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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 12 Nov 2014 17:33:56 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/12/t1415813656bjx12crfmwvy76x.htm/, Retrieved Sun, 19 May 2024 15:57:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253993, Retrieved Sun, 19 May 2024 15:57:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RMP   [Multiple Regression] [multiple linear r...] [2014-11-11 17:10:45] [3d5212c89039da1a3a24d8e18d23c716]
-   P       [Multiple Regression] [linear regression] [2014-11-12 17:33:56] [6c0da333d5967ad7c023c95d07a1face] [Current]
- R  D        [Multiple Regression] [linear regression] [2014-11-12 17:44:53] [3d5212c89039da1a3a24d8e18d23c716]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	83	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	9	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	9.5	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	12	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	31	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	10	7	16	8	71	44
29	31	10	9	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	15	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	35	14	6	15	14.5	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	80	48
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	12.5	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	85	52
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	67	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
36	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	85	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	11	9	13	12	69	41
32	34	16	8	15	10	54	33
30	37	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
38	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	38	12	8	12	18	84	50
34	36	13	8	13	16	69	46
27	26	13	10	10	24	66	43
31	26	12	8	14	14	81	47
38	33	17	14	16	20	82	50
34	39	15	10	10	18	72	43
24	30	10	8	11	23	54	33
30	33	14	11	14	12	78	48
26	25	11	12	12	14	74	44
34	38	13	12	9	16	82	50
27	37	16	12	9	18	73	41
37	31	12	5	11	20	55	34
36	37	16	12	16	12	72	44
41	35	12	10	9	12	78	47
29	25	9	7	13	17	59	35
36	28	12	12	16	13	72	44
32	35	15	11	13	9	78	44
37	33	12	8	9	16	68	43
30	30	12	9	12	18	69	41
31	31	14	10	16	10	67	41
38	37	12	9	11	14	74	42
36	36	16	12	14	11	54	33
35	30	11	6	13	9	67	41
31	36	19	15	15	11	70	44
38	32	15	12	14	10	80	48
22	28	8	12	16	11	89	55
32	36	16	12	13	19	76	44
36	34	17	11	14	14	74	43
39	31	12	7	15	12	87	52
28	28	11	7	13	14	54	30
32	36	11	5	11	21	61	39
32	36	14	12	11	13	38	11
38	40	16	12	14	10	75	44
32	33	12	3	15	15	69	42
35	37	16	11	11	16	62	41
32	32	13	10	15	14	72	44
37	38	15	12	12	12	70	44
34	31	16	9	14	19	79	48
33	37	16	12	14	15	87	53
33	33	14	9	8	19	62	37
26	32	16	12	13	13	77	44
30	30	16	12	9	17	69	44
24	30	14	10	15	12	69	40
34	31	11	9	17	11	75	42
34	32	12	12	13	14	54	35
33	34	15	8	15	11	72	43
34	36	15	11	15	13	74	45
35	37	16	11	14	12	85	55
35	36	16	12	16	15	52	31
36	33	11	10	13	14	70	44
34	33	15	10	16	12	84	50
34	33	12	12	9	17	64	40
41	44	12	12	16	11	84	53
32	39	15	11	11	18	87	54
30	32	15	8	10	13	79	49
35	35	16	12	11	17	67	40
28	25	14	10	15	13	65	41
33	35	17	11	17	11	85	52
39	34	14	10	14	12	83	52
36	35	13	8	8	22	61	36
36	39	15	12	15	14	82	52
35	33	13	12	11	12	76	46
38	36	14	10	16	12	58	31
33	32	15	12	10	17	72	44
31	32	12	9	15	9	72	44
34	36	13	9	9	21	38	11
32	36	8	6	16	10	78	46
31	32	14	10	19	11	54	33
33	34	14	9	12	12	63	34
34	33	11	9	8	23	66	42
34	35	12	9	11	13	70	43
34	30	13	6	14	12	71	43
33	38	10	10	9	16	67	44
32	34	16	6	15	9	58	36
41	33	18	14	13	17	72	46
34	32	13	10	16	9	72	44
36	31	11	10	11	14	70	43
37	30	4	6	12	17	76	50
36	27	13	12	13	13	50	33
29	31	16	12	10	11	72	43
37	30	10	7	11	12	72	44
27	32	12	8	12	10	88	53
35	35	12	11	8	19	53	34
28	28	10	3	12	16	58	35
35	33	13	6	12	16	66	40
37	31	15	10	15	14	82	53
29	35	12	8	11	20	69	42
32	35	14	9	13	15	68	43
36	32	10	9	14	23	44	29
19	21	12	8	10	20	56	36
21	20	12	9	12	16	53	30
31	34	11	7	15	14	70	42
33	32	10	7	13	17	78	47
36	34	12	6	13	11	71	44
33	32	16	9	13	13	72	45
37	33	12	10	12	17	68	44
34	33	14	11	12	15	67	43
35	37	16	12	9	21	75	43
31	32	14	8	9	18	62	40
37	34	13	11	15	15	67	41
35	30	4	3	10	8	83	52
27	30	15	11	14	12	64	38
34	38	11	12	15	12	68	41
40	36	11	7	7	22	62	39
29	32	14	9	14	12	72	43




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=0

As an alternative you can also use a 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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 29.9485992087434 -0.040302950174707Connected[t] + 0.00601675289846049Separate[t] -0.0846506179943029Learning[t] -0.023488668406038Software[t] -0.706983475750362Happiness[t] -0.146225555870818Belonging[t] + 0.142997159492401Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  29.9485992087434 -0.040302950174707Connected[t] +  0.00601675289846049Separate[t] -0.0846506179943029Learning[t] -0.023488668406038Software[t] -0.706983475750362Happiness[t] -0.146225555870818Belonging[t] +  0.142997159492401Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  29.9485992087434 -0.040302950174707Connected[t] +  0.00601675289846049Separate[t] -0.0846506179943029Learning[t] -0.023488668406038Software[t] -0.706983475750362Happiness[t] -0.146225555870818Belonging[t] +  0.142997159492401Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 29.9485992087434 -0.040302950174707Connected[t] + 0.00601675289846049Separate[t] -0.0846506179943029Learning[t] -0.023488668406038Software[t] -0.706983475750362Happiness[t] -0.146225555870818Belonging[t] + 0.142997159492401Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.94859920874342.12635814.084500
Connected-0.0403029501747070.051123-0.78840.4312180.215609
Separate0.006016752898460490.0524070.11480.9086870.454343
Learning-0.08465061799430290.091482-0.92530.355670.177835
Software-0.0234886684060380.094201-0.24930.8032930.401647
Happiness-0.7069834757503620.073153-9.664500
Belonging-0.1462255558708180.054745-2.6710.0080470.004023
Belonging_Final0.1429971594924010.0822911.73770.0834680.041734

\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) & 29.9485992087434 & 2.126358 & 14.0845 & 0 & 0 \tabularnewline
Connected & -0.040302950174707 & 0.051123 & -0.7884 & 0.431218 & 0.215609 \tabularnewline
Separate & 0.00601675289846049 & 0.052407 & 0.1148 & 0.908687 & 0.454343 \tabularnewline
Learning & -0.0846506179943029 & 0.091482 & -0.9253 & 0.35567 & 0.177835 \tabularnewline
Software & -0.023488668406038 & 0.094201 & -0.2493 & 0.803293 & 0.401647 \tabularnewline
Happiness & -0.706983475750362 & 0.073153 & -9.6645 & 0 & 0 \tabularnewline
Belonging & -0.146225555870818 & 0.054745 & -2.671 & 0.008047 & 0.004023 \tabularnewline
Belonging_Final & 0.142997159492401 & 0.082291 & 1.7377 & 0.083468 & 0.041734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&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]29.9485992087434[/C][C]2.126358[/C][C]14.0845[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]-0.040302950174707[/C][C]0.051123[/C][C]-0.7884[/C][C]0.431218[/C][C]0.215609[/C][/ROW]
[ROW][C]Separate[/C][C]0.00601675289846049[/C][C]0.052407[/C][C]0.1148[/C][C]0.908687[/C][C]0.454343[/C][/ROW]
[ROW][C]Learning[/C][C]-0.0846506179943029[/C][C]0.091482[/C][C]-0.9253[/C][C]0.35567[/C][C]0.177835[/C][/ROW]
[ROW][C]Software[/C][C]-0.023488668406038[/C][C]0.094201[/C][C]-0.2493[/C][C]0.803293[/C][C]0.401647[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.706983475750362[/C][C]0.073153[/C][C]-9.6645[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.146225555870818[/C][C]0.054745[/C][C]-2.671[/C][C]0.008047[/C][C]0.004023[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.142997159492401[/C][C]0.082291[/C][C]1.7377[/C][C]0.083468[/C][C]0.041734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=2

As an alternative you can also use a 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)29.94859920874342.12635814.084500
Connected-0.0403029501747070.051123-0.78840.4312180.215609
Separate0.006016752898460490.0524070.11480.9086870.454343
Learning-0.08465061799430290.091482-0.92530.355670.177835
Software-0.0234886684060380.094201-0.24930.8032930.401647
Happiness-0.7069834757503620.073153-9.664500
Belonging-0.1462255558708180.054745-2.6710.0080470.004023
Belonging_Final0.1429971594924010.0822911.73770.0834680.041734







Multiple Linear Regression - Regression Statistics
Multiple R0.618652608318162
R-squared0.382731049778866
Adjusted R-squared0.365852601921257
F-TEST (value)22.6757254581512
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.76292760702526
Sum Squared Residuals1954.24485418556

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.618652608318162 \tabularnewline
R-squared & 0.382731049778866 \tabularnewline
Adjusted R-squared & 0.365852601921257 \tabularnewline
F-TEST (value) & 22.6757254581512 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.76292760702526 \tabularnewline
Sum Squared Residuals & 1954.24485418556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.618652608318162[/C][/ROW]
[ROW][C]R-squared[/C][C]0.382731049778866[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.365852601921257[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.6757254581512[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/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]2.76292760702526[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1954.24485418556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.618652608318162
R-squared0.382731049778866
Adjusted R-squared0.365852601921257
F-TEST (value)22.6757254581512
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.76292760702526
Sum Squared Residuals1954.24485418556







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.0706787890202-2.07067878902023
2119.386966437633231.61303356236677
31415.5675810649187-1.56758106491873
41215.0690389054651-3.06903890546507
52111.463448897319.53655110268996
6129.96670764341012.0332923565899
72214.31624950857827.6837504914218
81112.6073490645059-1.60734906450589
91012.2507355875693-2.25073558756933
101312.2078414905570.792158509443016
111010.4506326835643-0.450632683564338
12810.4560288991884-2.45602889918836
131516.1303332426016-1.13033324260164
141412.50362615591521.4963738440848
15109.181354707056840.818645292943156
161414.0748811987288-0.0748811987287721
171412.75576996757131.24423003242873
18119.506125939688921.49387406031108
191013.4300135886584-3.43001358865835
201312.71647243220610.283527567793898
219.510.7309666966345-1.23096669663452
221415.8211721695944-1.82117216959437
231213.6166639166209-1.61666391662094
241415.6977202383925-1.6977202383925
25119.690131118863191.30986888113681
26915.4042718938735-6.40427189387354
271110.8429827819160.157017218083998
281513.5471640846961.45283591530395
291411.60540747282472.39459252717534
301316.0748009535387-3.07480095353867
31911.3311845358491-2.33118453584906
321515.4811799450673-0.481179945067336
331010.9720583597398-0.972058359739787
341111.9987926344505-0.998792634450494
351312.57924972069960.420750279300397
36812.4446557288483-4.44465572884833
372017.62426239753572.37573760246428
381212.3913844304001-0.391384430400062
391010.8230444242719-0.823044424271949
401013.8727843851482-3.8727843851482
41912.0700584795227-3.07005847952271
421411.32310179100822.67689820899176
43812.0752714430978-4.07527144309778
441415.6619347922747-1.66193479227465
451112.4771260546197-1.47712605461966
461315.4546859321419-2.45468593214188
47913.2007437142603-4.20074371426028
481112.3012373392283-1.30123733922831
491510.28001484870424.71998515129578
501113.4198208241544-2.41982082415444
511011.6561895506617-1.65618955066173
521413.4444175299720.555582470027986
531815.86165286010112.13834713989892
541416.1752560994128-2.17525609941278
551115.424028004785-4.42402800478503
5614.512.3449078550742.15509214492598
571311.24752527240781.75247472759222
58912.9521256625237-3.95212566252374
591014.1364691048419-4.13646910484192
601514.58255621741860.417443782581397
612018.89783852648561.10216147351436
621212.9701019822211-0.970101982221123
631214.4946349613718-2.49463496137181
641414.3977244788636-0.397724478863642
651313.3465184438438-0.346518443843839
661115.2551842026485-4.25518420264852
671715.43390265027081.56609734972917
681213.824492807011-1.82449280701102
691312.93671273565830.063287264341692
701412.43590710842921.56409289157076
711314.1962687930734-1.19626879307343
721512.99535099330152.00464900669853
731311.37524127810481.62475872189522
741011.8367923777135-1.83679237771348
751112.780603418997-1.78060341899701
761913.73184285378395.2681571462161
771310.91461719179462.08538280820535
781714.16355948010472.83644051989525
791312.48106044673980.518939553260202
80914.6155095332266-5.6155095332266
811111.6202293351416-0.620229335141621
82912.5724474751648-3.57244747516481
831211.26699627473910.733003725260945
841212.4942226380307-0.494222638030669
851312.77955465444580.220445345554208
861312.11843714816110.881562851838943
871213.2054462258336-1.20544622583363
881514.96801689911660.0319831008834403
892218.22973602480363.7702639751964
901311.57329009641051.42670990358946
911514.53077962468120.469220375318806
921311.86215831102611.13784168897386
931512.25135000724572.74864999275434
9412.513.4100310275549-0.910031027554902
951110.71517709877670.284822901223324
961614.58372662062041.41627337937964
971112.4332147071392-1.4332147071392
981110.02656028438830.973439715611686
991012.3154875257295-2.31548752572946
1001010.2534498040753-0.253449804075338
1011614.3317960529341.66820394706604
1021210.36991490629981.63008509370023
1031115.8774150398815-4.87741503988146
1041611.80476542173544.19523457826456
1051916.70338969792692.2966103020731
1061111.2027375647298-0.202737564729814
1071612.05666896092873.94333103907132
1081516.8483856022461-1.84838560224612
1092417.78175233540316.21824766459691
1101411.70973430159832.29026569840168
1111514.90887500335430.091124996645688
1121113.0143654843153-2.01436548431534
1131513.61725323322971.38274676677026
1141210.29328723728891.70671276271107
1151010.512578368055-0.512578368055028
1161414.2937682134376-0.293768213437622
1171314.0429451333889-1.04294513338889
118913.488903501121-4.48890350112104
1191511.80419138087113.19580861912894
1201515.8213128610493-0.821312861049344
1211412.76885593436171.23114406563833
1221111.3724384141774-0.372438414177408
123812.0057475305325-4.00574753053249
1241112.264754845446-1.26475484544597
1251113.1692830825707-2.16928308257066
12689.7504989466578-1.7504989466578
1271010.1649517011955-0.164951701195522
128119.655941060057761.34405893994224
1291312.6212488885910.378751111409003
1301113.7012421239307-2.70124212393068
1312017.61391957466162.38608042533843
1321011.7305312221829-1.73053122218292
1331513.37721248441941.62278751558061
1341212.3714800555824-0.371480055582409
1351411.11970893849292.88029106150705
1362316.68653713100846.31346286899159
1371414.1566526501826-0.156652650182635
1381617.1159559049064-1.11595590490635
1391113.2955474557045-2.29554745570451
1401214.3497742905291-2.3497742905291
1411013.5391292765128-3.53912927651279
1421411.17914610413422.82085389586583
1431212.2643895106563-0.264389510656309
1441211.93855722795870.0614427720412801
1451111.0276187019894-0.0276187019894091
1461211.288281956190.71171804380997
1471316.2155145272082-3.21551452720818
1481114.3363011715584-3.33630117155838
1491916.95922847212652.04077152787353
1501211.4787551984310.521244801568986
1511712.88164559704914.11835440295089
152911.4102610697361-2.41026106973612
1531214.5571407032858-2.55714070328582
1541916.94510192281712.05489807718293
1551814.46718225707283.53281774292717
1561514.53077962468120.469220375318806
1571413.80168045861530.19831954138474
158119.655941060057761.34405893994224
159912.9771730113877-3.97717301138775
1601814.06693422258123.93306577741879
1611614.80405542278291.19594457721712
1622417.10966682459556.89033317540452
