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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 27 Dec 2010 21:14:12 +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/2010/Dec/27/t129348432511rsgpd3zq4vu0o.htm/, Retrieved Mon, 06 May 2024 15:50:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116147, Retrieved Mon, 06 May 2024 15:50:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-26 21:43:16] [dcd1a35a8985187cb1e9de87792355b2]
-   PD    [Multiple Regression] [] [2010-12-26 22:27:50] [dcd1a35a8985187cb1e9de87792355b2]
-             [Multiple Regression] [] [2010-12-27 21:14:12] [4cd8ab5c565aadc5ce193fb2faa1c53a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	27	27	5	5	26	26	49	49	35	35	40
1	36	36	4	4	25	25	45	45	34	34	45
1	25	25	4	4	17	17	54	54	13	13	38
1	27	27	3	3	37	37	36	36	35	35	28
1	50	50	4	4	27	27	46	46	35	35	39
1	41	41	4	4	36	36	42	42	36	36	37
1	48	48	5	5	25	25	41	41	27	27	30
1	44	44	2	2	29	29	45	45	29	29	29
1	28	28	3	3	26	26	42	42	15	15	39
1	56	56	3	3	24	24	45	45	33	33	35
1	50	50	5	5	29	29	43	43	32	32	34
1	47	47	4	4	26	26	45	45	21	21	38
1	52	52	2	2	21	21	42	42	25	25	21
1	45	45	4	4	21	21	47	47	22	22	35
1			3	3	30	30	41	41	26	26	36
1	52	52	4	4	21	21	44	44	34	34	
1	46	46	2	2	29	29	51	51	34	34	37
1	58	58	3	3	28	28	46	46	36	36	37
1	54	54	5	5	19	19	47	47	36	36	37
1	29	29	3	3	26	26	46	46	26	26	32
1	43	43	2	2	34	34	50	50	34	34	31
1	45	45	3	3	24	24	51	51	33	33	42
1	46	46	5	5	20	20	47	47	37	37	31
1	25	25	4	4	21	21	46	46	29	29	44
1	47	47	2	2	33	33	43	43	35	35	35
1	41	41	3	3	22	22	55	55	28	28	32
1	29	29	4	4	18	18	52	52	25	25	38
1	45	45	5	5	20	20	56	56	32	32	40
1	54	54	2	2	26	26	46	46	27	27	45
1	28	28	4	4	23	23	51	51	27	27	42
1	37	37	3	3	34	34	39	39	31	31	34
1	56	56	2	2	25	25	45	45	16	16	11
1	43	43	3	3	33	33	31	31	25	25	35
1	34	34	3	3	46	46	29	29	27	27	39
1	42	42	3	3	24	24	48	48	32	32	32
1	46	46	3	3	42	42	32	32	25	25	18
1	25	25	4	4	30	30	45	45	27	27	34
1	25	25	5	5	19	19	33	33	29	29	34
1	25	25	3	3	37	37	40	40	28	28	28
1	48	48	4	4	23	23	44	44	32	32	30
1	27	27	5	5	34	34	46	46	29	29	36
1	28	28	3	3	38	38	39	39	32	32	40
1	25	25	1	1	26	26	39	39	16	16	22
1	26	26	3	3	36	36	41	41	26	26	28
1	51	51	3	3	20	20	52	52	32	32	34
1	29	29	3	3	27	27	42	42	24	24	23
1	29	29	4	4	27	27	44	44	26	26	29
1	43	43	2	2	41	41	33	33	19	19	35
1	44	44	3	3	33	33	46	46	25	25	36
1	25	25	3	3	37	37	45	45	24	24	32
1	51	51	2	2	30	30	40	40	23	23	35
1	42	42	5	5	20	20	48	48	28	28	45
1	25	25	4	4	20	20	53	53	28	28	41
1	51	51	3	3	31	31	45	45	26	26	37
1	46	46	5	5	32	32	46	46	34	34	33
1	29	29			22	22	37	37	30	30	40
1	38	38	3	3	25	25	46	46	30	30	
1	41	41	1	1	27	27	20	20	15	15	13
1	44	44	4	4	20	20	55	55	31	31	
1	41	41	5	5	40	40	40	40	33	33	38
1	46	46	4	4	33	33	40	40	27	27	32
1	41	41	4	4	24	24	45	45	31	31	24
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1	55	55	5	5	28	28	39	39	41	41	28
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1			3	3	26	26	46	46	28	28	30
1	52	52	3	3	30	30	39	39	45	45	43
1			4	4	31	31	35	35	25	25	24
1			3	3	21	21	45	45	29	29	27
1	38	38	5	5	28	28	34	34	25	25	18
1	32	32	5	5	29	29	41	41	27	27	38
1	44	44	4	4	36	36	45	45	24	24	
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1	44	44	3	3	27	27	33	33	23	23	24
1	42	42	3	3	32	32	45	45	34	34	39
1	45	45	5	5	26	26	44	44	32	32	33
1	45	45	2	2	29	29	44	44	24	24	35
1	50	50	5	5	28	28	40	40	34	34	26
1	49	49	3	3	19	19	48	48	33	33	32
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2	43	0	4	0	30	0	45	0	29	0	30
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2	47	0	5	0	30	0	40	0	28	0	31
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2	37	0	5	0	35	0	51	0	32	0	33
2	41	0	3	0	23	0	44	0	28	0	27
2	26	0	4	0	28	0	47	0	25	0	33
2	50	0	3	0	33	0	38	0	26	0	25
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2	47	0	2	0	40	0	53	0	20	0	30
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2	45	0	5	0	28	0	41	0	26	0	27
2	40	0	3	0	46	0	33	0	33	0	31
2	34	0	5	0	22	0	42	0	29	0	41
2	52	0	3	0	25	0	46	0	35	0	25
2	41	0	4	0	31	0	33	0	24	0	19
2	48	0	3	0	21	0	51	0	31	0	33
2	45	0	3	0	23	0	46	0	29	0	27
2	25	0	3	0	34	0	50	0	29	0	27
2	26	0	4	0	31	0	46	0	29	0	30
