771156
2250.1134351757115540.2268703514231080.886564824288446
2260.09143403243129990.18286806486260.9085659675687
2270.07244137339605890.1448827467921180.927558626603941
2280.05705165910731280.1141033182146260.942948340892687
2290.04297087842277750.08594175684555510.957029121577222
2300.03221773487643230.06443546975286460.967782265123568
2310.04700987409168460.09401974818336930.952990125908315
2320.05792399112480810.1158479822496160.942076008875192
2330.2251396172576770.4502792345153530.774860382742323
2340.1982994317038110.3965988634076230.801700568296189
2350.1580498719613950.316099743922790.841950128038605
2360.1566708046633290.3133416093266570.843329195336671
2370.1885084040063270.3770168080126530.811491595993673
2380.2037175637647380.4074351275294760.796282436235262
2390.1947671884349280.3895343768698560.805232811565072
2400.1549969058790090.3099938117580180.845003094120991
2410.1222736974496430.2445473948992870.877726302550357
2420.1544172772813610.3088345545627220.845582722718639
2430.4055149841959340.8110299683918680.594485015804066
2440.5963068133169050.8073863733661890.403693186683095
2450.5123118360074780.9753763279850450.487688163992522
2460.4372116377141450.874423275428290.562788362285855
2470.3599985045439480.7199970090878950.640001495456052
2480.2953321479192560.5906642958385120.704667852080744
2490.211514199650620.4230283993012410.78848580034938
2500.1602205742236090.3204411484472180.839779425776391
2510.2375607972936870.4751215945873750.762439202706313
2520.4109213658181130.8218427316362260.589078634181887






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.037344398340249NOK
5% type I error level610.253112033195021NOK
10% type I error level780.323651452282158NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 9 & 0.037344398340249 & NOK \tabularnewline
5% type I error level & 61 & 0.253112033195021 & NOK \tabularnewline
10% type I error level & 78 & 0.323651452282158 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190276&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]9[/C][C]0.037344398340249[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.253112033195021[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]78[/C][C]0.323651452282158[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190276&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190276&T=6

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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.037344398340249NOK
5% type I error level610.253112033195021NOK
10% type I error level780.323651452282158NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}