1631412.63075437560891.36924562439108
1642010.69536486537319.30463513462688
1651815.8591093898392.140890610161
1662317.17332347728395.82667652271613
1671213.055081181971-1.05508118197098
1681414.8255026820734-0.825502682073391
1691616.2211245696061-0.221124569606067
1701816.27233218135341.7276678186466
1712016.55333825076223.44666174923777
1721211.53593833387650.46406166612348
1731216.2084923595002-4.20849235950018
1741715.19076383644841.80923616355164
1751311.82039002976761.17960997023241
176913.0368530072049-4.03685300720495
1771617.2949149119527-1.29491491195274
1781814.98232633396763.01767366603244
1791012.2197674410369-2.21976744103686
1801414.8208728587478-0.820872858747804
1811114.0029797837371-3.00297978373709
182914.5213958422977-5.52139584229772
1831112.4064530601426-1.40645306014257
1841012.3260504295329-2.32605042953289
1851111.8103681088032-0.810368108803207
1861913.22718158544475.7728184145553
1871412.43524480585961.56475519414044
1881211.49255219359630.507447806403657
1891415.0959577912219-1.09595779122193
1902116.70721984635954.29278015364053
1911315.6481146327759-2.64811463277587
1921012.4486729761108-2.44867297611076
1931513.08274943499111.91725056500893
1941616.16791141144-0.167911411440049
1951412.67497903662841.32502096337161
1961214.7066877693377-2.70668776933766
1971912.61328642042796.38671357957211
1981512.16440523327072.83559476672929
1991917.98969066227781.01030933772221
2001313.2987067190283-0.298706719028284
2011717.1231997625005-0.123199762500526
2021212.7674065438777-0.767406543877671
2031110.64250834967720.357491650322818
2041415.391098939205-1.39109893920504
2051112.3813885335817-1.38138853358167
2061312.27619629122890.723803708771058
2071212.6857334320538-0.685733432053771
2081512.63577257516792.36422742483209
2091414.395503288059-0.395503288058991
2101210.82738146394311.17261853605694
2111717.4777798338588-0.477779833858776
2121111.2474110902512-0.247411090251218
2131814.58882856238623.41117143761383
2141315.8595853229194-2.85958532291937
2151715.25826429839061.7417357016094
2161313.3040103616622-0.304010361662213
2171110.11971330288370.88028669711633
2181212.5627209103186-0.562720910318637
2192217.99218300032934.00781699967066
2201412.02132765064911.97867234935086
2211215.0421356006936-3.04213560069364
2221211.85378896221960.146211037780439
2231715.95331489240481.04668510759519
224912.8234212732034-3.82342127320344
2252117.13659230713963.8634076928604
2261011.9179113198274-1.91791131982743
2271110.86178871706570.138211282934347
2281214.5925564778268-2.59255647782678
2292318.33342314006474.66657685993528
2301315.6979505366254-2.69795053662538
2311213.386506176235-1.38650617623499
2321617.8977570916836-1.89775709168362
233913.4301958683188-4.43019586831879
2341713.50102274484433.49897725515565
235911.8873896605286-2.88738966052862
2361415.6544395742704-1.65443957427039
2371715.7112721762531.28872782374696
2381315.4946665608675-2.49466656086749
2391115.8808621627185-4.88086216271848
2401215.6137825421604-3.61378254216041
2411014.0764377110579-4.07643771105792
2421918.9304806912620.0695193087380221
2431616.1116301325695-0.111630132569475
2441615.08035673713330.919643262866729
2451412.12286517359121.87713482640878
2462015.92618235228344.07381764771663
2471514.48773936122710.512260638772898
2482315.44754940606587.55245059393425
2492017.99491005856862.00508994143142
2501616.051525496072-0.0515254960719899
2511412.97353952656361.02646047343643
2521713.92469904040773.07530095959227
2531114.2645985407165-3.26459854071648
2541313.9611770118699-0.961177011869939
2551715.2699843073821.73001569261802
2561515.2013316498899-0.201331649889872
2572115.94345178719895.05654821280109
2581817.80977648086160.190223519138384
2591512.76414542402262.23585457597737
260816.5387264612104-8.53872646121037
2611213.6904753430729-1.69047534307286
2621212.9087082978523-0.908708297852339
2632219.01952725528042.98047274471963
2641213.2987122538222-1.29871225382221

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.0706787890202 & -2.07067878902023 \tabularnewline
2 & 11 & 9.38696643763323 & 1.61303356236677 \tabularnewline
3 & 14 & 15.5675810649187 & -1.56758106491873 \tabularnewline
4 & 12 & 15.0690389054651 & -3.06903890546507 \tabularnewline
5 & 21 & 11.46344889731 & 9.53655110268996 \tabularnewline
6 & 12 & 9.9667076434101 & 2.0332923565899 \tabularnewline
7 & 22 & 14.3162495085782 & 7.6837504914218 \tabularnewline
8 & 11 & 12.6073490645059 & -1.60734906450589 \tabularnewline
9 & 10 & 12.2507355875693 & -2.25073558756933 \tabularnewline
10 & 13 & 12.207841490557 & 0.792158509443016 \tabularnewline
11 & 10 & 10.4506326835643 & -0.450632683564338 \tabularnewline
12 & 8 & 10.4560288991884 & -2.45602889918836 \tabularnewline
13 & 15 & 16.1303332426016 & -1.13033324260164 \tabularnewline
14 & 14 & 12.5036261559152 & 1.4963738440848 \tabularnewline
15 & 10 & 9.18135470705684 & 0.818645292943156 \tabularnewline
16 & 14 & 14.0748811987288 & -0.0748811987287721 \tabularnewline
17 & 14 & 12.7557699675713 & 1.24423003242873 \tabularnewline
18 & 11 & 9.50612593968892 & 1.49387406031108 \tabularnewline
19 & 10 & 13.4300135886584 & -3.43001358865835 \tabularnewline
20 & 13 & 12.7164724322061 & 0.283527567793898 \tabularnewline
21 & 9.5 & 10.7309666966345 & -1.23096669663452 \tabularnewline
22 & 14 & 15.8211721695944 & -1.82117216959437 \tabularnewline
23 & 12 & 13.6166639166209 & -1.61666391662094 \tabularnewline
24 & 14 & 15.6977202383925 & -1.6977202383925 \tabularnewline
25 & 11 & 9.69013111886319 & 1.30986888113681 \tabularnewline
26 & 9 & 15.4042718938735 & -6.40427189387354 \tabularnewline
27 & 11 & 10.842982781916 & 0.157017218083998 \tabularnewline
28 & 15 & 13.547164084696 & 1.45283591530395 \tabularnewline
29 & 14 & 11.6054074728247 & 2.39459252717534 \tabularnewline
30 & 13 & 16.0748009535387 & -3.07480095353867 \tabularnewline
31 & 9 & 11.3311845358491 & -2.33118453584906 \tabularnewline
32 & 15 & 15.4811799450673 & -0.481179945067336 \tabularnewline
33 & 10 & 10.9720583597398 & -0.972058359739787 \tabularnewline
34 & 11 & 11.9987926344505 & -0.998792634450494 \tabularnewline
35 & 13 & 12.5792497206996 & 0.420750279300397 \tabularnewline
36 & 8 & 12.4446557288483 & -4.44465572884833 \tabularnewline
37 & 20 & 17.6242623975357 & 2.37573760246428 \tabularnewline
38 & 12 & 12.3913844304001 & -0.391384430400062 \tabularnewline
39 & 10 & 10.8230444242719 & -0.823044424271949 \tabularnewline
40 & 10 & 13.8727843851482 & -3.8727843851482 \tabularnewline
41 & 9 & 12.0700584795227 & -3.07005847952271 \tabularnewline
42 & 14 & 11.3231017910082 & 2.67689820899176 \tabularnewline
43 & 8 & 12.0752714430978 & -4.07527144309778 \tabularnewline
44 & 14 & 15.6619347922747 & -1.66193479227465 \tabularnewline
45 & 11 & 12.4771260546197 & -1.47712605461966 \tabularnewline
46 & 13 & 15.4546859321419 & -2.45468593214188 \tabularnewline
47 & 9 & 13.2007437142603 & -4.20074371426028 \tabularnewline
48 & 11 & 12.3012373392283 & -1.30123733922831 \tabularnewline
49 & 15 & 10.2800148487042 & 4.71998515129578 \tabularnewline
50 & 11 & 13.4198208241544 & -2.41982082415444 \tabularnewline
51 & 10 & 11.6561895506617 & -1.65618955066173 \tabularnewline
52 & 14 & 13.444417529972 & 0.555582470027986 \tabularnewline
53 & 18 & 15.8616528601011 & 2.13834713989892 \tabularnewline
54 & 14 & 16.1752560994128 & -2.17525609941278 \tabularnewline
55 & 11 & 15.424028004785 & -4.42402800478503 \tabularnewline
56 & 14.5 & 12.344907855074 & 2.15509214492598 \tabularnewline
57 & 13 & 11.2475252724078 & 1.75247472759222 \tabularnewline
58 & 9 & 12.9521256625237 & -3.95212566252374 \tabularnewline
59 & 10 & 14.1364691048419 & -4.13646910484192 \tabularnewline
60 & 15 & 14.5825562174186 & 0.417443782581397 \tabularnewline
61 & 20 & 18.8978385264856 & 1.10216147351436 \tabularnewline
62 & 12 & 12.9701019822211 & -0.970101982221123 \tabularnewline
63 & 12 & 14.4946349613718 & -2.49463496137181 \tabularnewline
64 & 14 & 14.3977244788636 & -0.397724478863642 \tabularnewline
65 & 13 & 13.3465184438438 & -0.346518443843839 \tabularnewline
66 & 11 & 15.2551842026485 & -4.25518420264852 \tabularnewline
67 & 17 & 15.4339026502708 & 1.56609734972917 \tabularnewline
68 & 12 & 13.824492807011 & -1.82449280701102 \tabularnewline
69 & 13 & 12.9367127356583 & 0.063287264341692 \tabularnewline
70 & 14 & 12.4359071084292 & 1.56409289157076 \tabularnewline
71 & 13 & 14.1962687930734 & -1.19626879307343 \tabularnewline
72 & 15 & 12.9953509933015 & 2.00464900669853 \tabularnewline
73 & 13 & 11.3752412781048 & 1.62475872189522 \tabularnewline
74 & 10 & 11.8367923777135 & -1.83679237771348 \tabularnewline
75 & 11 & 12.780603418997 & -1.78060341899701 \tabularnewline
76 & 19 & 13.7318428537839 & 5.2681571462161 \tabularnewline
77 & 13 & 10.9146171917946 & 2.08538280820535 \tabularnewline
78 & 17 & 14.1635594801047 & 2.83644051989525 \tabularnewline
79 & 13 & 12.4810604467398 & 0.518939553260202 \tabularnewline
80 & 9 & 14.6155095332266 & -5.6155095332266 \tabularnewline
81 & 11 & 11.6202293351416 & -0.620229335141621 \tabularnewline
82 & 9 & 12.5724474751648 & -3.57244747516481 \tabularnewline
83 & 12 & 11.2669962747391 & 0.733003725260945 \tabularnewline
84 & 12 & 12.4942226380307 & -0.494222638030669 \tabularnewline
85 & 13 & 12.7795546544458 & 0.220445345554208 \tabularnewline
86 & 13 & 12.1184371481611 & 0.881562851838943 \tabularnewline
87 & 12 & 13.2054462258336 & -1.20544622583363 \tabularnewline
88 & 15 & 14.9680168991166 & 0.0319831008834403 \tabularnewline
89 & 22 & 18.2297360248036 & 3.7702639751964 \tabularnewline
90 & 13 & 11.5732900964105 & 1.42670990358946 \tabularnewline
91 & 15 & 14.5307796246812 & 0.469220375318806 \tabularnewline
92 & 13 & 11.8621583110261 & 1.13784168897386 \tabularnewline
93 & 15 & 12.2513500072457 & 2.74864999275434 \tabularnewline
94 & 12.5 & 13.4100310275549 & -0.910031027554902 \tabularnewline
95 & 11 & 10.7151770987767 & 0.284822901223324 \tabularnewline
96 & 16 & 14.5837266206204 & 1.41627337937964 \tabularnewline
97 & 11 & 12.4332147071392 & -1.4332147071392 \tabularnewline
98 & 11 & 10.0265602843883 & 0.973439715611686 \tabularnewline
99 & 10 & 12.3154875257295 & -2.31548752572946 \tabularnewline
100 & 10 & 10.2534498040753 & -0.253449804075338 \tabularnewline
101 & 16 & 14.331796052934 & 1.66820394706604 \tabularnewline
102 & 12 & 10.3699149062998 & 1.63008509370023 \tabularnewline
103 & 11 & 15.8774150398815 & -4.87741503988146 \tabularnewline
104 & 16 & 11.8047654217354 & 4.19523457826456 \tabularnewline
105 & 19 & 16.7033896979269 & 2.2966103020731 \tabularnewline
106 & 11 & 11.2027375647298 & -0.202737564729814 \tabularnewline
107 & 16 & 12.0566689609287 & 3.94333103907132 \tabularnewline
108 & 15 & 16.8483856022461 & -1.84838560224612 \tabularnewline
109 & 24 & 17.7817523354031 & 6.21824766459691 \tabularnewline
110 & 14 & 11.7097343015983 & 2.29026569840168 \tabularnewline
111 & 15 & 14.9088750033543 & 0.091124996645688 \tabularnewline
112 & 11 & 13.0143654843153 & -2.01436548431534 \tabularnewline
113 & 15 & 13.6172532332297 & 1.38274676677026 \tabularnewline
114 & 12 & 10.2932872372889 & 1.70671276271107 \tabularnewline
115 & 10 & 10.512578368055 & -0.512578368055028 \tabularnewline
116 & 14 & 14.2937682134376 & -0.293768213437622 \tabularnewline
117 & 13 & 14.0429451333889 & -1.04294513338889 \tabularnewline
118 & 9 & 13.488903501121 & -4.48890350112104 \tabularnewline
119 & 15 & 11.8041913808711 & 3.19580861912894 \tabularnewline
120 & 15 & 15.8213128610493 & -0.821312861049344 \tabularnewline
121 & 14 & 12.7688559343617 & 1.23114406563833 \tabularnewline
122 & 11 & 11.3724384141774 & -0.372438414177408 \tabularnewline
123 & 8 & 12.0057475305325 & -4.00574753053249 \tabularnewline
124 & 11 & 12.264754845446 & -1.26475484544597 \tabularnewline
125 & 11 & 13.1692830825707 & -2.16928308257066 \tabularnewline
126 & 8 & 9.7504989466578 & -1.7504989466578 \tabularnewline
127 & 10 & 10.1649517011955 & -0.164951701195522 \tabularnewline
128 & 11 & 9.65594106005776 & 1.34405893994224 \tabularnewline
129 & 13 & 12.621248888591 & 0.378751111409003 \tabularnewline
130 & 11 & 13.7012421239307 & -2.70124212393068 \tabularnewline
131 & 20 & 17.6139195746616 & 2.38608042533843 \tabularnewline
132 & 10 & 11.7305312221829 & -1.73053122218292 \tabularnewline
133 & 15 & 13.3772124844194 & 1.62278751558061 \tabularnewline
134 & 12 & 12.3714800555824 & -0.371480055582409 \tabularnewline
135 & 14 & 11.1197089384929 & 2.88029106150705 \tabularnewline
136 & 23 & 16.6865371310084 & 6.31346286899159 \tabularnewline
137 & 14 & 14.1566526501826 & -0.156652650182635 \tabularnewline
138 & 16 & 17.1159559049064 & -1.11595590490635 \tabularnewline
139 & 11 & 13.2955474557045 & -2.29554745570451 \tabularnewline
140 & 12 & 14.3497742905291 & -2.3497742905291 \tabularnewline
141 & 10 & 13.5391292765128 & -3.53912927651279 \tabularnewline
142 & 14 & 11.1791461041342 & 2.82085389586583 \tabularnewline
143 & 12 & 12.2643895106563 & -0.264389510656309 \tabularnewline
144 & 12 & 11.9385572279587 & 0.0614427720412801 \tabularnewline
145 & 11 & 11.0276187019894 & -0.0276187019894091 \tabularnewline
146 & 12 & 11.28828195619 & 0.71171804380997 \tabularnewline
147 & 13 & 16.2155145272082 & -3.21551452720818 \tabularnewline
148 & 11 & 14.3363011715584 & -3.33630117155838 \tabularnewline
149 & 19 & 16.9592284721265 & 2.04077152787353 \tabularnewline
150 & 12 & 11.478755198431 & 0.521244801568986 \tabularnewline
151 & 17 & 12.8816455970491 & 4.11835440295089 \tabularnewline
152 & 9 & 11.4102610697361 & -2.41026106973612 \tabularnewline
153 & 12 & 14.5571407032858 & -2.55714070328582 \tabularnewline
154 & 19 & 16.9451019228171 & 2.05489807718293 \tabularnewline
155 & 18 & 14.4671822570728 & 3.53281774292717 \tabularnewline
156 & 15 & 14.5307796246812 & 0.469220375318806 \tabularnewline
157 & 14 & 13.8016804586153 & 0.19831954138474 \tabularnewline
158 & 11 & 9.65594106005776 & 1.34405893994224 \tabularnewline
159 & 9 & 12.9771730113877 & -3.97717301138775 \tabularnewline
160 & 18 & 14.0669342225812 & 3.93306577741879 \tabularnewline
161 & 16 & 14.8040554227829 & 1.19594457721712 \tabularnewline
162 & 24 & 17.1096668245955 & 6.89033317540452 \tabularnewline
163 & 14 & 12.6307543756089 & 1.36924562439108 \tabularnewline
164 & 20 & 10.6953648653731 & 9.30463513462688 \tabularnewline
165 & 18 & 15.859109389839 & 2.140890610161 \tabularnewline
166 & 23 & 17.1733234772839 & 5.82667652271613 \tabularnewline
167 & 12 & 13.055081181971 & -1.05508118197098 \tabularnewline
168 & 14 & 14.8255026820734 & -0.825502682073391 \tabularnewline
169 & 16 & 16.2211245696061 & -0.221124569606067 \tabularnewline
170 & 18 & 16.2723321813534 & 1.7276678186466 \tabularnewline
171 & 20 & 16.5533382507622 & 3.44666174923777 \tabularnewline
172 & 12 & 11.5359383338765 & 0.46406166612348 \tabularnewline
173 & 12 & 16.2084923595002 & -4.20849235950018 \tabularnewline
174 & 17 & 15.1907638364484 & 1.80923616355164 \tabularnewline
175 & 13 & 11.8203900297676 & 1.17960997023241 \tabularnewline
176 & 9 & 13.0368530072049 & -4.03685300720495 \tabularnewline
177 & 16 & 17.2949149119527 & -1.29491491195274 \tabularnewline
178 & 18 & 14.9823263339676 & 3.01767366603244 \tabularnewline
179 & 10 & 12.2197674410369 & -2.21976744103686 \tabularnewline
180 & 14 & 14.8208728587478 & -0.820872858747804 \tabularnewline
181 & 11 & 14.0029797837371 & -3.00297978373709 \tabularnewline
182 & 9 & 14.5213958422977 & -5.52139584229772 \tabularnewline
183 & 11 & 12.4064530601426 & -1.40645306014257 \tabularnewline
184 & 10 & 12.3260504295329 & -2.32605042953289 \tabularnewline
185 & 11 & 11.8103681088032 & -0.810368108803207 \tabularnewline
186 & 19 & 13.2271815854447 & 5.7728184145553 \tabularnewline
187 & 14 & 12.4352448058596 & 1.56475519414044 \tabularnewline