2	50	0	4	0	31	0	48	0	34	0	21
2	48	0	4	0	26	0	44	0	32	0	32
2	51	0	3	0	36	0	38	0	31	0	31
2	53	0	3	0	28	0	42	0	31	0	36
2	32	0	3	0	32	0	38	0	28	0	28
2	31	0	5	0	33	0	55	0	25	0	45
2	30	0	5	0	17	0	51	0	36	0	35
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2	33	0	4	0	40	0	47	0	36	0	36
2	21	0	4	0	33	0	49	0	32	0	38
2	36	0	5	0	35	0	46	0	29	0	28
2	50	0	3	0	23	0	42	0	31	0	23
2	48	0	3	0	15	0	56	0	34	0	37
2	48	0	2	0	38	0	35	0	27	0	29
2	49	0	5	0	41	0	46	0	33	0	24
2	43	0	2	0	45	0	35	0	35	0	37
2	48	0	3	0	27	0	48	0	33	0	27
2	48	0	4	0	46	0	42	0	27	0	25
2	49	0	4	0	44	0	39	0	32	0	21
2	25	0	4	0	44	0	45	0	38	0	32
2	46	0	3	0	30	0	42	0	37	0	31
2	53	0	5	0	44	0	32	0	38	0	29
2	49	0	2	0	33	0	39	0	28	0	36
2	20	0	3	0	23	0	36	0	21	0	25
2	44	0	3	0	33	0	38	0	35	0	36
2	38	0	4	0	33	0	49	0	31	0	34
2	42	0	4	0	25	0	43	0	30	0	32
2	46	0	4	0	16	0	48	0	24	0	27
2	49	0	2	0	36	0	45	0	27	0	24
2	51	0	3	0	35	0	32	0	26	0	26
2	47	0	3	0	32	0	42	0	28	0	22
2	44	0	3	0	36	0	45	0	34	0	29
2	47	0	3	0	51	0	29	0	29	0	30
2	46	0	3	0	30	0	51	0	26	0	24
2	28	0	3	0	29	0	50	0	28	0	26
2	47	0	4	0	26	0	44	0	33	0	37
2	28	0	4	0	20	0	41	0	32	0	36
2	45	0	4	0	29	0	47	0	31	0	34
2	46	0	4	0	32	0	42	0	37	0	35
2	22	0	3	0	32	0	51	0	19	0	44
2	33	0	3	0	34	0	43	0	27	0	40
2	47	0	5	0	25	0	41	0	38	0	36
2	42	0	3	0	39	0	37	0	35	0	28
2	47	0	3	0	21	0	46	0	35	0	18
2	50	0	3	0	38	0	38	0	30	0	23
2	49	0	4	0	25	0	21	0	21	0	20
2	46	0	4	0	38	0	31	0	33	0	37
2	45	0	3	0	31	0	49	0	29	0	33
2	52	0	3	0	27	0	40	0	31	0	43
2	40	0	4	0	26	0	46	0	31	0	22
2	49	0	4	0	37	0	45	0	31	0	28
2	46	0	4	0	33	0	43	0	26	0	23
2	32	0	3	0	41	0	45	0	26	0	38
2	41	0	3	0	19	0	48	0	23	0	21
2	43	0	3	0	37	0	43	0	27	0	25
2	28	0	3	0	33	0	34	0	24	0	25
2	45	0	5	0	27	0	55	0	24	0	39
2	43	0	3	0	37	0	43	0	35	0	25
2	47	0	4	0	34	0	44	0	22	0	20
2	52	0	4	0	27	0	44	0	34	0	34
2	40	0	2	0	37	0	41	0	28	0	22
2	48	0	3	0	31	0	46	0	29	0	39
2	51	0	3	0	42	0	49	0	38	0	35
2	49	0	4	0	33	0	55	0	24	0	21
2	31	0	4	0	39	0	51	0	25	0	27
2	43	0	3	0	27	0	46	0	37	0	31
2	31	0	3	0	35	0	37	0	33	0	20
2	28	0	4	0	23	0	43	0	30	0	28
2	43	0	4	0	32	0	41	0	22	0	26
2	31	0	3	0	22	0	45	0	28	0	36
2	51	0	3	0	17	0	39	0	24	0	16
2	58	0	4	0	35	0	38	0	33	0	34
2	25	0	5	0	34	0	41	0	37	0	30

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 18 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&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]18 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Intrinsieke_waarden[t] = -24.8966504105950 + 0.456806170205304geslacht[t] + 0.0318319415995283leeftijd[t] + 0.318881581238335leeftijd_man[t] + 0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] + 0.567135811953931Neuroticisme[t] + 0.0966406034130302Neuroticisme_man[t] + 0.114508410025917Extraversie[t] + 0.267732438537325Extraversie_man[t] -0.0326812144046273Openheid[t] + 0.305178309477321Openheid_man[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intrinsieke_waarden[t] =  -24.8966504105950 +  0.456806170205304geslacht[t] +  0.0318319415995283leeftijd[t] +  0.318881581238335leeftijd_man[t] +  0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] +  0.567135811953931Neuroticisme[t] +  0.0966406034130302Neuroticisme_man[t] +  0.114508410025917Extraversie[t] +  0.267732438537325Extraversie_man[t] -0.0326812144046273Openheid[t] +  0.305178309477321Openheid_man[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intrinsieke_waarden[t] =  -24.8966504105950 +  0.456806170205304geslacht[t] +  0.0318319415995283leeftijd[t] +  0.318881581238335leeftijd_man[t] +  0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] +  0.567135811953931Neuroticisme[t] +  0.0966406034130302Neuroticisme_man[t] +  0.114508410025917Extraversie[t] +  0.267732438537325Extraversie_man[t] -0.0326812144046273Openheid[t] +  0.305178309477321Openheid_man[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Intrinsieke_waarden[t] = -24.8966504105950 + 0.456806170205304geslacht[t] + 0.0318319415995283leeftijd[t] + 0.318881581238335leeftijd_man[t] + 0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] + 0.567135811953931Neuroticisme[t] + 0.0966406034130302Neuroticisme_man[t] + 0.114508410025917Extraversie[t] + 0.267732438537325Extraversie_man[t] -0.0326812144046273Openheid[t] + 0.305178309477321Openheid_man[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-24.89665041059504.424988-5.626400
geslacht0.4568061702053040.0663426.885600
leeftijd0.03183194159952830.0796830.39950.6900040.345002
leeftijd_man0.3188815812383350.0923323.45370.0006870.000344
opleiding0.1831195755878830.0789292.32010.0214410.010721
opleiding_man-0.1353537350166680.0883-1.53290.127030.063515
Neuroticisme0.5671358119539310.0726477.806700
Neuroticisme_man0.09664060341303020.0936351.03210.3033870.151694
Extraversie0.1145084100259170.0875611.30780.1925940.096297
Extraversie_man0.2677324385373250.1009382.65250.0086930.004346
Openheid-0.03268121440462730.073388-0.44530.6566130.328307
Openheid_man0.3051783094773210.0680794.48271.3e-056e-06

\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) & -24.8966504105950 & 4.424988 & -5.6264 & 0 & 0 \tabularnewline
geslacht & 0.456806170205304 & 0.066342 & 6.8856 & 0 & 0 \tabularnewline
leeftijd & 0.0318319415995283 & 0.079683 & 0.3995 & 0.690004 & 0.345002 \tabularnewline
leeftijd_man & 0.318881581238335 & 0.092332 & 3.4537 & 0.000687 & 0.000344 \tabularnewline
opleiding & 0.183119575587883 & 0.078929 & 2.3201 & 0.021441 & 0.010721 \tabularnewline
opleiding_man & -0.135353735016668 & 0.0883 & -1.5329 & 0.12703 & 0.063515 \tabularnewline
Neuroticisme & 0.567135811953931 & 0.072647 & 7.8067 & 0 & 0 \tabularnewline
Neuroticisme_man & 0.0966406034130302 & 0.093635 & 1.0321 & 0.303387 & 0.151694 \tabularnewline
Extraversie & 0.114508410025917 & 0.087561 & 1.3078 & 0.192594 & 0.096297 \tabularnewline
Extraversie_man & 0.267732438537325 & 0.100938 & 2.6525 & 0.008693 & 0.004346 \tabularnewline
Openheid & -0.0326812144046273 & 0.073388 & -0.4453 & 0.656613 & 0.328307 \tabularnewline
Openheid_man & 0.305178309477321 & 0.068079 & 4.4827 & 1.3e-05 & 6e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&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]-24.8966504105950[/C][C]4.424988[/C][C]-5.6264[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]0.456806170205304[/C][C]0.066342[/C][C]6.8856[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]0.0318319415995283[/C][C]0.079683[/C][C]0.3995[/C][C]0.690004[/C][C]0.345002[/C][/ROW]
[ROW][C]leeftijd_man[/C][C]0.318881581238335[/C][C]0.092332[/C][C]3.4537[/C][C]0.000687[/C][C]0.000344[/C][/ROW]
[ROW][C]opleiding[/C][C]0.183119575587883[/C][C]0.078929[/C][C]2.3201[/C][C]0.021441[/C][C]0.010721[/C][/ROW]
[ROW][C]opleiding_man[/C][C]-0.135353735016668[/C][C]0.0883[/C][C]-1.5329[/C][C]0.12703[/C][C]0.063515[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.567135811953931[/C][C]0.072647[/C][C]7.8067[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme_man[/C][C]0.0966406034130302[/C][C]0.093635[/C][C]1.0321[/C][C]0.303387[/C][C]0.151694[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.114508410025917[/C][C]0.087561[/C][C]1.3078[/C][C]0.192594[/C][C]0.096297[/C][/ROW]
[ROW][C]Extraversie_man[/C][C]0.267732438537325[/C][C]0.100938[/C][C]2.6525[/C][C]0.008693[/C][C]0.004346[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.0326812144046273[/C][C]0.073388[/C][C]-0.4453[/C][C]0.656613[/C][C]0.328307[/C][/ROW]
[ROW][C]Openheid_man[/C][C]0.305178309477321[/C][C]0.068079[/C][C]4.4827[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-24.89665041059504.424988-5.626400
geslacht0.4568061702053040.0663426.885600
leeftijd0.03183194159952830.0796830.39950.6900040.345002
leeftijd_man0.3188815812383350.0923323.45370.0006870.000344
opleiding0.1831195755878830.0789292.32010.0214410.010721
opleiding_man-0.1353537350166680.0883-1.53290.127030.063515
Neuroticisme0.5671358119539310.0726477.806700
Neuroticisme_man0.09664060341303020.0936351.03210.3033870.151694
Extraversie0.1145084100259170.0875611.30780.1925940.096297
Extraversie_man0.2677324385373250.1009382.65250.0086930.004346
Openheid-0.03268121440462730.073388-0.44530.6566130.328307
Openheid_man0.3051783094773210.0680794.48271.3e-056e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.890589690556144