188 & 12 & 11.4925521935963 & 0.507447806403657 \tabularnewline
189 & 14 & 15.0959577912219 & -1.09595779122193 \tabularnewline
190 & 21 & 16.7072198463595 & 4.29278015364053 \tabularnewline
191 & 13 & 15.6481146327759 & -2.64811463277587 \tabularnewline
192 & 10 & 12.4486729761108 & -2.44867297611076 \tabularnewline
193 & 15 & 13.0827494349911 & 1.91725056500893 \tabularnewline
194 & 16 & 16.16791141144 & -0.167911411440049 \tabularnewline
195 & 14 & 12.6749790366284 & 1.32502096337161 \tabularnewline
196 & 12 & 14.7066877693377 & -2.70668776933766 \tabularnewline
197 & 19 & 12.6132864204279 & 6.38671357957211 \tabularnewline
198 & 15 & 12.1644052332707 & 2.83559476672929 \tabularnewline
199 & 19 & 17.9896906622778 & 1.01030933772221 \tabularnewline
200 & 13 & 13.2987067190283 & -0.298706719028284 \tabularnewline
201 & 17 & 17.1231997625005 & -0.123199762500526 \tabularnewline
202 & 12 & 12.7674065438777 & -0.767406543877671 \tabularnewline
203 & 11 & 10.6425083496772 & 0.357491650322818 \tabularnewline
204 & 14 & 15.391098939205 & -1.39109893920504 \tabularnewline
205 & 11 & 12.3813885335817 & -1.38138853358167 \tabularnewline
206 & 13 & 12.2761962912289 & 0.723803708771058 \tabularnewline
207 & 12 & 12.6857334320538 & -0.685733432053771 \tabularnewline
208 & 15 & 12.6357725751679 & 2.36422742483209 \tabularnewline
209 & 14 & 14.395503288059 & -0.395503288058991 \tabularnewline
210 & 12 & 10.8273814639431 & 1.17261853605694 \tabularnewline
211 & 17 & 17.4777798338588 & -0.477779833858776 \tabularnewline
212 & 11 & 11.2474110902512 & -0.247411090251218 \tabularnewline
213 & 18 & 14.5888285623862 & 3.41117143761383 \tabularnewline
214 & 13 & 15.8595853229194 & -2.85958532291937 \tabularnewline
215 & 17 & 15.2582642983906 & 1.7417357016094 \tabularnewline
216 & 13 & 13.3040103616622 & -0.304010361662213 \tabularnewline
217 & 11 & 10.1197133028837 & 0.88028669711633 \tabularnewline
218 & 12 & 12.5627209103186 & -0.562720910318637 \tabularnewline
219 & 22 & 17.9921830003293 & 4.00781699967066 \tabularnewline
220 & 14 & 12.0213276506491 & 1.97867234935086 \tabularnewline
221 & 12 & 15.0421356006936 & -3.04213560069364 \tabularnewline
222 & 12 & 11.8537889622196 & 0.146211037780439 \tabularnewline
223 & 17 & 15.9533148924048 & 1.04668510759519 \tabularnewline
224 & 9 & 12.8234212732034 & -3.82342127320344 \tabularnewline
225 & 21 & 17.1365923071396 & 3.8634076928604 \tabularnewline
226 & 10 & 11.9179113198274 & -1.91791131982743 \tabularnewline
227 & 11 & 10.8617887170657 & 0.138211282934347 \tabularnewline
228 & 12 & 14.5925564778268 & -2.59255647782678 \tabularnewline
229 & 23 & 18.3334231400647 & 4.66657685993528 \tabularnewline
230 & 13 & 15.6979505366254 & -2.69795053662538 \tabularnewline
231 & 12 & 13.386506176235 & -1.38650617623499 \tabularnewline
232 & 16 & 17.8977570916836 & -1.89775709168362 \tabularnewline
233 & 9 & 13.4301958683188 & -4.43019586831879 \tabularnewline
234 & 17 & 13.5010227448443 & 3.49897725515565 \tabularnewline
235 & 9 & 11.8873896605286 & -2.88738966052862 \tabularnewline
236 & 14 & 15.6544395742704 & -1.65443957427039 \tabularnewline
237 & 17 & 15.711272176253 & 1.28872782374696 \tabularnewline
238 & 13 & 15.4946665608675 & -2.49466656086749 \tabularnewline
239 & 11 & 15.8808621627185 & -4.88086216271848 \tabularnewline
240 & 12 & 15.6137825421604 & -3.61378254216041 \tabularnewline
241 & 10 & 14.0764377110579 & -4.07643771105792 \tabularnewline
242 & 19 & 18.930480691262 & 0.0695193087380221 \tabularnewline
243 & 16 & 16.1116301325695 & -0.111630132569475 \tabularnewline
244 & 16 & 15.0803567371333 & 0.919643262866729 \tabularnewline
245 & 14 & 12.1228651735912 & 1.87713482640878 \tabularnewline
246 & 20 & 15.9261823522834 & 4.07381764771663 \tabularnewline
247 & 15 & 14.4877393612271 & 0.512260638772898 \tabularnewline
248 & 23 & 15.4475494060658 & 7.55245059393425 \tabularnewline
249 & 20 & 17.9949100585686 & 2.00508994143142 \tabularnewline
250 & 16 & 16.051525496072 & -0.0515254960719899 \tabularnewline
251 & 14 & 12.9735395265636 & 1.02646047343643 \tabularnewline
252 & 17 & 13.9246990404077 & 3.07530095959227 \tabularnewline
253 & 11 & 14.2645985407165 & -3.26459854071648 \tabularnewline
254 & 13 & 13.9611770118699 & -0.961177011869939 \tabularnewline
255 & 17 & 15.269984307382 & 1.73001569261802 \tabularnewline
256 & 15 & 15.2013316498899 & -0.201331649889872 \tabularnewline
257 & 21 & 15.9434517871989 & 5.05654821280109 \tabularnewline
258 & 18 & 17.8097764808616 & 0.190223519138384 \tabularnewline
259 & 15 & 12.7641454240226 & 2.23585457597737 \tabularnewline
260 & 8 & 16.5387264612104 & -8.53872646121037 \tabularnewline
261 & 12 & 13.6904753430729 & -1.69047534307286 \tabularnewline
262 & 12 & 12.9087082978523 & -0.908708297852339 \tabularnewline
263 & 22 & 19.0195272552804 & 2.98047274471963 \tabularnewline
264 & 12 & 13.2987122538222 & -1.29871225382221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&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]14.0706787890202[/C][C]-2.07067878902023[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.38696643763323[/C][C]1.61303356236677[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.5675810649187[/C][C]-1.56758106491873[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]15.0690389054651[/C][C]-3.06903890546507[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.46344889731[/C][C]9.53655110268996[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.9667076434101[/C][C]2.0332923565899[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]14.3162495085782[/C][C]7.6837504914218[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.6073490645059[/C][C]-1.60734906450589[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.2507355875693[/C][C]-2.25073558756933[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.207841490557[/C][C]0.792158509443016[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.4506326835643[/C][C]-0.450632683564338[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.4560288991884[/C][C]-2.45602889918836[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]16.1303332426016[/C][C]-1.13033324260164[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.5036261559152[/C][C]1.4963738440848[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.18135470705684[/C][C]0.818645292943156[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.0748811987288[/C][C]-0.0748811987287721[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.7557699675713[/C][C]1.24423003242873[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.50612593968892[/C][C]1.49387406031108[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]13.4300135886584[/C][C]-3.43001358865835[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.7164724322061[/C][C]0.283527567793898[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]10.7309666966345[/C][C]-1.23096669663452[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.8211721695944[/C][C]-1.82117216959437[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.6166639166209[/C][C]-1.61666391662094[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]15.6977202383925[/C][C]-1.6977202383925[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.69013111886319[/C][C]1.30986888113681[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.4042718938735[/C][C]-6.40427189387354[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.842982781916[/C][C]0.157017218083998[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.547164084696[/C][C]1.45283591530395[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.6054074728247[/C][C]2.39459252717534[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]16.0748009535387[/C][C]-3.07480095353867[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.3311845358491[/C][C]-2.33118453584906[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]15.4811799450673[/C][C]-0.481179945067336[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.9720583597398[/C][C]-0.972058359739787[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.9987926344505[/C][C]-0.998792634450494[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.5792497206996[/C][C]0.420750279300397[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.4446557288483[/C][C]-4.44465572884833[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.6242623975357[/C][C]2.37573760246428[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3913844304001[/C][C]-0.391384430400062[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.8230444242719[/C][C]-0.823044424271949[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.8727843851482[/C][C]-3.8727843851482[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.0700584795227[/C][C]-3.07005847952271[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.3231017910082[/C][C]2.67689820899176[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.0752714430978[/C][C]-4.07527144309778[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.6619347922747[/C][C]-1.66193479227465[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.4771260546197[/C][C]-1.47712605461966[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.4546859321419[/C][C]-2.45468593214188[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]13.2007437142603[/C][C]-4.20074371426028[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.3012373392283[/C][C]-1.30123733922831[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.2800148487042[/C][C]4.71998515129578[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.4198208241544[/C][C]-2.41982082415444[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.6561895506617[/C][C]-1.65618955066173[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.444417529972[/C][C]0.555582470027986[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.8616528601011[/C][C]2.13834713989892[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]16.1752560994128[/C][C]-2.17525609941278[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.424028004785[/C][C]-4.42402800478503[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]12.344907855074[/C][C]2.15509214492598[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2475252724078[/C][C]1.75247472759222[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.9521256625237[/C][C]-3.95212566252374[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.1364691048419[/C][C]-4.13646910484192[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5825562174186[/C][C]0.417443782581397[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.8978385264856[/C][C]1.10216147351436[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9701019822211[/C][C]-0.970101982221123[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.4946349613718[/C][C]-2.49463496137181[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3977244788636[/C][C]-0.397724478863642[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.3465184438438[/C][C]-0.346518443843839[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.2551842026485[/C][C]-4.25518420264852[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.4339026502708[/C][C]1.56609734972917[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.824492807011[/C][C]-1.82449280701102[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9367127356583[/C][C]0.063287264341692[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.4359071084292[/C][C]1.56409289157076[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.1962687930734[/C][C]-1.19626879307343[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.9953509933015[/C][C]2.00464900669853[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.3752412781048[/C][C]1.62475872189522[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.8367923777135[/C][C]-1.83679237771348[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.780603418997[/C][C]-1.78060341899701[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.7318428537839[/C][C]5.2681571462161[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.9146171917946[/C][C]2.08538280820535[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.1635594801047[/C][C]2.83644051989525[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.4810604467398[/C][C]0.518939553260202[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.6155095332266[/C][C]-5.6155095332266[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.6202293351416[/C][C]-0.620229335141621[/C][/ROW]
[ROW][C]82[/C][C]9[/C][C]12.5724474751648[/C][C]-3.57244747516481[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.2669962747391[/C][C]0.733003725260945[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.4942226380307[/C][C]-0.494222638030669[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]12.7795546544458[/C][C]0.220445345554208[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.1184371481611[/C][C]0.881562851838943[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]13.2054462258336[/C][C]-1.20544622583363[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]14.9680168991166[/C][C]0.0319831008834403[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]18.2297360248036[/C][C]3.7702639751964[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]11.5732900964105[/C][C]1.42670990358946[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]14.5307796246812[/C][C]0.469220375318806[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.8621583110261[/C][C]1.13784168897386[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]12.2513500072457[/C][C]2.74864999275434[/C][/ROW]
[ROW][C]94[/C][C]12.5[/C][C]13.4100310275549[/C][C]-0.910031027554902[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.7151770987767[/C][C]0.284822901223324[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.5837266206204[/C][C]1.41627337937964[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]12.4332147071392[/C][C]-1.4332147071392[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.0265602843883[/C][C]0.973439715611686[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]12.3154875257295[/C][C]-2.31548752572946[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.2534498040753[/C][C]-0.253449804075338[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.331796052934[/C][C]1.66820394706604[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]10.3699149062998[/C][C]1.63008509370023[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]15.8774150398815[/C][C]-4.87741503988146[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]11.8047654217354[/C][C]4.19523457826456[/C][/ROW]