R-squared0.793149996924888
Adjusted R-squared0.780716390182669
F-TEST (value)63.7908221941467
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.62015225140122
Sum Squared Residuals13598.1855452328

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.890589690556144 \tabularnewline
R-squared & 0.793149996924888 \tabularnewline
Adjusted R-squared & 0.780716390182669 \tabularnewline
F-TEST (value) & 63.7908221941467 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 183 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.62015225140122 \tabularnewline
Sum Squared Residuals & 13598.1855452328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.890589690556144[/C][/ROW]
[ROW][C]R-squared[/C][C]0.793149996924888[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.780716390182669[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]63.7908221941467[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]183[/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]8.62015225140122[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13598.1855452328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.890589690556144
R-squared0.793149996924888
Adjusted R-squared0.780716390182669
F-TEST (value)63.7908221941467
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.62015225140122
Sum Squared Residuals13598.1855452328






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14030.79363678577209.20636321422803
24531.43705574604913.5629442539510
33819.986724312440018.0132756875600
42833.0305146423447-5.03051464234472
53938.32933584014960.670664159850432
63739.8904355737312-2.89043557373119
73032.2569408009132-2.25694080091324
82935.4398524337145-6.43985243371447
93922.923190786071516.0768092139285
103537.4672868517960-2.46728685179604
113437.7404406805469-3.74044068054692
123832.41621867668815.58378132331195
132130.6986383675012-9.69863836750121
143529.43288834637675.56711165362329
155214.327021228860637.6729787711394
164657.4710793322765-11.4710793322765
175847.412372702860510.5876272971395
185444.44168406212149.5583159378786
192933.7032742142993-4.70327421429934
204336.63056801714236.36943198285766
214543.91688375562521.08311624437480
224642.85637849605413.14362150394595
232531.5893165154313-6.58931651543127
244739.67614988549017.32385011450989
254140.45377358991710.546226410082895
262934.6584037764682-5.65840377646822
274537.69949157270077.30050842729935
285445.67937306235018.32062693764987
292835.2559259776019-7.25592597760186
303738.8295165851031-1.8295165851031
315643.182630700665812.8173692993342
324327.474275327900715.5257246720993
333431.51328847774412.48671152225593
344241.11039361945620.889606380543839
354638.67255886643017.32744113356993
362529.1650247256869-4.16502472568689
372533.9488329992366-8.9488329992366
382524.15637148958380.84362851041622
394840.43914810072247.56085189927762
402730.5982147565159-3.59821475651589
412838.7505556476681-10.7505556476681
422537.5476513788366-12.5476513788366
432622.65397147409473.34602852590534
445140.862165922387510.1378340776125
452933.7391161628344-4.73911616283444
462928.30105455982320.698945440176799
474335.95444581628697.0455541837131
484437.68911278856316.31088721143695
492535.5111472850444-10.5111472850444
505143.73992711423427.26007288576583
514235.42381403683666.57618596316338
522533.8690116109406-8.86901161094056
535142.40299017608318.59700982391692
544640.96298884545485.03701115454523
552938.3042793950757-9.30427939507573
56331.2085071518208-28.2085071518208
572739.4058303962283-12.4058303962283
582020.5458661261612-0.545866126161208
594027.616091670008312.3839083299917
603327.3625498456585.63745015434198
612421.36031546231012.63968453768991
624128.468936350731112.5310636492689
632825.37504326037112.62495673962891
643727.95039501643259.04960498356747
654626.931598406455619.0684015935444
663940.5743895673292-1.57438956732918
672535.1160904953926-10.1160904953926
68121.2299621625815-20.2299621625815
69126.8200584071965-25.8200584071965
70132.3717235451892-31.3717235451892
714729.603699199278617.3963008007214