[ROW][C]105[/C][C]19[/C][C]16.7033896979269[/C][C]2.2966103020731[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]11.2027375647298[/C][C]-0.202737564729814[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]12.0566689609287[/C][C]3.94333103907132[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]16.8483856022461[/C][C]-1.84838560224612[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]17.7817523354031[/C][C]6.21824766459691[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]11.7097343015983[/C][C]2.29026569840168[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.9088750033543[/C][C]0.091124996645688[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.0143654843153[/C][C]-2.01436548431534[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.6172532332297[/C][C]1.38274676677026[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]10.2932872372889[/C][C]1.70671276271107[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]10.512578368055[/C][C]-0.512578368055028[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.2937682134376[/C][C]-0.293768213437622[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]14.0429451333889[/C][C]-1.04294513338889[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]13.488903501121[/C][C]-4.48890350112104[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]11.8041913808711[/C][C]3.19580861912894[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.8213128610493[/C][C]-0.821312861049344[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]12.7688559343617[/C][C]1.23114406563833[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]11.3724384141774[/C][C]-0.372438414177408[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]12.0057475305325[/C][C]-4.00574753053249[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]12.264754845446[/C][C]-1.26475484544597[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.1692830825707[/C][C]-2.16928308257066[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]9.7504989466578[/C][C]-1.7504989466578[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.1649517011955[/C][C]-0.164951701195522[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.65594106005776[/C][C]1.34405893994224[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]12.621248888591[/C][C]0.378751111409003[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]13.7012421239307[/C][C]-2.70124212393068[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]17.6139195746616[/C][C]2.38608042533843[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]11.7305312221829[/C][C]-1.73053122218292[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.3772124844194[/C][C]1.62278751558061[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]12.3714800555824[/C][C]-0.371480055582409[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]11.1197089384929[/C][C]2.88029106150705[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]16.6865371310084[/C][C]6.31346286899159[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.1566526501826[/C][C]-0.156652650182635[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]17.1159559049064[/C][C]-1.11595590490635[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2955474557045[/C][C]-2.29554745570451[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]14.3497742905291[/C][C]-2.3497742905291[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]13.5391292765128[/C][C]-3.53912927651279[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]11.1791461041342[/C][C]2.82085389586583[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]12.2643895106563[/C][C]-0.264389510656309[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.9385572279587[/C][C]0.0614427720412801[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.0276187019894[/C][C]-0.0276187019894091[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.28828195619[/C][C]0.71171804380997[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]16.2155145272082[/C][C]-3.21551452720818[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]14.3363011715584[/C][C]-3.33630117155838[/C][/ROW]
[ROW][C]149[/C][C]19[/C][C]16.9592284721265[/C][C]2.04077152787353[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.478755198431[/C][C]0.521244801568986[/C][/ROW]
[ROW][C]151[/C][C]17[/C][C]12.8816455970491[/C][C]4.11835440295089[/C][/ROW]
[ROW][C]152[/C][C]9[/C][C]11.4102610697361[/C][C]-2.41026106973612[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.5571407032858[/C][C]-2.55714070328582[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]16.9451019228171[/C][C]2.05489807718293[/C][/ROW]
[ROW][C]155[/C][C]18[/C][C]14.4671822570728[/C][C]3.53281774292717[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5307796246812[/C][C]0.469220375318806[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.8016804586153[/C][C]0.19831954138474[/C][/ROW]
[ROW][C]158[/C][C]11[/C][C]9.65594106005776[/C][C]1.34405893994224[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]12.9771730113877[/C][C]-3.97717301138775[/C][/ROW]
[ROW][C]160[/C][C]18[/C][C]14.0669342225812[/C][C]3.93306577741879[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]14.8040554227829[/C][C]1.19594457721712[/C][/ROW]
[ROW][C]162[/C][C]24[/C][C]17.1096668245955[/C][C]6.89033317540452[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]12.6307543756089[/C][C]1.36924562439108[/C][/ROW]
[ROW][C]164[/C][C]20[/C][C]10.6953648653731[/C][C]9.30463513462688[/C][/ROW]
[ROW][C]165[/C][C]18[/C][C]15.859109389839[/C][C]2.140890610161[/C][/ROW]
[ROW][C]166[/C][C]23[/C][C]17.1733234772839[/C][C]5.82667652271613[/C][/ROW]
[ROW][C]167[/C][C]12[/C][C]13.055081181971[/C][C]-1.05508118197098[/C][/ROW]
[ROW][C]168[/C][C]14[/C][C]14.8255026820734[/C][C]-0.825502682073391[/C][/ROW]
[ROW][C]169[/C][C]16[/C][C]16.2211245696061[/C][C]-0.221124569606067[/C][/ROW]
[ROW][C]170[/C][C]18[/C][C]16.2723321813534[/C][C]1.7276678186466[/C][/ROW]
[ROW][C]171[/C][C]20[/C][C]16.5533382507622[/C][C]3.44666174923777[/C][/ROW]
[ROW][C]172[/C][C]12[/C][C]11.5359383338765[/C][C]0.46406166612348[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]16.2084923595002[/C][C]-4.20849235950018[/C][/ROW]
[ROW][C]174[/C][C]17[/C][C]15.1907638364484[/C][C]1.80923616355164[/C][/ROW]
[ROW][C]175[/C][C]13[/C][C]11.8203900297676[/C][C]1.17960997023241[/C][/ROW]
[ROW][C]176[/C][C]9[/C][C]13.0368530072049[/C][C]-4.03685300720495[/C][/ROW]
[ROW][C]177[/C][C]16[/C][C]17.2949149119527[/C][C]-1.29491491195274[/C][/ROW]
[ROW][C]178[/C][C]18[/C][C]14.9823263339676[/C][C]3.01767366603244[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]12.2197674410369[/C][C]-2.21976744103686[/C][/ROW]
[ROW][C]180[/C][C]14[/C][C]14.8208728587478[/C][C]-0.820872858747804[/C][/ROW]
[ROW][C]181[/C][C]11[/C][C]14.0029797837371[/C][C]-3.00297978373709[/C][/ROW]
[ROW][C]182[/C][C]9[/C][C]14.5213958422977[/C][C]-5.52139584229772[/C][/ROW]
[ROW][C]183[/C][C]11[/C][C]12.4064530601426[/C][C]-1.40645306014257[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]12.3260504295329[/C][C]-2.32605042953289[/C][/ROW]
[ROW][C]185[/C][C]11[/C][C]11.8103681088032[/C][C]-0.810368108803207[/C][/ROW]
[ROW][C]186[/C][C]19[/C][C]13.2271815854447[/C][C]5.7728184145553[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]12.4352448058596[/C][C]1.56475519414044[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.4925521935963[/C][C]0.507447806403657[/C][/ROW]
[ROW][C]189[/C][C]14[/C][C]15.0959577912219[/C][C]-1.09595779122193[/C][/ROW]
[ROW][C]190[/C][C]21[/C][C]16.7072198463595[/C][C]4.29278015364053[/C][/ROW]
[ROW][C]191[/C][C]13[/C][C]15.6481146327759[/C][C]-2.64811463277587[/C][/ROW]
[ROW][C]192[/C][C]10[/C][C]12.4486729761108[/C][C]-2.44867297611076[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]13.0827494349911[/C][C]1.91725056500893[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]16.16791141144[/C][C]-0.167911411440049[/C][/ROW]
[ROW][C]195[/C][C]14[/C][C]12.6749790366284[/C][C]1.32502096337161[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]14.7066877693377[/C][C]-2.70668776933766[/C][/ROW]
[ROW][C]197[/C][C]19[/C][C]12.6132864204279[/C][C]6.38671357957211[/C][/ROW]
[ROW][C]198[/C][C]15[/C][C]12.1644052332707[/C][C]2.83559476672929[/C][/ROW]
[ROW][C]199[/C][C]19[/C][C]17.9896906622778[/C][C]1.01030933772221[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]13.2987067190283[/C][C]-0.298706719028284[/C][/ROW]
[ROW][C]201[/C][C]17[/C][C]17.1231997625005[/C][C]-0.123199762500526[/C][/ROW]
[ROW][C]202[/C][C]12[/C][C]12.7674065438777[/C][C]-0.767406543877671[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]10.6425083496772[/C][C]0.357491650322818[/C][/ROW]
[ROW][C]204[/C][C]14[/C][C]15.391098939205[/C][C]-1.39109893920504[/C][/ROW]
[ROW][C]205[/C][C]11[/C][C]12.3813885335817[/C][C]-1.38138853358167[/C][/ROW]
[ROW][C]206[/C][C]13[/C][C]12.2761962912289[/C][C]0.723803708771058[/C][/ROW]
[ROW][C]207[/C][C]12[/C][C]12.6857334320538[/C][C]-0.685733432053771[/C][/ROW]
[ROW][C]208[/C][C]15[/C][C]12.6357725751679[/C][C]2.36422742483209[/C][/ROW]
[ROW][C]209[/C][C]14[/C][C]14.395503288059[/C][C]-0.395503288058991[/C][/ROW]
[ROW][C]210[/C][C]12[/C][C]10.8273814639431[/C][C]1.17261853605694[/C][/ROW]
[ROW][C]211[/C][C]17[/C][C]17.4777798338588[/C][C]-0.477779833858776[/C][/ROW]
[ROW][C]212[/C][C]11[/C][C]11.2474110902512[/C][C]-0.247411090251218[/C][/ROW]
[ROW][C]213[/C][C]18[/C][C]14.5888285623862[/C][C]3.41117143761383[/C][/ROW]
[ROW][C]214[/C][C]13[/C][C]15.8595853229194[/C][C]-2.85958532291937[/C][/ROW]
[ROW][C]215[/C][C]17[/C][C]15.2582642983906[/C][C]1.7417357016094[/C][/ROW]
[ROW][C]216[/C][C]13[/C][C]13.3040103616622[/C][C]-0.304010361662213[/C][/ROW]
[ROW][C]217[/C][C]11[/C][C]10.1197133028837[/C][C]0.88028669711633[/C][/ROW]
[ROW][C]218[/C][C]12[/C][C]12.5627209103186[/C][C]-0.562720910318637[/C][/ROW]
[ROW][C]219[/C][C]22[/C][C]17.9921830003293[/C][C]4.00781699967066[/C][/ROW]
[ROW][C]220[/C][C]14[/C][C]12.0213276506491[/C][C]1.97867234935086[/C][/ROW]
[ROW][C]221[/C][C]12[/C][C]15.0421356006936[/C][C]-3.04213560069364[/C][/ROW]
[ROW][C]222[/C][C]12[/C][C]11.8537889622196[/C][C]0.146211037780439[/C][/ROW]
[ROW][C]223[/C][C]17[/C][C]15.9533148924048[/C][C]1.04668510759519[/C][/ROW]
[ROW][C]224[/C][C]9[/C][C]12.8234212732034[/C][C]-3.82342127320344[/C][/ROW]
[ROW][C]225[/C][C]21[/C][C]17.1365923071396[/C][C]3.8634076928604[/C][/ROW]
[ROW][C]226[/C][C]10[/C][C]11.9179113198274[/C][C]-1.91791131982743[/C][/ROW]
[ROW][C]227[/C][C]11[/C][C]10.8617887170657[/C][C]0.138211282934347[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]14.5925564778268[/C][C]-2.59255647782678[/C][/ROW]
[ROW][C]229[/C][C]23[/C][C]18.3334231400647[/C][C]4.66657685993528[/C][/ROW]
[ROW][C]230[/C][C]13[/C][C]15.6979505366254[/C][C]-2.69795053662538[/C][/ROW]
[ROW][C]231[/C][C]12[/C][C]13.386506176235[/C][C]-1.38650617623499[/C][/ROW]
[ROW][C]232[/C][C]16[/C][C]17.8977570916836[/C][C]-1.89775709168362[/C][/ROW]
[ROW][C]233[/C][C]9[/C][C]13.4301958683188[/C][C]-4.43019586831879[/C][/ROW]
[ROW][C]234[/C][C]17[/C][C]13.5010227448443[/C][C]3.49897725515565[/C][/ROW]
[ROW][C]235[/C][C]9[/C][C]11.8873896605286[/C][C]-2.88738966052862[/C][/ROW]
[ROW][C]236[/C][C]14[/C][C]15.6544395742704[/C][C]-1.65443957427039[/C][/ROW]
[ROW][C]237[/C][C]17[/C][C]15.711272176253[/C][C]1.28872782374696[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]15.4946665608675[/C][C]-2.49466656086749[/C][/ROW]
[ROW][C]239[/C][C]11[/C][C]15.8808621627185[/C][C]-4.88086216271848[/C][/ROW]
[ROW][C]240[/C][C]12[/C][C]15.6137825421604[/C][C]-3.61378254216041[/C][/ROW]
[ROW][C]241[/C][C]10[/C][C]14.0764377110579[/C][C]-4.07643771105792[/C][/ROW]
[ROW][C]242[/C][C]19[/C][C]18.930480691262[/C][C]0.0695193087380221[/C][/ROW]
[ROW][C]243[/C][C]16[/C][C]16.1116301325695[/C][C]-0.111630132569475[/C][/ROW]
[ROW][C]244[/C][C]16[/C][C]15.0803567371333[/C][C]0.919643262866729[/C][/ROW]
[ROW][C]245[/C][C]14[/C][C]12.1228651735912[/C][C]1.87713482640878[/C][/ROW]
[ROW][C]246[/C][C]20[/C][C]15.9261823522834[/C][C]4.07381764771663[/C][/ROW]
[ROW][C]247[/C][C]15[/C][C]14.4877393612271[/C][C]0.512260638772898[/C][/ROW]
[ROW][C]248[/C][C]23[/C][C]15.4475494060658[/C][C]7.55245059393425[/C][/ROW]
[ROW][C]249[/C][C]20[/C][C]17.9949100585686[/C][C]2.00508994143142[/C][/ROW]
[ROW][C]250[/C][C]16[/C][C]16.051525496072[/C][C]-0.0515254960719899[/C][/ROW]
[ROW][C]251[/C][C]14[/C][C]12.9735395265636[/C][C]1.02646047343643[/C][/ROW]
[ROW][C]252[/C][C]17[/C][C]13.9246990404077[/C][C]3.07530095959227[/C][/ROW]
[ROW][C]253[/C][C]11[/C][C]14.2645985407165[/C][C]-3.26459854071648[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]13.9611770118699[/C][C]-0.961177011869939[/C][/ROW]
[ROW][C]255[/C][C]17[/C][C]15.269984307382[/C][C]1.73001569261802[/C][/ROW]
[ROW][C]256[/C][C]15[/C][C]15.2013316498899[/C][C]-0.201331649889872[/C][/ROW]
[ROW][C]257[/C][C]21[/C][C]15.9434517871989[/C][C]5.05654821280109[/C][/ROW]
[ROW][C]258[/C][C]18[/C][C]17.8097764808616[/C][C]0.190223519138384[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.7641454240226[/C][C]2.23585457597737[/C][/ROW]
[ROW][C]260[/C][C]8[/C][C]16.5387264612104[/C][C]-8.53872646121037[/C][/ROW]
[ROW][C]261[/C][C]12[/C][C]13.6904753430729[/C][C]-1.69047534307286[/C][/ROW]
[ROW][C]262[/C][C]12[/C][C]12.9087082978523[/C][C]-0.908708297852339[/C][/ROW]
[ROW][C]263[/C][C]22[/C][C]19.0195272552804[/C][C]2.98047274471963[/C][/ROW]
[ROW][C]264[/C][C]12[/C][C]13.2987122538222[/C][C]-1.29871225382221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.0706787890202-2.07067878902023
2119.386966437633231.61303356236677
31415.5675810649187-1.56758106491873
41215.0690389054651-3.06903890546507
52111.463448897319.53655110268996
6129.96670764341012.0332923565899
72214.31624950857827.6837504914218
81112.6073490645059-1.60734906450589
91012.2507355875693-2.25073558756933
101312.2078414905570.792158509443016
111010.4506326835643-0.450632683564338
12810.4560288991884-2.45602889918836
131516.1303332426016-1.13033324260164
141412.50362615591521.4963738440848
15109.181354707056840.818645292943156
161414.0748811987288-0.0748811987287721
171412.75576996757131.24423003242873
18119.506125939688921.49387406031108
191013.4300135886584-3.43001358865835
201312.71647243220610.283527567793898
219.510.7309666966345-1.23096669663452
221415.8211721695944-1.82117216959437
231213.6166639166209-1.61666391662094
241415.6977202383925-1.6977202383925
25119.690131118863191.30986888113681
26915.4042718938735-6.40427189387354
271110.8429827819160.157017218083998
281513.5471640846961.45283591530395
291411.60540747282472.39459252717534
301316.0748009535387-3.07480095353867
31911.3311845358491-2.33118453584906
321515.4811799450673-0.481179945067336
331010.9720583597398-0.972058359739787
341111.9987926344505-0.998792634450494
351312.57924972069960.420750279300397
36812.4446557288483-4.44465572884833
372017.62426239753572.37573760246428
381212.3913844304001-0.391384430400062
391010.8230444242719-0.823044424271949
401013.8727843851482-3.8727843851482
41912.0700584795227-3.07005847952271
421411.32310179100822.67689820899176
43812.0752714430978-4.07527144309778
441415.6619347922747-1.66193479227465
451112.4771260546197-1.47712605461966
461315.4546859321419-2.45468593214188
47913.2007437142603-4.20074371426028
481112.3012373392283-1.30123733922831
491510.28001484870424.71998515129578
501113.4198208241544-2.41982082415444
511011.6561895506617-1.65618955066173
521413.4444175299720.555582470027986
531815.86165286010112.13834713989892
541416.1752560994128-2.17525609941278
551115.424028004785-4.42402800478503
5614.512.3449078550742.15509214492598
571311.24752527240781.75247472759222
58912.9521256625237-3.95212566252374
591014.1364691048419-4.13646910484192
601514.58255621741860.417443782581397
612018.89783852648561.10216147351436
621212.9701019822211-0.970101982221123
631214.4946349613718-2.49463496137181
641414.3977244788636-0.397724478863642
651313.3465184438438-0.346518443843839
661115.2551842026485-4.25518420264852
671715.43390265027081.56609734972917
681213.824492807011-1.82449280701102
691312.93671273565830.063287264341692
701412.43590710842921.56409289157076
711314.1962687930734-1.19626879307343
721512.99535099330152.00464900669853
731311.37524127810481.62475872189522
741011.8367923777135-1.83679237771348
751112.780603418997-1.78060341899701
761913.73184285378395.2681571462161
771310.91461719179462.08538280820535
781714.16355948010472.83644051989525
791312.48106044673980.518939553260202
80914.6155095332266-5.6155095332266
811111.6202293351416-0.620229335141621
82912.5724474751648-3.57244747516481
831211.26699627473910.733003725260945
841212.4942226380307-0.494222638030669
851312.77955465444580.220445345554208
861312.11843714816110.881562851838943
871213.2054462258336-1.20544622583363
881514.96801689911660.0319831008834403
892218.22973602480363.7702639751964
901311.57329009641051.42670990358946
911514.53077962468120.469220375318806
921311.86215831102611.13784168897386
931512.25135000724572.74864999275434
9412.513.4100310275549-0.910031027554902
951110.71517709877670.284822901223324
961614.58372662062041.41627337937964
971112.4332147071392-1.4332147071392
981110.02656028438830.973439715611686
991012.3154875257295-2.31548752572946
1001010.2534498040753-0.253449804075338
1011614.3317960529341.66820394706604
1021210.36991490629981.63008509370023
1031115.8774150398815-4.87741503988146
1041611.80476542173544.19523457826456
1051916.70338969792692.2966103020731
1061111.2027375647298-0.202737564729814
1071612.05666896092873.94333103907132
1081516.8483856022461-1.84838560224612
1092417.78175233540316.21824766459691
1101411.70973430159832.29026569840168
1111514.90887500335430.091124996645688
1121113.0143654843153-2.01436548431534
1131513.61725323322971.38274676677026
1141210.29328723728891.70671276271107
1151010.512578368055-0.512578368055028
1161414.2937682134376-0.293768213437622
1171314.0429451333889-1.04294513338889
118913.488903501121-4.48890350112104
1191511.80419138087113.19580861912894
1201515.8213128610493-0.821312861049344
1211412.76885593436171.23114406563833
1221111.3724384141774-0.372438414177408
123812.0057475305325-4.00574753053249
1241112.264754845446-1.26475484544597
1251113.1692830825707-2.16928308257066
12689.7504989466578-1.7504989466578
1271010.1649517011955-0.164951701195522
128119.655941060057761.34405893994224
1291312.6212488885910.378751111409003
1301113.7012421239307-2.70124212393068
1312017.61391957466162.38608042533843
1321011.7305312221829-1.73053122218292
1331513.37721248441941.62278751558061
1341212.3714800555824-0.371480055582409
1351411.11970893849292.88029106150705
1362316.68653713100846.31346286899159
1371414.1566526501826-0.156652650182635
1381617.1159559049064-1.11595590490635
1391113.2955474557045-2.29554745570451
1401214.3497742905291-2.3497742905291
1411013.5391292765128-3.53912927651279
1421411.17914610413422.82085389586583
1431212.2643895106563-0.264389510656309
1441211.93855722795870.0614427720412801
1451111.0276187019894-0.0276187019894091
1461211.288281956190.71171804380997
1471316.2155145272082-3.21551452720818
1481114.3363011715584-3.33630117155838
1491916.95922847212652.04077152787353
1501211.4787551984310.521244801568986
1511712.88164559704914.11835440295089
152911.4102610697361-2.41026106973612
1531214.5571407032858-2.55714070328582
1541916.94510192281712.05489807718293
1551814.46718225707283.53281774292717
1561514.53077962468120.469220375318806
1571413.80168045861530.19831954138474
158119.655941060057761.34405893994224
159912.9771730113877-3.97717301138775
1601814.06693422258123.93306577741879
1611614.80405542278291.19594457721712
1622417.10966682459556.89033317540452
1631412.63075437560891.36924562439108
1642010.69536486537319.30463513462688
1651815.8591093898392.140890610161
1662317.17332347728395.82667652271613
1671213.055081181971-1.05508118197098
1681414.8255026820734-0.825502682073391
1691616.2211245696061-0.221124569606067
1701816.27233218135341.7276678186466
1712016.55333825076223.44666174923777
1721211.53593833387650.46406166612348
1731216.2084923595002-4.20849235950018
1741715.19076383644841.80923616355164
1751311.82039002976761.17960997023241
176913.0368530072049-4.03685300720495
1771617.2949149119527-1.29491491195274
1781814.98232633396763.01767366603244
1791012.2197674410369-2.21976744103686
1801414.8208728587478-0.820872858747804
1811114.0029797837371-3.00297978373709
182914.5213958422977-5.52139584229772
1831112.4064530601426-1.40645306014257
1841012.3260504295329-2.32605042953289
1851111.8103681088032-0.810368108803207
1861913.22718158544475.7728184145553
1871412.43524480585961.56475519414044
1881211.49255219359630.507447806403657
1891415.0959577912219-1.09595779122193
1902116.70721984635954.29278015364053
1911315.6481146327759-2.64811463277587
1921012.4486729761108-2.44867297611076
1931513.08274943499111.91725056500893
1941616.16791141144-0.167911411440049
1951412.67497903662841.32502096337161
1961214.7066877693377-2.70668776933766
1971912.61328642042796.38671357957211
1981512.16440523327072.83559476672929
1991917.98969066227781.01030933772221
2001313.2987067190283-0.298706719028284
2011717.1231997625005-0.123199762500526
2021212.7674065438777-0.767406543877671
2031110.64250834967720.357491650322818
2041415.391098939205-1.39109893920504
2051112.3813885335817-1.38138853358167
2061312.27619629122890.723803708771058
2071212.6857334320538-0.685733432053771
2081512.63577257516792.36422742483209
2091414.395503288059-0.395503288058991
2101210.82738146394311.17261853605694
2111717.4777798338588-0.477779833858776
2121111.2474110902512-0.247411090251218
2131814.58882856238623.41117143761383
2141315.8595853229194-2.85958532291937
2151715.25826429839061.7417357016094
2161313.3040103616622-0.304010361662213
2171110.11971330288370.88028669711633
2181212.5627209103186-0.562720910318637
2192217.99218300032934.00781699967066
2201412.02132765064911.97867234935086
2211215.0421356006936-3.04213560069364
2221211.85378896221960.146211037780439
2231715.95331489240481.04668510759519
224912.8234212732034-3.82342127320344
2252117.13659230713963.8634076928604
2261011.9179113198274-1.91791131982743
2271110.86178871706570.138211282934347
2281214.5925564778268-2.59255647782678
2292318.33342314006474.66657685993528
2301315.6979505366254-2.69795053662538
2311213.386506176235-1.38650617623499
2321617.8977570916836-1.89775709168362
233913.4301958683188-4.43019586831879
2341713.50102274484433.49897725515565
235911.8873896605286-2.88738966052862
2361415.6544395742704-1.65443957427039
2371715.7112721762531.28872782374696
2381315.4946665608675-2.49466656086749
2391115.8808621627185-4.88086216271848
2401215.6137825421604-3.61378254216041
2411014.0764377110579-4.07643771105792
2421918.9304806912620.0695193087380221
2431616.1116301325695-0.111630132569475
2441615.08035673713330.919643262866729
2451412.12286517359121.87713482640878
2462015.92618235228344.07381764771663
2471514.48773936122710.512260638772898
2482315.44754940606587.55245059393425
2492017.99491005856862.00508994143142
2501616.051525496072-0.0515254960719899
2511412.97353952656361.02646047343643
2521713.92469904040773.07530095959227
2531114.2645985407165-3.26459854071648
2541313.9611770118699-0.961177011869939
2551715.2699843073821.73001569261802
2561515.2013316498899-0.201331649889872
2572115.94345178719895.05654821280109
2581817.80977648086160.190223519138384
2591512.76414542402262.23585457597737
260816.5387264612104-8.53872646121037
2611213.6904753430729-1.69047534307286
2621212.9087082978523-0.908708297852339
2632219.01952725528042.98047274471963
2641213.2987122538222-1.29871225382221







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4463279045029760.8926558090059520.553672095497024
120.9522335594823180.0955328810353640.047766440517682
130.9458207250446130.1083585499107740.0541792749553868
140.9597723579432340.08045528411353230.0402276420567662
150.9360392148426670.1279215703146660.0639607851573329
160.9012585924158040.1974828151683920.0987414075841959
170.9501853656235770.09962926875284550.0498146343764228
180.9322188474495280.1355623051009430.0677811525504715
190.9571082193184310.08578356136313880.0428917806815694
200.937604538728940.124790922542120.0623954612710602
210.9265416443843070.1469167112313860.0734583556156931
220.9218152935290550.156369412941890.0781847064709448
230.8944365219774720.2111269560450550.105563478022528
240.8750700872314690.2498598255370620.124929912768531
250.8366461930843170.3267076138313660.163353806915683
260.8377232857289080.3245534285421850.162276714271092
270.8488010577639050.3023978844721910.151198942236095
280.8089633526593140.3820732946813710.191036647340686
290.8402573784053490.3194852431893020.159742621594651
300.8307678352445920.3384643295108160.169232164755408
310.8365683433842030.3268633132315930.163431656615796
320.8052945886912250.3894108226175490.194705411308775
330.7742716786984410.4514566426031180.225728321301559
340.7497873161015210.5004253677969570.250212683898479
350.7163104782806470.5673790434387070.283689521719353
360.750689279309930.4986214413801410.24931072069007
370.8321416639029020.3357166721941950.167858336097098
380.7970177607635660.4059644784728690.202982239236434
390.7632041505531370.4735916988937260.236795849446863
400.762826178449210.4743476431015810.23717382155079
410.7510926770852190.4978146458295620.248907322914781
420.7375337344374790.5249325311250410.262466265562521
430.7902789260450660.4194421479098680.209721073954934
440.7550514410991220.4898971178017550.244948558900877
450.7187057693861030.5625884612277950.281294230613897
460.6928635648430730.6142728703138540.307136435156927
470.7487027706028590.5025944587942820.251297229397141
480.7148595691689860.5702808616620290.285140430831014
490.7683180043557950.463363991288410.231681995644205
500.7426765207757810.5146469584484390.257323479224219
510.7306888053118480.5386223893763030.269311194688152
520.6963511996007540.6072976007984930.303648800399246
530.6985556677843220.6028886644313550.301444332215678
540.6661088505996860.6677822988006280.333891149400314
550.6781576288469330.6436847423061330.321842371153067
560.6939825138123940.6120349723752120.306017486187606
570.6716192299567770.6567615400864470.328380770043223
580.7083308756372490.5833382487255010.291669124362751
590.7233295430206730.5533409139586540.276670456979327
600.7001469331438060.5997061337123890.299853066856194
610.7122493527147730.5755012945704530.287750647285227
620.6757692867682580.6484614264634850.324230713231742
630.6520898764601470.6958202470797060.347910123539853
640.6120835992326910.7758328015346180.387916400767309
650.5711704408992630.8576591182014740.428829559100737
660.5813684543883610.8372630912232790.418631545611639
670.5841118642724080.8317762714551840.415888135727592
680.5542636364549260.8914727270901480.445736363545074
690.5135644480605630.9728711038788750.486435551939437
700.4895370834587810.9790741669175620.510462916541219
710.4507582157643520.9015164315287040.549241784235648
720.4269952860384310.8539905720768620.573004713961569
730.4002773084587880.8005546169175750.599722691541212
740.3793882995555750.7587765991111490.620611700444425
750.3495254783122040.6990509566244080.650474521687796
760.5113807088215080.9772385823569840.488619291178492
770.4901661626436220.9803323252872440.509833837356378
780.5064315491175930.9871369017648140.493568450882407
790.4681799037321270.9363598074642540.531820096267873
800.5661954074955770.8676091850088470.433804592504423
810.529343775063850.9413124498722990.47065622493615
820.5534282633448910.8931434733102170.446571736655108
830.517041460989180.9659170780216390.48295853901082
840.4790579175051880.9581158350103760.520942082494812
850.4411858671171260.8823717342342510.558814132882874
860.4076287078188250.8152574156376510.592371292181175
870.3757107706130030.7514215412260060.624289229386997
880.3426718404698750.6853436809397490.657328159530125
890.4330312208022110.8660624416044210.566968779197789
900.4035111459534760.8070222919069520.596488854046524
910.3749560808269720.7499121616539430.625043919173028
920.3446894256757470.6893788513514940.655310574324253
930.3444014501019990.6888029002039980.655598549898001
940.3131897596974130.6263795193948260.686810240302587
950.2804810772852710.5609621545705430.719518922714729
960.2645877308376680.5291754616753360.735412269162332
970.2419151873073960.4838303746147910.758084812692605
980.2154561424731440.4309122849462880.784543857526856
990.2074787967542140.4149575935084270.792521203245786
1000.1823278790564770.3646557581129540.817672120943523
1010.171849907758240.3436998155164790.82815009224176
1020.1537133562165160.3074267124330320.846286643783484
1030.1940613467901840.3881226935803670.805938653209817
1040.2249135884204760.4498271768409520.775086411579524
1050.233729419291150.46745883858230.76627058070885
1060.2067241767188610.4134483534377230.793275823281139
1070.2318868631907560.4637737263815110.768113136809244
1080.2154442016107960.4308884032215920.784555798389204
1090.3440330705239470.6880661410478950.655966929476053
1100.3353864076125260.6707728152250530.664613592387474
1110.3046271747159020.6092543494318050.695372825284098
1120.2935575170452530.5871150340905070.706442482954747
1130.2693810554387190.5387621108774370.730618944561281
1140.2505102698888720.5010205397777450.749489730111128
1150.2235721227080250.4471442454160510.776427877291975
1160.1975902596189850.3951805192379690.802409740381016
1170.1767884547438580.3535769094877150.823211545256142
1180.2154226031821260.4308452063642520.784577396817874
1190.2213570659079680.4427141318159350.778642934092032
1200.1974302529136390.3948605058272780.802569747086361
1210.1823231958038380.3646463916076760.817676804196162
1220.161451860678020.322903721356040.83854813932198
1230.1906433061254650.3812866122509290.809356693874535
1240.1716404775115890.3432809550231790.828359522488411
1250.1619494256583630.3238988513167270.838050574341637
1260.1507609962400750.3015219924801510.849239003759925
1270.1325745175190820.2651490350381630.867425482480918
1280.1182261000678350.2364522001356710.881773899932165
1290.1018591543258420.2037183086516840.898140845674158
1300.1008898027982110.2017796055964220.899110197201789
1310.1025617069879720.2051234139759430.897438293012028
1320.09454795021400080.1890959004280020.905452049785999
1330.08634471828627740.1726894365725550.913655281713723
1340.0733799400048260.1467598800096520.926620059995174
1350.07485014738019420.1497002947603880.925149852619806
1360.1492236745265770.2984473490531540.850776325473423
1370.1300289751445170.2600579502890350.869971024855483
1380.1152080566027980.2304161132055950.884791943397202
1390.1103981390584470.2207962781168940.889601860941553
1400.1051487826693090.2102975653386180.894851217330691
1410.1177754119494420.2355508238988830.882224588050558
1420.1185327024955460.2370654049910920.881467297504454
1430.101943280990130.203886561980260.89805671900987
1440.08679032564508760.1735806512901750.913209674354912
1450.07390058715235920.1478011743047180.926099412847641
1460.06261175131783360.1252235026356670.937388248682166
1470.06863007274242170.1372601454848430.931369927257578
1480.07478567499495480.149571349989910.925214325005045
1490.070274199711610.140548399423220.92972580028839
1500.05970817587303670.1194163517460730.940291824126963
1510.07280106342161180.1456021268432240.927198936578388
1520.06989169674601020.139783393492020.93010830325399
1530.06854252237925130.1370850447585030.931457477620749
1540.06373037303029060.1274607460605810.936269626969709
1550.07091898407611370.1418379681522270.929081015923886
1560.06046151681820280.1209230336364060.939538483181797
1570.05050120659676420.1010024131935280.949498793403236
1580.04306938929076860.08613877858153720.956930610709231
1590.05236391368620410.1047278273724080.947636086313796
1600.06526640303742340.1305328060748470.934733596962577
1610.05582610266015090.1116522053203020.944173897339849
1620.1230237884097330.2460475768194660.876976211590267
1630.1124770216894410.2249540433788820.887522978310559
1640.3959741345179040.7919482690358080.604025865482096
1650.378885954738160.7577719094763190.62111404526184
1660.4870899917554890.9741799835109770.512910008244511
1670.4550028797796760.9100057595593520.544997120220324
1680.4232520557689960.8465041115379920.576747944231004
1690.3870156930575490.7740313861150970.612984306942451
1700.3651125562784470.7302251125568950.634887443721553
1710.3749106550169230.7498213100338470.625089344983077
1720.3401638197535040.6803276395070090.659836180246496
1730.3835019050238340.7670038100476680.616498094976166
1740.3707500871152640.7415001742305290.629249912884736
1750.3485076478747460.6970152957494910.651492352125254
1760.3850932120296340.7701864240592680.614906787970366
1770.3591536525917160.7183073051834330.640846347408284
1780.3716937705375330.7433875410750670.628306229462467
1790.3568329159571060.7136658319142130.643167084042894
1800.3245210691516460.6490421383032920.675478930848354