725242.18959516723169.81040483276838
732739.1255547727848-12.1255547727848
742725.32501224976821.67498775023183
752534.2390648252486-9.23906482524856
762830.9319082841473-2.93190828414725
772530.725024561518-5.72502456151800
785228.933057145689523.0669428543105
794446.169520769564-2.16952076956396
804227.761629730205314.2383702697947
814538.76659469518056.23340530481945
824539.31967438197925.68032561802077
835035.287542117864114.7124578821359
844940.03738145063928.96261854936078
855243.18113885756198.81886114243807
862546.7862884096534-21.7862884096534
87018.5936384590394-18.5936384590394
8800.406880715063305-0.406880715063305
8905.51867240334858-5.51867240334858
9001.35335678549965-1.35335678549965
9105.25700182434596-5.25700182434596
9204.68709091742028-4.68709091742028
9302.45572330527534-2.45572330527534
9402.39159585217417-2.39159585217417
950-0.4814660777800530.481466077780053
96011.9029382805405-11.9029382805405
970-1.413542317906641.41354231790664
9805.15540528016989-5.15540528016989
9906.0198133386327-6.0198133386327
1000-4.343533518557154.34353351855715
10105.9423716012304-5.9423716012304
10200.149371857934789-0.149371857934789
10306.80220950107238-6.80220950107238
10406.29772974432683-6.29772974432683
10505.65396348052784-5.65396348052784
1060-1.364815053904761.36481505390476
1073-7.1872069744826710.1872069744827
10833.18026629667281-0.180266296672810
10920.8754534239235331.12454657607647
1102-0.07525647173364642.07525647173365
11140.04839903122493893.95160096877506
11259.1865504788181-4.18655047881811
11330.5680210356863852.43197896431361
11454.300110989983770.699889010016229
11533.47249956504620-0.472499565046196
11646.23064736195612-2.23064736195612
1173-3.113228363570316.11322836357031
11835.53571664827053-2.53571664827053
11933.51608485755936-0.516084857559356
12044.56603330310107-0.566033303101073
12143.297235260317650.702764739682347
12245.53394020983907-1.53394020983907
12334.66957944265324-1.66957944265324
12433.14217475433213-0.142174754332125
12534.87884507651419-1.87884507651419
12651.688901038896273.31109896110373
12755.11168251447323-0.111682514473229
12848.40772233288465-4.40772233288465
12949.83630537611623-5.83630537611623
13049.3715986718685-5.37159867186849
13156.98526958801426-1.98526958801426
13233.19554620666393-0.195546206663933
13332.708176607705590.291823392294406
13428.32172030936436-6.32172030936435
13550.2896447005184884.71035529948151
13625.42581596480555-3.42581596480555
13735.99830328195859-2.99830328195859
13845.52652709164253-1.52652709164253
13941.368103622326232.63189637767377
14043.917075577740560.0829244222594392
14138.99189491077935-5.99189491077935
14257.08647627881007-2.08647627881007
14326.16977155484524-4.16977155484524
14433.17941319474191-0.179413194741914
1453-3.702157287690676.70215728769067
14646.37798244354826-2.37798244354826
14745.7640128495289-1.76401284952891
14843.483762373581100.516237626418895
14920.03947221205772291.96052778794228
15031.419272094259421.58072790574058
1513-1.200508464266174.20050846426617
15231.309473093832151.69052690616785
15336.09248968568503-3.09248968568503
15430.9515661649327152.04843383506729
15532.511530017542050.488469982457953
15643.038923870626410.961076129373588
15746.56092523604318-2.56092523604318
15844.58334999282386-0.583349992823856
15945.13972107433651-1.13972107433651
16038.62129144865624-5.62129144865624
16130.7320053455587892.26799465444121
16252.952228477917782.04777152208222
16338.24336821575896-5.24336821575896
16435.17565630770856-2.17565630770856
16535.00762979573495-2.00762979573495
16641.853360402988432.14663959701157
1674-7.023191517248711.0231915172487
16834.00677340771638-1.00677340771638
16934.1247567883498-1.12475678834980
17045.02088313868329-1.02088313868329
17143.388961110425170.611038889574834
17244.34108699580148-0.341086995801475
17330.8968359699931432.10316403000686
17432.941225874712240.0587741252877589
1753-0.923130948825363.92313094882536
17631.951041226015531.04895877398447
1775-2.081350801413997.08135080141399
17833.24164880550647-0.241648805506475
17945.86718464795905-1.86718464795905