1810.3519327704173890.7038655408347770.648067229582611
1820.4570749952548210.9141499905096420.542925004745179
1830.4421090672556160.8842181345112330.557890932744384
1840.430623002046470.861246004092940.56937699795353
1850.4140617383990580.8281234767981150.585938261600942
1860.5412294913835790.9175410172328430.458770508616421
1870.5092431138806530.9815137722386930.490756886119347
1880.4765167141802430.9530334283604870.523483285819757
1890.4402804590057490.8805609180114970.559719540994251
1900.4790350424453080.9580700848906160.520964957554692
1910.5084526219468430.9830947561063140.491547378053157
1920.5254128699941460.9491742600117080.474587130005854
1930.5161614425373140.9676771149253720.483838557462686
1940.4853519660992730.9707039321985470.514648033900727
1950.4575474386124240.9150948772248490.542452561387576
1960.4928153287641080.9856306575282150.507184671235892
1970.6973941421641390.6052117156717230.302605857835861
1980.7055090881210030.5889818237579940.294490911878997
1990.6688720435673950.662255912865210.331127956432605
2000.6293520206935750.741295958612850.370647979306425
2010.5890264798364220.8219470403271560.410973520163578
2020.5482287393486310.9035425213027390.451771260651369
2030.523546448541730.9529071029165410.47645355145827
2040.519320283177250.96135943364550.48067971682275
2050.4820681032720710.9641362065441420.517931896727929
2060.4394459041264290.8788918082528580.560554095873571
2070.3982527243632660.7965054487265320.601747275636734
2080.3648431767218830.7296863534437660.635156823278117
2090.3237661163656560.6475322327313120.676233883634344
2100.3164326090042440.6328652180084890.683567390995756
2110.2909111370468080.5818222740936150.709088862953192
2120.2565793185206940.5131586370413880.743420681479306
2130.2837074456867610.5674148913735210.716292554313239
2140.2659421842460070.5318843684920130.734057815753993
2150.2329721559085530.4659443118171050.767027844091448
2160.2002631541081920.4005263082163850.799736845891807
2170.1901394597651890.3802789195303790.809860540234811
2180.160492009465950.32098401893190.83950799053405
2190.1634965271407640.3269930542815290.836503472859236
2200.155155275284890.3103105505697810.84484472471511
2210.1541199822585170.3082399645170350.845880017741483
2220.1275378421660050.255075684332010.872462157833995
2230.1049216758889310.2098433517778610.895078324111069
2240.1058394081198380.2116788162396770.894160591880162
2250.09576817764201250.1915363552840250.904231822357987
2260.07798028019421660.1559605603884330.922019719805783
2270.06071950645563650.1214390129112730.939280493544363
2280.05700005452223970.1140001090444790.94299994547776
2290.07715872969658240.1543174593931650.922841270303418
2300.07343508624837210.1468701724967440.926564913751628
2310.05652088001458220.1130417600291640.943479119985418
2320.05802216100956890.1160443220191380.941977838990431
2330.1207749026978650.241549805395730.879225097302135
2340.1285174940888360.2570349881776710.871482505911164
2350.1221870354616680.2443740709233360.877812964538332
2360.09518633202041250.1903726640408250.904813667979588
2370.1238311602012720.2476623204025440.876168839798728
2380.1684373205137990.3368746410275980.831562679486201
2390.27078105992780.54156211985560.7292189400722
2400.2691496220083360.5382992440166720.730850377991664
2410.2231843482776780.4463686965553560.776815651722322
2420.2711652684349710.5423305368699410.728834731565029
2430.2114672018764470.4229344037528940.788532798123553
2440.1585675716058870.3171351432117740.841432428394113
2450.2350397229500640.4700794459001280.764960277049936
2460.2565838079439860.5131676158879710.743416192056014
2470.1896872213523820.3793744427047650.810312778647618
2480.2345499276889960.4690998553779910.765450072311004
2490.3441553042563340.6883106085126680.655844695743666
2500.24757472177560.49514944355120.7524252782244
2510.2786775396300780.5573550792601550.721322460369922
2520.9092773389400070.1814453221199870.0907226610599935
2530.8430764456808850.313847108638230.156923554319115

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.446327904502976 & 0.892655809005952 & 0.553672095497024 \tabularnewline
12 & 0.952233559482318 & 0.095532881035364 & 0.047766440517682 \tabularnewline
13 & 0.945820725044613 & 0.108358549910774 & 0.0541792749553868 \tabularnewline
14 & 0.959772357943234 & 0.0804552841135323 & 0.0402276420567662 \tabularnewline
15 & 0.936039214842667 & 0.127921570314666 & 0.0639607851573329 \tabularnewline
16 & 0.901258592415804 & 0.197482815168392 & 0.0987414075841959 \tabularnewline
17 & 0.950185365623577 & 0.0996292687528455 & 0.0498146343764228 \tabularnewline
18 & 0.932218847449528 & 0.135562305100943 & 0.0677811525504715 \tabularnewline
19 & 0.957108219318431 & 0.0857835613631388 & 0.0428917806815694 \tabularnewline
20 & 0.93760453872894 & 0.12479092254212 & 0.0623954612710602 \tabularnewline
21 & 0.926541644384307 & 0.146916711231386 & 0.0734583556156931 \tabularnewline
22 & 0.921815293529055 & 0.15636941294189 & 0.0781847064709448 \tabularnewline
23 & 0.894436521977472 & 0.211126956045055 & 0.105563478022528 \tabularnewline
24 & 0.875070087231469 & 0.249859825537062 & 0.124929912768531 \tabularnewline
25 & 0.836646193084317 & 0.326707613831366 & 0.163353806915683 \tabularnewline
26 & 0.837723285728908 & 0.324553428542185 & 0.162276714271092 \tabularnewline
27 & 0.848801057763905 & 0.302397884472191 & 0.151198942236095 \tabularnewline
28 & 0.808963352659314 & 0.382073294681371 & 0.191036647340686 \tabularnewline
29 & 0.840257378405349 & 0.319485243189302 & 0.159742621594651 \tabularnewline
30 & 0.830767835244592 & 0.338464329510816 & 0.169232164755408 \tabularnewline
31 & 0.836568343384203 & 0.326863313231593 & 0.163431656615796 \tabularnewline
32 & 0.805294588691225 & 0.389410822617549 & 0.194705411308775 \tabularnewline
33 & 0.774271678698441 & 0.451456642603118 & 0.225728321301559 \tabularnewline
34 & 0.749787316101521 & 0.500425367796957 & 0.250212683898479 \tabularnewline
35 & 0.716310478280647 & 0.567379043438707 & 0.283689521719353 \tabularnewline
36 & 0.75068927930993 & 0.498621441380141 & 0.24931072069007 \tabularnewline
37 & 0.832141663902902 & 0.335716672194195 & 0.167858336097098 \tabularnewline
38 & 0.797017760763566 & 0.405964478472869 & 0.202982239236434 \tabularnewline
39 & 0.763204150553137 & 0.473591698893726 & 0.236795849446863 \tabularnewline
40 & 0.76282617844921 & 0.474347643101581 & 0.23717382155079 \tabularnewline
41 & 0.751092677085219 & 0.497814645829562 & 0.248907322914781 \tabularnewline
42 & 0.737533734437479 & 0.524932531125041 & 0.262466265562521 \tabularnewline
43 & 0.790278926045066 & 0.419442147909868 & 0.209721073954934 \tabularnewline
44 & 0.755051441099122 & 0.489897117801755 & 0.244948558900877 \tabularnewline
45 & 0.718705769386103 & 0.562588461227795 & 0.281294230613897 \tabularnewline
46 & 0.692863564843073 & 0.614272870313854 & 0.307136435156927 \tabularnewline
47 & 0.748702770602859 & 0.502594458794282 & 0.251297229397141 \tabularnewline
48 & 0.714859569168986 & 0.570280861662029 & 0.285140430831014 \tabularnewline
49 & 0.768318004355795 & 0.46336399128841 & 0.231681995644205 \tabularnewline
50 & 0.742676520775781 & 0.514646958448439 & 0.257323479224219 \tabularnewline
51 & 0.730688805311848 & 0.538622389376303 & 0.269311194688152 \tabularnewline
52 & 0.696351199600754 & 0.607297600798493 & 0.303648800399246 \tabularnewline
53 & 0.698555667784322 & 0.602888664431355 & 0.301444332215678 \tabularnewline
54 & 0.666108850599686 & 0.667782298800628 & 0.333891149400314 \tabularnewline
55 & 0.678157628846933 & 0.643684742306133 & 0.321842371153067 \tabularnewline
56 & 0.693982513812394 & 0.612034972375212 & 0.306017486187606 \tabularnewline
57 & 0.671619229956777 & 0.656761540086447 & 0.328380770043223 \tabularnewline
58 & 0.708330875637249 & 0.583338248725501 & 0.291669124362751 \tabularnewline
59 & 0.723329543020673 & 0.553340913958654 & 0.276670456979327 \tabularnewline
60 & 0.700146933143806 & 0.599706133712389 & 0.299853066856194 \tabularnewline
61 & 0.712249352714773 & 0.575501294570453 & 0.287750647285227 \tabularnewline
62 & 0.675769286768258 & 0.648461426463485 & 0.324230713231742 \tabularnewline
63 & 0.652089876460147 & 0.695820247079706 & 0.347910123539853 \tabularnewline
64 & 0.612083599232691 & 0.775832801534618 & 0.387916400767309 \tabularnewline
65 & 0.571170440899263 & 0.857659118201474 & 0.428829559100737 \tabularnewline
66 & 0.581368454388361 & 0.837263091223279 & 0.418631545611639 \tabularnewline
67 & 0.584111864272408 & 0.831776271455184 & 0.415888135727592 \tabularnewline
68 & 0.554263636454926 & 0.891472727090148 & 0.445736363545074 \tabularnewline
69 & 0.513564448060563 & 0.972871103878875 & 0.486435551939437 \tabularnewline
70 & 0.489537083458781 & 0.979074166917562 & 0.510462916541219 \tabularnewline
71 & 0.450758215764352 & 0.901516431528704 & 0.549241784235648 \tabularnewline
72 & 0.426995286038431 & 0.853990572076862 & 0.573004713961569 \tabularnewline
73 & 0.400277308458788 & 0.800554616917575 & 0.599722691541212 \tabularnewline
74 & 0.379388299555575 & 0.758776599111149 & 0.620611700444425 \tabularnewline
75 & 0.349525478312204 & 0.699050956624408 & 0.650474521687796 \tabularnewline
76 & 0.511380708821508 & 0.977238582356984 & 0.488619291178492 \tabularnewline
77 & 0.490166162643622 & 0.980332325287244 & 0.509833837356378 \tabularnewline
78 & 0.506431549117593 & 0.987136901764814 & 0.493568450882407 \tabularnewline
79 & 0.468179903732127 & 0.936359807464254 & 0.531820096267873 \tabularnewline
80 & 0.566195407495577 & 0.867609185008847 & 0.433804592504423 \tabularnewline
81 & 0.52934377506385 & 0.941312449872299 & 0.47065622493615 \tabularnewline
82 & 0.553428263344891 & 0.893143473310217 & 0.446571736655108 \tabularnewline
83 & 0.51704146098918 & 0.965917078021639 & 0.48295853901082 \tabularnewline
84 & 0.479057917505188 & 0.958115835010376 & 0.520942082494812 \tabularnewline
85 & 0.441185867117126 & 0.882371734234251 & 0.558814132882874 \tabularnewline
86 & 0.407628707818825 & 0.815257415637651 & 0.592371292181175 \tabularnewline
87 & 0.375710770613003 & 0.751421541226006 & 0.624289229386997 \tabularnewline
88 & 0.342671840469875 & 0.685343680939749 & 0.657328159530125 \tabularnewline
89 & 0.433031220802211 & 0.866062441604421 & 0.566968779197789 \tabularnewline
90 & 0.403511145953476 & 0.807022291906952 & 0.596488854046524 \tabularnewline
91 & 0.374956080826972 & 0.749912161653943 & 0.625043919173028 \tabularnewline
92 & 0.344689425675747 & 0.689378851351494 & 0.655310574324253 \tabularnewline
93 & 0.344401450101999 & 0.688802900203998 & 0.655598549898001 \tabularnewline
94 & 0.313189759697413 & 0.626379519394826 & 0.686810240302587 \tabularnewline
95 & 0.280481077285271 & 0.560962154570543 & 0.719518922714729 \tabularnewline
96 & 0.264587730837668 & 0.529175461675336 & 0.735412269162332 \tabularnewline
97 & 0.241915187307396 & 0.483830374614791 & 0.758084812692605 \tabularnewline
98 & 0.215456142473144 & 0.430912284946288 & 0.784543857526856 \tabularnewline
99 & 0.207478796754214 & 0.414957593508427 & 0.792521203245786 \tabularnewline
100 & 0.182327879056477 & 0.364655758112954 & 0.817672120943523 \tabularnewline
101 & 0.17184990775824 & 0.343699815516479 & 0.82815009224176 \tabularnewline
102 & 0.153713356216516 & 0.307426712433032 & 0.846286643783484 \tabularnewline
103 & 0.194061346790184 & 0.388122693580367 & 0.805938653209817 \tabularnewline
104 & 0.224913588420476 & 0.449827176840952 & 0.775086411579524 \tabularnewline
105 & 0.23372941929115 & 0.4674588385823 & 0.76627058070885 \tabularnewline
106 & 0.206724176718861 & 0.413448353437723 & 0.793275823281139 \tabularnewline
107 & 0.231886863190756 & 0.463773726381511 & 0.768113136809244 \tabularnewline
108 & 0.215444201610796 & 0.430888403221592 & 0.784555798389204 \tabularnewline
109 & 0.344033070523947 & 0.688066141047895 & 0.655966929476053 \tabularnewline
110 & 0.335386407612526 & 0.670772815225053 & 0.664613592387474 \tabularnewline
111 & 0.304627174715902 & 0.609254349431805 & 0.695372825284098 \tabularnewline
112 & 0.293557517045253 & 0.587115034090507 & 0.706442482954747 \tabularnewline
113 & 0.269381055438719 & 0.538762110877437 & 0.730618944561281 \tabularnewline
114 & 0.250510269888872 & 0.501020539777745 & 0.749489730111128 \tabularnewline
115 & 0.223572122708025 & 0.447144245416051 & 0.776427877291975 \tabularnewline
116 & 0.197590259618985 & 0.395180519237969 & 0.802409740381016 \tabularnewline
117 & 0.176788454743858 & 0.353576909487715 & 0.823211545256142 \tabularnewline
118 & 0.215422603182126 & 0.430845206364252 & 0.784577396817874 \tabularnewline
119 & 0.221357065907968 & 0.442714131815935 & 0.778642934092032 \tabularnewline
120 & 0.197430252913639 & 0.394860505827278 & 0.802569747086361 \tabularnewline
121 & 0.182323195803838 & 0.364646391607676 & 0.817676804196162 \tabularnewline
122 & 0.16145186067802 & 0.32290372135604 & 0.83854813932198 \tabularnewline
123 & 0.190643306125465 & 0.381286612250929 & 0.809356693874535 \tabularnewline
124 & 0.171640477511589 & 0.343280955023179 & 0.828359522488411 \tabularnewline
125 & 0.161949425658363 & 0.323898851316727 & 0.838050574341637 \tabularnewline
126 & 0.150760996240075 & 0.301521992480151 & 0.849239003759925 \tabularnewline
127 & 0.132574517519082 & 0.265149035038163 & 0.867425482480918 \tabularnewline
128 & 0.118226100067835 & 0.236452200135671 & 0.881773899932165 \tabularnewline
129 & 0.101859154325842 & 0.203718308651684 & 0.898140845674158 \tabularnewline
130 & 0.100889802798211 & 0.201779605596422 & 0.899110197201789 \tabularnewline
131 & 0.102561706987972 & 0.205123413975943 & 0.897438293012028 \tabularnewline
132 & 0.0945479502140008 & 0.189095900428002 & 0.905452049785999 \tabularnewline
133 & 0.0863447182862774 & 0.172689436572555 & 0.913655281713723 \tabularnewline
134 & 0.073379940004826 & 0.146759880009652 & 0.926620059995174 \tabularnewline
135 & 0.0748501473801942 & 0.149700294760388 & 0.925149852619806 \tabularnewline
136 & 0.149223674526577 & 0.298447349053154 & 0.850776325473423 \tabularnewline
137 & 0.130028975144517 & 0.260057950289035 & 0.869971024855483 \tabularnewline
138 & 0.115208056602798 & 0.230416113205595 & 0.884791943397202 \tabularnewline
139 & 0.110398139058447 & 0.220796278116894 & 0.889601860941553 \tabularnewline
140 & 0.105148782669309 & 0.210297565338618 & 0.894851217330691 \tabularnewline
141 & 0.117775411949442 & 0.235550823898883 & 0.882224588050558 \tabularnewline
142 & 0.118532702495546 & 0.237065404991092 & 0.881467297504454 \tabularnewline
143 & 0.10194328099013 & 0.20388656198026 & 0.89805671900987 \tabularnewline
144 & 0.0867903256450876 & 0.173580651290175 & 0.913209674354912 \tabularnewline
145 & 0.0739005871523592 & 0.147801174304718 & 0.926099412847641 \tabularnewline
146 & 0.0626117513178336 & 0.125223502635667 & 0.937388248682166 \tabularnewline
147 & 0.0686300727424217 & 0.137260145484843 & 0.931369927257578 \tabularnewline
148 & 0.0747856749949548 & 0.14957134998991 & 0.925214325005045 \tabularnewline
149 & 0.07027419971161 & 0.14054839942322 & 0.92972580028839 \tabularnewline
150 & 0.0597081758730367 & 0.119416351746073 & 0.940291824126963 \tabularnewline
151 & 0.0728010634216118 & 0.145602126843224 & 0.927198936578388 \tabularnewline
152 & 0.0698916967460102 & 0.13978339349202 & 0.93010830325399 \tabularnewline
153 & 0.0685425223792513 & 0.137085044758503 & 0.931457477620749 \tabularnewline
154 & 0.0637303730302906 & 0.127460746060581 & 0.936269626969709 \tabularnewline
155 & 0.0709189840761137 & 0.141837968152227 & 0.929081015923886 \tabularnewline
156 & 0.0604615168182028 & 0.120923033636406 & 0.939538483181797 \tabularnewline
157 & 0.0505012065967642 & 0.101002413193528 & 0.949498793403236 \tabularnewline
158 & 0.0430693892907686 & 0.0861387785815372 & 0.956930610709231 \tabularnewline