1804-2.153905278805466.15390527880546
18126.42419318666336-4.42419318666336
18231.154788368623401.8452116313766
18334.29512973614627-1.29512973614627
18449.9061909167957-5.90619091679569
18541.763663647492672.23633635250733
18632.084188693992210.915811306007787
18738.44244547326481-5.44244547326481
18843.618932710883170.381067289116828
18943.060108493492270.939891506507734
1903-1.393571926115494.39357192611549
19132.914861644130960.0851383558690433
1924-3.130495466560437.13049546656043
19355.50321367004777-0.503213670047772
194515.7379695514650-10.7379695514650
195426.73659702224-22.73659702224

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 40 & 30.7936367857720 & 9.20636321422803 \tabularnewline
2 & 45 & 31.437055746049 & 13.5629442539510 \tabularnewline
3 & 38 & 19.9867243124400 & 18.0132756875600 \tabularnewline
4 & 28 & 33.0305146423447 & -5.03051464234472 \tabularnewline
5 & 39 & 38.3293358401496 & 0.670664159850432 \tabularnewline
6 & 37 & 39.8904355737312 & -2.89043557373119 \tabularnewline
7 & 30 & 32.2569408009132 & -2.25694080091324 \tabularnewline
8 & 29 & 35.4398524337145 & -6.43985243371447 \tabularnewline
9 & 39 & 22.9231907860715 & 16.0768092139285 \tabularnewline
10 & 35 & 37.4672868517960 & -2.46728685179604 \tabularnewline
11 & 34 & 37.7404406805469 & -3.74044068054692 \tabularnewline
12 & 38 & 32.4162186766881 & 5.58378132331195 \tabularnewline
13 & 21 & 30.6986383675012 & -9.69863836750121 \tabularnewline
14 & 35 & 29.4328883463767 & 5.56711165362329 \tabularnewline
15 & 52 & 14.3270212288606 & 37.6729787711394 \tabularnewline
16 & 46 & 57.4710793322765 & -11.4710793322765 \tabularnewline
17 & 58 & 47.4123727028605 & 10.5876272971395 \tabularnewline
18 & 54 & 44.4416840621214 & 9.5583159378786 \tabularnewline
19 & 29 & 33.7032742142993 & -4.70327421429934 \tabularnewline
20 & 43 & 36.6305680171423 & 6.36943198285766 \tabularnewline
21 & 45 & 43.9168837556252 & 1.08311624437480 \tabularnewline
22 & 46 & 42.8563784960541 & 3.14362150394595 \tabularnewline
23 & 25 & 31.5893165154313 & -6.58931651543127 \tabularnewline
24 & 47 & 39.6761498854901 & 7.32385011450989 \tabularnewline
25 & 41 & 40.4537735899171 & 0.546226410082895 \tabularnewline
26 & 29 & 34.6584037764682 & -5.65840377646822 \tabularnewline
27 & 45 & 37.6994915727007 & 7.30050842729935 \tabularnewline
28 & 54 & 45.6793730623501 & 8.32062693764987 \tabularnewline
29 & 28 & 35.2559259776019 & -7.25592597760186 \tabularnewline
30 & 37 & 38.8295165851031 & -1.8295165851031 \tabularnewline
31 & 56 & 43.1826307006658 & 12.8173692993342 \tabularnewline
32 & 43 & 27.4742753279007 & 15.5257246720993 \tabularnewline
33 & 34 & 31.5132884777441 & 2.48671152225593 \tabularnewline
34 & 42 & 41.1103936194562 & 0.889606380543839 \tabularnewline
35 & 46 & 38.6725588664301 & 7.32744113356993 \tabularnewline
36 & 25 & 29.1650247256869 & -4.16502472568689 \tabularnewline
37 & 25 & 33.9488329992366 & -8.9488329992366 \tabularnewline
38 & 25 & 24.1563714895838 & 0.84362851041622 \tabularnewline
39 & 48 & 40.4391481007224 & 7.56085189927762 \tabularnewline
40 & 27 & 30.5982147565159 & -3.59821475651589 \tabularnewline
41 & 28 & 38.7505556476681 & -10.7505556476681 \tabularnewline
42 & 25 & 37.5476513788366 & -12.5476513788366 \tabularnewline
43 & 26 & 22.6539714740947 & 3.34602852590534 \tabularnewline
44 & 51 & 40.8621659223875 & 10.1378340776125 \tabularnewline
45 & 29 & 33.7391161628344 & -4.73911616283444 \tabularnewline
46 & 29 & 28.3010545598232 & 0.698945440176799 \tabularnewline
47 & 43 & 35.9544458162869 & 7.0455541837131 \tabularnewline
48 & 44 & 37.6891127885631 & 6.31088721143695 \tabularnewline
49 & 25 & 35.5111472850444 & -10.5111472850444 \tabularnewline
50 & 51 & 43.7399271142342 & 7.26007288576583 \tabularnewline
51 & 42 & 35.4238140368366 & 6.57618596316338 \tabularnewline
52 & 25 & 33.8690116109406 & -8.86901161094056 \tabularnewline
53 & 51 & 42.4029901760831 & 8.59700982391692 \tabularnewline
54 & 46 & 40.9629888454548 & 5.03701115454523 \tabularnewline
55 & 29 & 38.3042793950757 & -9.30427939507573 \tabularnewline
56 & 3 & 31.2085071518208 & -28.2085071518208 \tabularnewline
57 & 27 & 39.4058303962283 & -12.4058303962283 \tabularnewline
58 & 20 & 20.5458661261612 & -0.545866126161208 \tabularnewline
59 & 40 & 27.6160916700083 & 12.3839083299917 \tabularnewline
60 & 33 & 27.362549845658 & 5.63745015434198 \tabularnewline
61 & 24 & 21.3603154623101 & 2.63968453768991 \tabularnewline
62 & 41 & 28.4689363507311 & 12.5310636492689 \tabularnewline
63 & 28 & 25.3750432603711 & 2.62495673962891 \tabularnewline
64 & 37 & 27.9503950164325 & 9.04960498356747 \tabularnewline
65 & 46 & 26.9315984064556 & 19.0684015935444 \tabularnewline
66 & 39 & 40.5743895673292 & -1.57438956732918 \tabularnewline
67 & 25 & 35.1160904953926 & -10.1160904953926 \tabularnewline
68 & 1 & 21.2299621625815 & -20.2299621625815 \tabularnewline
69 & 1 & 26.8200584071965 & -25.8200584071965 \tabularnewline
70 & 1 & 32.3717235451892 & -31.3717235451892 \tabularnewline
71 & 47 & 29.6036991992786 & 17.3963008007214 \tabularnewline
72 & 52 & 42.1895951672316 & 9.81040483276838 \tabularnewline
73 & 27 & 39.1255547727848 & -12.1255547727848 \tabularnewline
74 & 27 & 25.3250122497682 & 1.67498775023183 \tabularnewline
75 & 25 & 34.2390648252486 & -9.23906482524856 \tabularnewline
76 & 28 & 30.9319082841473 & -2.93190828414725 \tabularnewline
77 & 25 & 30.725024561518 & -5.72502456151800 \tabularnewline
78 & 52 & 28.9330571456895 & 23.0669428543105 \tabularnewline
79 & 44 & 46.169520769564 & -2.16952076956396 \tabularnewline
80 & 42 & 27.7616297302053 & 14.2383702697947 \tabularnewline
81 & 45 & 38.7665946951805 & 6.23340530481945 \tabularnewline
82 & 45 & 39.3196743819792 & 5.68032561802077 \tabularnewline
83 & 50 & 35.2875421178641 & 14.7124578821359 \tabularnewline
84 & 49 & 40.0373814506392 & 8.96261854936078 \tabularnewline
85 & 52 & 43.1811388575619 & 8.81886114243807 \tabularnewline
86 & 25 & 46.7862884096534 & -21.7862884096534 \tabularnewline
87 & 0 & 18.5936384590394 & -18.5936384590394 \tabularnewline
88 & 0 & 0.406880715063305 & -0.406880715063305 \tabularnewline
89 & 0 & 5.51867240334858 & -5.51867240334858 \tabularnewline
90 & 0 & 1.35335678549965 & -1.35335678549965 \tabularnewline
91 & 0 & 5.25700182434596 & -5.25700182434596 \tabularnewline
92 & 0 & 4.68709091742028 & -4.68709091742028 \tabularnewline
93 & 0 & 2.45572330527534 & -2.45572330527534 \tabularnewline
94 & 0 & 2.39159585217417 & -2.39159585217417 \tabularnewline
95 & 0 & -0.481466077780053 & 0.481466077780053 \tabularnewline
96 & 0 & 11.9029382805405 & -11.9029382805405 \tabularnewline
97 & 0 & -1.41354231790664 & 1.41354231790664 \tabularnewline
98 & 0 & 5.15540528016989 & -5.15540528016989 \tabularnewline
99 & 0 & 6.0198133386327 & -6.0198133386327 \tabularnewline
100 & 0 & -4.34353351855715 & 4.34353351855715 \tabularnewline
101 & 0 & 5.9423716012304 & -5.9423716012304 \tabularnewline
102 & 0 & 0.149371857934789 & -0.149371857934789 \tabularnewline
103 & 0 & 6.80220950107238 & -6.80220950107238 \tabularnewline
104 & 0 & 6.29772974432683 & -6.29772974432683 \tabularnewline
105 & 0 & 5.65396348052784 & -5.65396348052784 \tabularnewline
106 & 0 & -1.36481505390476 & 1.36481505390476 \tabularnewline
107 & 3 & -7.18720697448267 & 10.1872069744827 \tabularnewline
108 & 3 & 3.18026629667281 & -0.180266296672810 \tabularnewline
109 & 2 & 0.875453423923533 & 1.12454657607647 \tabularnewline
110 & 2 & -0.0752564717336464 & 2.07525647173365 \tabularnewline
111 & 4 & 0.0483990312249389 & 3.95160096877506 \tabularnewline
112 & 5 & 9.1865504788181 & -4.18655047881811 \tabularnewline
113 & 3 & 0.568021035686385 & 2.43197896431361 \tabularnewline
114 & 5 & 4.30011098998377 & 0.699889010016229 \tabularnewline
115 & 3 & 3.47249956504620 & -0.472499565046196 \tabularnewline
116 & 4 & 6.23064736195612 & -2.23064736195612 \tabularnewline
117 & 3 & -3.11322836357031 & 6.11322836357031 \tabularnewline
118 & 3 & 5.53571664827053 & -2.53571664827053 \tabularnewline
119 & 3 & 3.51608485755936 & -0.516084857559356 \tabularnewline
120 & 4 & 4.56603330310107 & -0.566033303101073 \tabularnewline