159 & 0.0523639136862041 & 0.104727827372408 & 0.947636086313796 \tabularnewline
160 & 0.0652664030374234 & 0.130532806074847 & 0.934733596962577 \tabularnewline
161 & 0.0558261026601509 & 0.111652205320302 & 0.944173897339849 \tabularnewline
162 & 0.123023788409733 & 0.246047576819466 & 0.876976211590267 \tabularnewline
163 & 0.112477021689441 & 0.224954043378882 & 0.887522978310559 \tabularnewline
164 & 0.395974134517904 & 0.791948269035808 & 0.604025865482096 \tabularnewline
165 & 0.37888595473816 & 0.757771909476319 & 0.62111404526184 \tabularnewline
166 & 0.487089991755489 & 0.974179983510977 & 0.512910008244511 \tabularnewline
167 & 0.455002879779676 & 0.910005759559352 & 0.544997120220324 \tabularnewline
168 & 0.423252055768996 & 0.846504111537992 & 0.576747944231004 \tabularnewline
169 & 0.387015693057549 & 0.774031386115097 & 0.612984306942451 \tabularnewline
170 & 0.365112556278447 & 0.730225112556895 & 0.634887443721553 \tabularnewline
171 & 0.374910655016923 & 0.749821310033847 & 0.625089344983077 \tabularnewline
172 & 0.340163819753504 & 0.680327639507009 & 0.659836180246496 \tabularnewline
173 & 0.383501905023834 & 0.767003810047668 & 0.616498094976166 \tabularnewline
174 & 0.370750087115264 & 0.741500174230529 & 0.629249912884736 \tabularnewline
175 & 0.348507647874746 & 0.697015295749491 & 0.651492352125254 \tabularnewline
176 & 0.385093212029634 & 0.770186424059268 & 0.614906787970366 \tabularnewline
177 & 0.359153652591716 & 0.718307305183433 & 0.640846347408284 \tabularnewline
178 & 0.371693770537533 & 0.743387541075067 & 0.628306229462467 \tabularnewline
179 & 0.356832915957106 & 0.713665831914213 & 0.643167084042894 \tabularnewline
180 & 0.324521069151646 & 0.649042138303292 & 0.675478930848354 \tabularnewline
181 & 0.351932770417389 & 0.703865540834777 & 0.648067229582611 \tabularnewline
182 & 0.457074995254821 & 0.914149990509642 & 0.542925004745179 \tabularnewline
183 & 0.442109067255616 & 0.884218134511233 & 0.557890932744384 \tabularnewline
184 & 0.43062300204647 & 0.86124600409294 & 0.56937699795353 \tabularnewline
185 & 0.414061738399058 & 0.828123476798115 & 0.585938261600942 \tabularnewline
186 & 0.541229491383579 & 0.917541017232843 & 0.458770508616421 \tabularnewline
187 & 0.509243113880653 & 0.981513772238693 & 0.490756886119347 \tabularnewline
188 & 0.476516714180243 & 0.953033428360487 & 0.523483285819757 \tabularnewline
189 & 0.440280459005749 & 0.880560918011497 & 0.559719540994251 \tabularnewline
190 & 0.479035042445308 & 0.958070084890616 & 0.520964957554692 \tabularnewline
191 & 0.508452621946843 & 0.983094756106314 & 0.491547378053157 \tabularnewline
192 & 0.525412869994146 & 0.949174260011708 & 0.474587130005854 \tabularnewline
193 & 0.516161442537314 & 0.967677114925372 & 0.483838557462686 \tabularnewline
194 & 0.485351966099273 & 0.970703932198547 & 0.514648033900727 \tabularnewline
195 & 0.457547438612424 & 0.915094877224849 & 0.542452561387576 \tabularnewline
196 & 0.492815328764108 & 0.985630657528215 & 0.507184671235892 \tabularnewline
197 & 0.697394142164139 & 0.605211715671723 & 0.302605857835861 \tabularnewline
198 & 0.705509088121003 & 0.588981823757994 & 0.294490911878997 \tabularnewline
199 & 0.668872043567395 & 0.66225591286521 & 0.331127956432605 \tabularnewline
200 & 0.629352020693575 & 0.74129595861285 & 0.370647979306425 \tabularnewline
201 & 0.589026479836422 & 0.821947040327156 & 0.410973520163578 \tabularnewline
202 & 0.548228739348631 & 0.903542521302739 & 0.451771260651369 \tabularnewline
203 & 0.52354644854173 & 0.952907102916541 & 0.47645355145827 \tabularnewline
204 & 0.51932028317725 & 0.9613594336455 & 0.48067971682275 \tabularnewline
205 & 0.482068103272071 & 0.964136206544142 & 0.517931896727929 \tabularnewline
206 & 0.439445904126429 & 0.878891808252858 & 0.560554095873571 \tabularnewline
207 & 0.398252724363266 & 0.796505448726532 & 0.601747275636734 \tabularnewline
208 & 0.364843176721883 & 0.729686353443766 & 0.635156823278117 \tabularnewline
209 & 0.323766116365656 & 0.647532232731312 & 0.676233883634344 \tabularnewline
210 & 0.316432609004244 & 0.632865218008489 & 0.683567390995756 \tabularnewline
211 & 0.290911137046808 & 0.581822274093615 & 0.709088862953192 \tabularnewline
212 & 0.256579318520694 & 0.513158637041388 & 0.743420681479306 \tabularnewline
213 & 0.283707445686761 & 0.567414891373521 & 0.716292554313239 \tabularnewline
214 & 0.265942184246007 & 0.531884368492013 & 0.734057815753993 \tabularnewline
215 & 0.232972155908553 & 0.465944311817105 & 0.767027844091448 \tabularnewline
216 & 0.200263154108192 & 0.400526308216385 & 0.799736845891807 \tabularnewline
217 & 0.190139459765189 & 0.380278919530379 & 0.809860540234811 \tabularnewline
218 & 0.16049200946595 & 0.3209840189319 & 0.83950799053405 \tabularnewline
219 & 0.163496527140764 & 0.326993054281529 & 0.836503472859236 \tabularnewline
220 & 0.15515527528489 & 0.310310550569781 & 0.84484472471511 \tabularnewline
221 & 0.154119982258517 & 0.308239964517035 & 0.845880017741483 \tabularnewline
222 & 0.127537842166005 & 0.25507568433201 & 0.872462157833995 \tabularnewline
223 & 0.104921675888931 & 0.209843351777861 & 0.895078324111069 \tabularnewline
224 & 0.105839408119838 & 0.211678816239677 & 0.894160591880162 \tabularnewline
225 & 0.0957681776420125 & 0.191536355284025 & 0.904231822357987 \tabularnewline
226 & 0.0779802801942166 & 0.155960560388433 & 0.922019719805783 \tabularnewline
227 & 0.0607195064556365 & 0.121439012911273 & 0.939280493544363 \tabularnewline
228 & 0.0570000545222397 & 0.114000109044479 & 0.94299994547776 \tabularnewline
229 & 0.0771587296965824 & 0.154317459393165 & 0.922841270303418 \tabularnewline
230 & 0.0734350862483721 & 0.146870172496744 & 0.926564913751628 \tabularnewline
231 & 0.0565208800145822 & 0.113041760029164 & 0.943479119985418 \tabularnewline
232 & 0.0580221610095689 & 0.116044322019138 & 0.941977838990431 \tabularnewline
233 & 0.120774902697865 & 0.24154980539573 & 0.879225097302135 \tabularnewline
234 & 0.128517494088836 & 0.257034988177671 & 0.871482505911164 \tabularnewline
235 & 0.122187035461668 & 0.244374070923336 & 0.877812964538332 \tabularnewline
236 & 0.0951863320204125 & 0.190372664040825 & 0.904813667979588 \tabularnewline
237 & 0.123831160201272 & 0.247662320402544 & 0.876168839798728 \tabularnewline
238 & 0.168437320513799 & 0.336874641027598 & 0.831562679486201 \tabularnewline
239 & 0.2707810599278 & 0.5415621198556 & 0.7292189400722 \tabularnewline
240 & 0.269149622008336 & 0.538299244016672 & 0.730850377991664 \tabularnewline
241 & 0.223184348277678 & 0.446368696555356 & 0.776815651722322 \tabularnewline
242 & 0.271165268434971 & 0.542330536869941 & 0.728834731565029 \tabularnewline
243 & 0.211467201876447 & 0.422934403752894 & 0.788532798123553 \tabularnewline
244 & 0.158567571605887 & 0.317135143211774 & 0.841432428394113 \tabularnewline
245 & 0.235039722950064 & 0.470079445900128 & 0.764960277049936 \tabularnewline
246 & 0.256583807943986 & 0.513167615887971 & 0.743416192056014 \tabularnewline
247 & 0.189687221352382 & 0.379374442704765 & 0.810312778647618 \tabularnewline
248 & 0.234549927688996 & 0.469099855377991 & 0.765450072311004 \tabularnewline
249 & 0.344155304256334 & 0.688310608512668 & 0.655844695743666 \tabularnewline
250 & 0.2475747217756 & 0.4951494435512 & 0.7524252782244 \tabularnewline
251 & 0.278677539630078 & 0.557355079260155 & 0.721322460369922 \tabularnewline
252 & 0.909277338940007 & 0.181445322119987 & 0.0907226610599935 \tabularnewline
253 & 0.843076445680885 & 0.31384710863823 & 0.156923554319115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&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]11[/C][C]0.446327904502976[/C][C]0.892655809005952[/C][C]0.553672095497024[/C][/ROW]
[ROW][C]12[/C][C]0.952233559482318[/C][C]0.095532881035364[/C][C]0.047766440517682[/C][/ROW]
[ROW][C]13[/C][C]0.945820725044613[/C][C]0.108358549910774[/C][C]0.0541792749553868[/C][/ROW]
[ROW][C]14[/C][C]0.959772357943234[/C][C]0.0804552841135323[/C][C]0.0402276420567662[/C][/ROW]
[ROW][C]15[/C][C]0.936039214842667[/C][C]0.127921570314666[/C][C]0.0639607851573329[/C][/ROW]
[ROW][C]16[/C][C]0.901258592415804[/C][C]0.197482815168392[/C][C]0.0987414075841959[/C][/ROW]
[ROW][C]17[/C][C]0.950185365623577[/C][C]0.0996292687528455[/C][C]0.0498146343764228[/C][/ROW]
[ROW][C]18[/C][C]0.932218847449528[/C][C]0.135562305100943[/C][C]0.0677811525504715[/C][/ROW]
[ROW][C]19[/C][C]0.957108219318431[/C][C]0.0857835613631388[/C][C]0.0428917806815694[/C][/ROW]
[ROW][C]20[/C][C]0.93760453872894[/C][C]0.12479092254212[/C][C]0.0623954612710602[/C][/ROW]
[ROW][C]21[/C][C]0.926541644384307[/C][C]0.146916711231386[/C][C]0.0734583556156931[/C][/ROW]
[ROW][C]22[/C][C]0.921815293529055[/C][C]0.15636941294189[/C][C]0.0781847064709448[/C][/ROW]
[ROW][C]23[/C][C]0.894436521977472[/C][C]0.211126956045055[/C][C]0.105563478022528[/C][/ROW]
[ROW][C]24[/C][C]0.875070087231469[/C][C]0.249859825537062[/C][C]0.124929912768531[/C][/ROW]
[ROW][C]25[/C][C]0.836646193084317[/C][C]0.326707613831366[/C][C]0.163353806915683[/C][/ROW]
[ROW][C]26[/C][C]0.837723285728908[/C][C]0.324553428542185[/C][C]0.162276714271092[/C][/ROW]
[ROW][C]27[/C][C]0.848801057763905[/C][C]0.302397884472191[/C][C]0.151198942236095[/C][/ROW]
[ROW][C]28[/C][C]0.808963352659314[/C][C]0.382073294681371[/C][C]0.191036647340686[/C][/ROW]
[ROW][C]29[/C][C]0.840257378405349[/C][C]0.319485243189302[/C][C]0.159742621594651[/C][/ROW]
[ROW][C]30[/C][C]0.830767835244592[/C][C]0.338464329510816[/C][C]0.169232164755408[/C][/ROW]
[ROW][C]31[/C][C]0.836568343384203[/C][C]0.326863313231593[/C][C]0.163431656615796[/C][/ROW]
[ROW][C]32[/C][C]0.805294588691225[/C][C]0.389410822617549[/C][C]0.194705411308775[/C][/ROW]
[ROW][C]33[/C][C]0.774271678698441[/C][C]0.451456642603118[/C][C]0.225728321301559[/C][/ROW]
[ROW][C]34[/C][C]0.749787316101521[/C][C]0.500425367796957[/C][C]0.250212683898479[/C][/ROW]
[ROW][C]35[/C][C]0.716310478280647[/C][C]0.567379043438707[/C][C]0.283689521719353[/C][/ROW]
[ROW][C]36[/C][C]0.75068927930993[/C][C]0.498621441380141[/C][C]0.24931072069007[/C][/ROW]
[ROW][C]37[/C][C]0.832141663902902[/C][C]0.335716672194195[/C][C]0.167858336097098[/C][/ROW]
[ROW][C]38[/C][C]0.797017760763566[/C][C]0.405964478472869[/C][C]0.202982239236434[/C][/ROW]
[ROW][C]39[/C][C]0.763204150553137[/C][C]0.473591698893726[/C][C]0.236795849446863[/C][/ROW]
[ROW][C]40[/C][C]0.76282617844921[/C][C]0.474347643101581[/C][C]0.23717382155079[/C][/ROW]
[ROW][C]41[/C][C]0.751092677085219[/C][C]0.497814645829562[/C][C]0.248907322914781[/C][/ROW]
[ROW][C]42[/C][C]0.737533734437479[/C][C]0.524932531125041[/C][C]0.262466265562521[/C][/ROW]
[ROW][C]43[/C][C]0.790278926045066[/C][C]0.419442147909868[/C][C]0.209721073954934[/C][/ROW]
[ROW][C]44[/C][C]0.755051441099122[/C][C]0.489897117801755[/C][C]0.244948558900877[/C][/ROW]
[ROW][C]45[/C][C]0.718705769386103[/C][C]0.562588461227795[/C][C]0.281294230613897[/C][/ROW]
[ROW][C]46[/C][C]0.692863564843073[/C][C]0.614272870313854[/C][C]0.307136435156927[/C][/ROW]
[ROW][C]47[/C][C]0.748702770602859[/C][C]0.502594458794282[/C][C]0.251297229397141[/C][/ROW]
[ROW][C]48[/C][C]0.714859569168986[/C][C]0.570280861662029[/C][C]0.285140430831014[/C][/ROW]
[ROW][C]49[/C][C]0.768318004355795[/C][C]0.46336399128841[/C][C]0.231681995644205[/C][/ROW]
[ROW][C]50[/C][C]0.742676520775781[/C][C]0.514646958448439[/C][C]0.257323479224219[/C][/ROW]
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[ROW][C]172[/C][C]0.340163819753504[/C][C]0.680327639507009[/C][C]0.659836180246496[/C][/ROW]
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[ROW][C]186[/C][C]0.541229491383579[/C][C]0.917541017232843[/C][C]0.458770508616421[/C][/ROW]
[ROW][C]187[/C][C]0.509243113880653[/C][C]0.981513772238693[/C][C]0.490756886119347[/C][/ROW]
[ROW][C]188[/C][C]0.476516714180243[/C][C]0.953033428360487[/C][C]0.523483285819757[/C][/ROW]
[ROW][C]189[/C][C]0.440280459005749[/C][C]0.880560918011497[/C][C]0.559719540994251[/C][/ROW]
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[ROW][C]191[/C][C]0.508452621946843[/C][C]0.983094756106314[/C][C]0.491547378053157[/C][/ROW]
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[ROW][C]195[/C][C]0.457547438612424[/C][C]0.915094877224849[/C][C]0.542452561387576[/C][/ROW]
[ROW][C]196[/C][C]0.492815328764108[/C][C]0.985630657528215[/C][C]0.507184671235892[/C][/ROW]
[ROW][C]197[/C][C]0.697394142164139[/C][C]0.605211715671723[/C][C]0.302605857835861[/C][/ROW]
[ROW][C]198[/C][C]0.705509088121003[/C][C]0.588981823757994[/C][C]0.294490911878997[/C][/ROW]
[ROW][C]199[/C][C]0.668872043567395[/C][C]0.66225591286521[/C][C]0.331127956432605[/C][/ROW]
[ROW][C]200[/C][C]0.629352020693575[/C][C]0.74129595861285[/C][C]0.370647979306425[/C][/ROW]
[ROW][C]201[/C][C]0.589026479836422[/C][C]0.821947040327156[/C][C]0.410973520163578[/C][/ROW]
[ROW][C]202[/C][C]0.548228739348631[/C][C]0.903542521302739[/C][C]0.451771260651369[/C][/ROW]
[ROW][C]203[/C][C]0.52354644854173[/C][C]0.952907102916541[/C][C]0.47645355145827[/C][/ROW]
[ROW][C]204[/C][C]0.51932028317725[/C][C]0.9613594336455[/C][C]0.48067971682275[/C][/ROW]
[ROW][C]205[/C][C]0.482068103272071[/C][C]0.964136206544142[/C][C]0.517931896727929[/C][/ROW]
[ROW][C]206[/C][C]0.439445904126429[/C][C]0.878891808252858[/C][C]0.560554095873571[/C][/ROW]
[ROW][C]207[/C][C]0.398252724363266[/C][C]0.796505448726532[/C][C]0.601747275636734[/C][/ROW]
[ROW][C]208[/C][C]0.364843176721883[/C][C]0.729686353443766[/C][C]0.635156823278117[/C][/ROW]
[ROW][C]209[/C][C]0.323766116365656[/C][C]0.647532232731312[/C][C]0.676233883634344[/C][/ROW]
[ROW][C]210[/C][C]0.316432609004244[/C][C]0.632865218008489[/C][C]0.683567390995756[/C][/ROW]
[ROW][C]211[/C][C]0.290911137046808[/C][C]0.581822274093615[/C][C]0.709088862953192[/C][/ROW]
[ROW][C]212[/C][C]0.256579318520694[/C][C]0.513158637041388[/C][C]0.743420681479306[/C][/ROW]
[ROW][C]213[/C][C]0.283707445686761[/C][C]0.567414891373521[/C][C]0.716292554313239[/C][/ROW]
[ROW][C]214[/C][C]0.265942184246007[/C][C]0.531884368492013[/C][C]0.734057815753993[/C][/ROW]
[ROW][C]215[/C][C]0.232972155908553[/C][C]0.465944311817105[/C][C]0.767027844091448[/C][/ROW]
[ROW][C]216[/C][C]0.200263154108192[/C][C]0.400526308216385[/C][C]0.799736845891807[/C][/ROW]
[ROW][C]217[/C][C]0.190139459765189[/C][C]0.380278919530379[/C][C]0.809860540234811[/C][/ROW]
[ROW][C]218[/C][C]0.16049200946595[/C][C]0.3209840189319[/C][C]0.83950799053405[/C][/ROW]
[ROW][C]219[/C][C]0.163496527140764[/C][C]0.326993054281529[/C][C]0.836503472859236[/C][/ROW]
[ROW][C]220[/C][C]0.15515527528489[/C][C]0.310310550569781[/C][C]0.84484472471511[/C][/ROW]
[ROW][C]221[/C][C]0.154119982258517[/C][C]0.308239964517035[/C][C]0.845880017741483[/C][/ROW]
[ROW][C]222[/C][C]0.127537842166005[/C][C]0.25507568433201[/C][C]0.872462157833995[/C][/ROW]
[ROW][C]223[/C][C]0.104921675888931[/C][C]0.209843351777861[/C][C]0.895078324111069[/C][/ROW]
[ROW][C]224[/C][C]0.105839408119838[/C][C]0.211678816239677[/C][C]0.894160591880162[/C][/ROW]
[ROW][C]225[/C][C]0.0957681776420125[/C][C]0.191536355284025[/C][C]0.904231822357987[/C][/ROW]
[ROW][C]226[/C][C]0.0779802801942166[/C][C]0.155960560388433[/C][C]0.922019719805783[/C][/ROW]
[ROW][C]227[/C][C]0.0607195064556365[/C][C]0.121439012911273[/C][C]0.939280493544363[/C][/ROW]
[ROW][C]228[/C][C]0.0570000545222397[/C][C]0.114000109044479[/C][C]0.94299994547776[/C][/ROW]
[ROW][C]229[/C][C]0.0771587296965824[/C][C]0.154317459393165[/C][C]0.922841270303418[/C][/ROW]
[ROW][C]230[/C][C]0.0734350862483721[/C][C]0.146870172496744[/C][C]0.926564913751628[/C][/ROW]
[ROW][C]231[/C][C]0.0565208800145822[/C][C]0.113041760029164[/C][C]0.943479119985418[/C][/ROW]
[ROW][C]232[/C][C]0.0580221610095689[/C][C]0.116044322019138[/C][C]0.941977838990431[/C][/ROW]
[ROW][C]233[/C][C]0.120774902697865[/C][C]0.24154980539573[/C][C]0.879225097302135[/C][/ROW]
[ROW][C]234[/C][C]0.128517494088836[/C][C]0.257034988177671[/C][C]0.871482505911164[/C][/ROW]
[ROW][C]235[/C][C]0.122187035461668[/C][C]0.244374070923336[/C][C]0.877812964538332[/C][/ROW]
[ROW][C]236[/C][C]0.0951863320204125[/C][C]0.190372664040825[/C][C]0.904813667979588[/C][/ROW]
[ROW][C]237[/C][C]0.123831160201272[/C][C]0.247662320402544[/C][C]0.876168839798728[/C][/ROW]
[ROW][C]238[/C][C]0.168437320513799[/C][C]0.336874641027598[/C][C]0.831562679486201[/C][/ROW]
[ROW][C]239[/C][C]0.2707810599278[/C][C]0.5415621198556[/C][C]0.7292189400722[/C][/ROW]
[ROW][C]240[/C][C]0.269149622008336[/C][C]0.538299244016672[/C][C]0.730850377991664[/C][/ROW]
[ROW][C]241[/C][C]0.223184348277678[/C][C]0.446368696555356[/C][C]0.776815651722322[/C][/ROW]
[ROW][C]242[/C][C]0.271165268434971[/C][C]0.542330536869941[/C][C]0.728834731565029[/C][/ROW]
[ROW][C]243[/C][C]0.211467201876447[/C][C]0.422934403752894[/C][C]0.788532798123553[/C][/ROW]
[ROW][C]244[/C][C]0.158567571605887[/C][C]0.317135143211774[/C][C]0.841432428394113[/C][/ROW]
[ROW][C]245[/C][C]0.235039722950064[/C][C]0.470079445900128[/C][C]0.764960277049936[/C][/ROW]
[ROW][C]246[/C][C]0.256583807943986[/C][C]0.513167615887971[/C][C]0.743416192056014[/C][/ROW]
[ROW][C]247[/C][C]0.189687221352382[/C][C]0.379374442704765[/C][C]0.810312778647618[/C][/ROW]
[ROW][C]248[/C][C]0.234549927688996[/C][C]0.469099855377991[/C][C]0.765450072311004[/C][/ROW]
[ROW][C]249[/C][C]0.344155304256334[/C][C]0.688310608512668[/C][C]0.655844695743666[/C][/ROW]
[ROW][C]250[/C][C]0.2475747217756[/C][C]0.4951494435512[/C][C]0.7524252782244[/C][/ROW]
[ROW][C]251[/C][C]0.278677539630078[/C][C]0.557355079260155[/C][C]0.721322460369922[/C][/ROW]
[ROW][C]252[/C][C]0.909277338940007[/C][C]0.181445322119987[/C][C]0.0907226610599935[/C][/ROW]
[ROW][C]253[/C][C]0.843076445680885[/C][C]0.31384710863823[/C][C]0.156923554319115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4463279045029760.8926558090059520.553672095497024
120.9522335594823180.0955328810353640.047766440517682
130.9458207250446130.1083585499107740.0541792749553868
140.9597723579432340.08045528411353230.0402276420567662
150.9360392148426670.1279215703146660.0639607851573329
160.9012585924158040.1974828151683920.0987414075841959
170.9501853656235770.09962926875284550.0498146343764228
180.9322188474495280.1355623051009430.0677811525504715
190.9571082193184310.08578356136313880.0428917806815694
200.937604538728940.124790922542120.0623954612710602
210.9265416443843070.1469167112313860.0734583556156931
220.9218152935290550.156369412941890.0781847064709448
230.8944365219774720.2111269560450550.105563478022528
240.8750700872314690.2498598255370620.124929912768531
250.8366461930843170.3267076138313660.163353806915683
260.8377232857289080.3245534285421850.162276714271092
270.8488010577639050.3023978844721910.151198942236095
280.8089633526593140.3820732946813710.191036647340686
290.8402573784053490.3194852431893020.159742621594651
300.8307678352445920.3384643295108160.169232164755408
310.8365683433842030.3268633132315930.163431656615796
320.8052945886912250.3894108226175490.194705411308775
330.7742716786984410.4514566426031180.225728321301559
340.7497873161015210.5004253677969570.250212683898479
350.7163104782806470.5673790434387070.283689521719353
360.750689279309930.4986214413801410.24931072069007
370.8321416639029020.3357166721941950.167858336097098
380.7970177607635660.4059644784728690.202982239236434
390.7632041505531370.4735916988937260.236795849446863
400.762826178449210.4743476431015810.23717382155079
410.7510926770852190.4978146458295620.248907322914781
420.7375337344374790.5249325311250410.262466265562521
430.7902789260450660.4194421479098680.209721073954934
440.7550514410991220.4898971178017550.244948558900877
450.7187057693861030.5625884612277950.281294230613897
460.6928635648430730.6142728703138540.307136435156927
470.7487027706028590.5025944587942820.251297229397141
480.7148595691689860.5702808616620290.285140430831014
490.7683180043557950.463363991288410.231681995644205
500.7426765207757810.5146469584484390.257323479224219
510.7306888053118480.5386223893763030.269311194688152
520.6963511996007540.6072976007984930.303648800399246
530.6985556677843220.6028886644313550.301444332215678
540.6661088505996860.6677822988006280.333891149400314
550.6781576288469330.6436847423061330.321842371153067
560.6939825138123940.6120349723752120.306017486187606
570.6716192299567770.6567615400864470.328380770043223
580.7083308756372490.5833382487255010.291669124362751
590.7233295430206730.5533409139586540.276670456979327
600.7001469331438060.5997061337123890.299853066856194
610.7122493527147730.5755012945704530.287750647285227
620.6757692867682580.6484614264634850.324230713231742
630.6520898764601470.6958202470797060.347910123539853
640.6120835992326910.7758328015346180.387916400767309
650.5711704408992630.8576591182014740.428829559100737
660.5813684543883610.8372630912232790.418631545611639
670.5841118642724080.8317762714551840.415888135727592
680.5542636364549260.8914727270901480.445736363545074
690.5135644480605630.9728711038788750.486435551939437
700.4895370834587810.9790741669175620.510462916541219
710.4507582157643520.9015164315287040.549241784235648
720.4269952860384310.8539905720768620.573004713961569
730.4002773084587880.8005546169175750.599722691541212
740.3793882995555750.7587765991111490.620611700444425
750.3495254783122040.6990509566244080.650474521687796
760.5113807088215080.9772385823569840.488619291178492
770.4901661626436220.9803323252872440.509833837356378
780.5064315491175930.9871369017648140.493568450882407
790.4681799037321270.9363598074642540.531820096267873
800.5661954074955770.8676091850088470.433804592504423
810.529343775063850.9413124498722990.47065622493615
820.5534282633448910.8931434733102170.446571736655108
830.517041460989180.9659170780216390.48295853901082
840.4790579175051880.9581158350103760.520942082494812
850.4411858671171260.8823717342342510.558814132882874
860.4076287078188250.8152574156376510.592371292181175
870.3757107706130030.7514215412260060.624289229386997
880.3426718404698750.6853436809397490.657328159530125
890.4330312208022110.8660624416044210.566968779197789
900.4035111459534760.8070222919069520.596488854046524
910.3749560808269720.7499121616539430.625043919173028
920.3446894256757470.6893788513514940.655310574324253
930.3444014501019990.6888029002039980.655598549898001
940.3131897596974130.6263795193948260.686810240302587
950.2804810772852710.5609621545705430.719518922714729
960.2645877308376680.5291754616753360.735412269162332
970.2419151873073960.4838303746147910.758084812692605
980.2154561424731440.4309122849462880.784543857526856
990.2074787967542140.4149575935084270.792521203245786
1000.1823278790564770.3646557581129540.817672120943523
1010.171849907758240.3436998155164790.82815009224176
1020.1537133562165160.3074267124330320.846286643783484
1030.1940613467901840.3881226935803670.805938653209817
1040.2249135884204760.4498271768409520.775086411579524
1050.233729419291150.46745883858230.76627058070885
1060.2067241767188610.4134483534377230.793275823281139
1070.2318868631907560.4637737263815110.768113136809244
1080.2154442016107960.4308884032215920.784555798389204
1090.3440330705239470.6880661410478950.655966929476053
1100.3353864076125260.6707728152250530.664613592387474
1110.3046271747159020.6092543494318050.695372825284098
1120.2935575170452530.5871150340905070.706442482954747
1130.2693810554387190.5387621108774370.730618944561281
1140.2505102698888720.5010205397777450.749489730111128
1150.2235721227080250.4471442454160510.776427877291975
1160.1975902596189850.3951805192379690.802409740381016
1170.1767884547438580.3535769094877150.823211545256142
1180.2154226031821260.4308452063642520.784577396817874
1190.2213570659079680.4427141318159350.778642934092032
1200.1974302529136390.3948605058272780.802569747086361
1210.1823231958038380.3646463916076760.817676804196162
1220.161451860678020.322903721356040.83854813932198
1230.1906433061254650.3812866122509290.809356693874535
1240.1716404775115890.3432809550231790.828359522488411
1250.1619494256583630.3238988513167270.838050574341637
1260.1507609962400750.3015219924801510.849239003759925
1270.1325745175190820.2651490350381630.867425482480918
1280.1182261000678350.2364522001356710.881773899932165
1290.1018591543258420.2037183086516840.898140845674158
1300.1008898027982110.2017796055964220.899110197201789
1310.1025617069879720.2051234139759430.897438293012028
1320.09454795021400080.1890959004280020.905452049785999
1330.08634471828627740.1726894365725550.913655281713723
1340.0733799400048260.1467598800096520.926620059995174
1350.07485014738019420.1497002947603880.925149852619806
1360.1492236745265770.2984473490531540.850776325473423
1370.1300289751445170.2600579502890350.869971024855483
1380.1152080566027980.2304161132055950.884791943397202
1390.1103981390584470.2207962781168940.889601860941553
1400.1051487826693090.2102975653386180.894851217330691
1410.1177754119494420.2355508238988830.882224588050558
1420.1185327024955460.2370654049910920.881467297504454
1430.101943280990130.203886561980260.89805671900987
1440.08679032564508760.1735806512901750.913209674354912
1450.07390058715235920.1478011743047180.926099412847641
1460.06261175131783360.1252235026356670.937388248682166
1470.06863007274242170.1372601454848430.931369927257578
1480.07478567499495480.149571349989910.925214325005045
1490.070274199711610.140548399423220.92972580028839
1500.05970817587303670.1194163517460730.940291824126963
1510.07280106342161180.1456021268432240.927198936578388
1520.06989169674601020.139783393492020.93010830325399
1530.06854252237925130.1370850447585030.931457477620749
1540.06373037303029060.1274607460605810.936269626969709
1550.07091898407611370.1418379681522270.929081015923886
1560.06046151681820280.1209230336364060.939538483181797
1570.05050120659676420.1010024131935280.949498793403236
1580.04306938929076860.08613877858153720.956930610709231
1590.05236391368620410.1047278273724080.947636086313796
1600.06526640303742340.1305328060748470.934733596962577
1610.05582610266015090.1116522053203020.944173897339849
1620.1230237884097330.2460475768194660.876976211590267
1630.1124770216894410.2249540433788820.887522978310559
1640.3959741345179040.7919482690358080.604025865482096
1650.378885954738160.7577719094763190.62111404526184
1660.4870899917554890.9741799835109770.512910008244511
1670.4550028797796760.9100057595593520.544997120220324
1680.4232520557689960.8465041115379920.576747944231004
1690.3870156930575490.7740313861150970.612984306942451
1700.3651125562784470.7302251125568950.634887443721553
1710.3749106550169230.7498213100338470.625089344983077
1720.3401638197535040.6803276395070090.659836180246496
1730.3835019050238340.7670038100476680.616498094976166
1740.3707500871152640.7415001742305290.629249912884736
1750.3485076478747460.6970152957494910.651492352125254
1760.3850932120296340.7701864240592680.614906787970366
1770.3591536525917160.7183073051834330.640846347408284
1780.3716937705375330.7433875410750670.628306229462467
1790.3568329159571060.7136658319142130.643167084042894
1800.3245210691516460.6490421383032920.675478930848354
1810.3519327704173890.7038655408347770.648067229582611
1820.4570749952548210.9141499905096420.542925004745179
1830.4421090672556160.8842181345112330.557890932744384
1840.430623002046470.861246004092940.56937699795353
1850.4140617383990580.8281234767981150.585938261600942
1860.5412294913835790.9175410172328430.458770508616421
1870.5092431138806530.9815137722386930.490756886119347
1880.4765167141802430.9530334283604870.523483285819757
1890.4402804590057490.8805609180114970.559719540994251
1900.4790350424453080.9580700848906160.520964957554692
1910.5084526219468430.9830947561063140.491547378053157
1920.5254128699941460.9491742600117080.474587130005854
1930.5161614425373140.9676771149253720.483838557462686
1940.4853519660992730.9707039321985470.514648033900727
1950.4575474386124240.9150948772248490.542452561387576
1960.4928153287641080.9856306575282150.507184671235892
1970.6973941421641390.6052117156717230.302605857835861
1980.7055090881210030.5889818237579940.294490911878997
1990.6688720435673950.662255912865210.331127956432605
2000.6293520206935750.741295958612850.370647979306425
2010.5890264798364220.8219470403271560.410973520163578
2020.5482287393486310.9035425213027390.451771260651369
2030.523546448541730.9529071029165410.47645355145827
2040.519320283177250.96135943364550.48067971682275
2050.4820681032720710.9641362065441420.517931896727929
2060.4394459041264290.8788918082528580.560554095873571
2070.3982527243632660.7965054487265320.601747275636734
2080.3648431767218830.7296863534437660.635156823278117
2090.3237661163656560.6475322327313120.676233883634344
2100.3164326090042440.6328652180084890.683567390995756
2110.2909111370468080.5818222740936150.709088862953192
2120.2565793185206940.5131586370413880.743420681479306
2130.2837074456867610.5674148913735210.716292554313239
2140.2659421842460070.5318843684920130.734057815753993
2150.2329721559085530.4659443118171050.767027844091448
2160.2002631541081920.4005263082163850.799736845891807
2170.1901394597651890.3802789195303790.809860540234811
2180.160492009465950.32098401893190.83950799053405
2190.1634965271407640.3269930542815290.836503472859236
2200.155155275284890.3103105505697810.84484472471511
2210.1541199822585170.3082399645170350.845880017741483
2220.1275378421660050.255075684332010.872462157833995
2230.1049216758889310.2098433517778610.895078324111069
2240.1058394081198380.2116788162396770.894160591880162
2250.09576817764201250.1915363552840250.904231822357987
2260.07798028019421660.1559605603884330.922019719805783
2270.06071950645563650.1214390129112730.939280493544363
2280.05700005452223970.1140001090444790.94299994547776
2290.07715872969658240.1543174593931650.922841270303418
2300.07343508624837210.1468701724967440.926564913751628
2310.05652088001458220.1130417600291640.943479119985418
2320.05802216100956890.1160443220191380.941977838990431
2330.1207749026978650.241549805395730.879225097302135
2340.1285174940888360.2570349881776710.871482505911164
2350.1221870354616680.2443740709233360.877812964538332
2360.09518633202041250.1903726640408250.904813667979588
2370.1238311602012720.2476623204025440.876168839798728
2380.1684373205137990.3368746410275980.831562679486201
2390.27078105992780.54156211985560.7292189400722
2400.2691496220083360.5382992440166720.730850377991664
2410.2231843482776780.4463686965553560.776815651722322
2420.2711652684349710.5423305368699410.728834731565029
2430.2114672018764470.4229344037528940.788532798123553
2440.1585675716058870.3171351432117740.841432428394113
2450.2350397229500640.4700794459001280.764960277049936
2460.2565838079439860.5131676158879710.743416192056014
2470.1896872213523820.3793744427047650.810312778647618
2480.2345499276889960.4690998553779910.765450072311004
2490.3441553042563340.6883106085126680.655844695743666
2500.24757472177560.49514944355120.7524252782244
2510.2786775396300780.5573550792601550.721322460369922
2520.9092773389400070.1814453221199870.0907226610599935
2530.8430764456808850.313847108638230.156923554319115







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.0205761316872428OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 5 & 0.0205761316872428 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253993&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0205761316872428[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253993&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253993&T=6

As an alternative you can also use a QR Code:  

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 level00OK
5% type I error level00OK
10% type I error level50.0205761316872428OK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}