121 & 4 & 3.29723526031765 & 0.702764739682347 \tabularnewline
122 & 4 & 5.53394020983907 & -1.53394020983907 \tabularnewline
123 & 3 & 4.66957944265324 & -1.66957944265324 \tabularnewline
124 & 3 & 3.14217475433213 & -0.142174754332125 \tabularnewline
125 & 3 & 4.87884507651419 & -1.87884507651419 \tabularnewline
126 & 5 & 1.68890103889627 & 3.31109896110373 \tabularnewline
127 & 5 & 5.11168251447323 & -0.111682514473229 \tabularnewline
128 & 4 & 8.40772233288465 & -4.40772233288465 \tabularnewline
129 & 4 & 9.83630537611623 & -5.83630537611623 \tabularnewline
130 & 4 & 9.3715986718685 & -5.37159867186849 \tabularnewline
131 & 5 & 6.98526958801426 & -1.98526958801426 \tabularnewline
132 & 3 & 3.19554620666393 & -0.195546206663933 \tabularnewline
133 & 3 & 2.70817660770559 & 0.291823392294406 \tabularnewline
134 & 2 & 8.32172030936436 & -6.32172030936435 \tabularnewline
135 & 5 & 0.289644700518488 & 4.71035529948151 \tabularnewline
136 & 2 & 5.42581596480555 & -3.42581596480555 \tabularnewline
137 & 3 & 5.99830328195859 & -2.99830328195859 \tabularnewline
138 & 4 & 5.52652709164253 & -1.52652709164253 \tabularnewline
139 & 4 & 1.36810362232623 & 2.63189637767377 \tabularnewline
140 & 4 & 3.91707557774056 & 0.0829244222594392 \tabularnewline
141 & 3 & 8.99189491077935 & -5.99189491077935 \tabularnewline
142 & 5 & 7.08647627881007 & -2.08647627881007 \tabularnewline
143 & 2 & 6.16977155484524 & -4.16977155484524 \tabularnewline
144 & 3 & 3.17941319474191 & -0.179413194741914 \tabularnewline
145 & 3 & -3.70215728769067 & 6.70215728769067 \tabularnewline
146 & 4 & 6.37798244354826 & -2.37798244354826 \tabularnewline
147 & 4 & 5.7640128495289 & -1.76401284952891 \tabularnewline
148 & 4 & 3.48376237358110 & 0.516237626418895 \tabularnewline
149 & 2 & 0.0394722120577229 & 1.96052778794228 \tabularnewline
150 & 3 & 1.41927209425942 & 1.58072790574058 \tabularnewline
151 & 3 & -1.20050846426617 & 4.20050846426617 \tabularnewline
152 & 3 & 1.30947309383215 & 1.69052690616785 \tabularnewline
153 & 3 & 6.09248968568503 & -3.09248968568503 \tabularnewline
154 & 3 & 0.951566164932715 & 2.04843383506729 \tabularnewline
155 & 3 & 2.51153001754205 & 0.488469982457953 \tabularnewline
156 & 4 & 3.03892387062641 & 0.961076129373588 \tabularnewline
157 & 4 & 6.56092523604318 & -2.56092523604318 \tabularnewline
158 & 4 & 4.58334999282386 & -0.583349992823856 \tabularnewline
159 & 4 & 5.13972107433651 & -1.13972107433651 \tabularnewline
160 & 3 & 8.62129144865624 & -5.62129144865624 \tabularnewline
161 & 3 & 0.732005345558789 & 2.26799465444121 \tabularnewline
162 & 5 & 2.95222847791778 & 2.04777152208222 \tabularnewline
163 & 3 & 8.24336821575896 & -5.24336821575896 \tabularnewline
164 & 3 & 5.17565630770856 & -2.17565630770856 \tabularnewline
165 & 3 & 5.00762979573495 & -2.00762979573495 \tabularnewline
166 & 4 & 1.85336040298843 & 2.14663959701157 \tabularnewline
167 & 4 & -7.0231915172487 & 11.0231915172487 \tabularnewline
168 & 3 & 4.00677340771638 & -1.00677340771638 \tabularnewline
169 & 3 & 4.1247567883498 & -1.12475678834980 \tabularnewline
170 & 4 & 5.02088313868329 & -1.02088313868329 \tabularnewline
171 & 4 & 3.38896111042517 & 0.611038889574834 \tabularnewline
172 & 4 & 4.34108699580148 & -0.341086995801475 \tabularnewline
173 & 3 & 0.896835969993143 & 2.10316403000686 \tabularnewline
174 & 3 & 2.94122587471224 & 0.0587741252877589 \tabularnewline
175 & 3 & -0.92313094882536 & 3.92313094882536 \tabularnewline
176 & 3 & 1.95104122601553 & 1.04895877398447 \tabularnewline
177 & 5 & -2.08135080141399 & 7.08135080141399 \tabularnewline
178 & 3 & 3.24164880550647 & -0.241648805506475 \tabularnewline
179 & 4 & 5.86718464795905 & -1.86718464795905 \tabularnewline
180 & 4 & -2.15390527880546 & 6.15390527880546 \tabularnewline
181 & 2 & 6.42419318666336 & -4.42419318666336 \tabularnewline
182 & 3 & 1.15478836862340 & 1.8452116313766 \tabularnewline
183 & 3 & 4.29512973614627 & -1.29512973614627 \tabularnewline
184 & 4 & 9.9061909167957 & -5.90619091679569 \tabularnewline
185 & 4 & 1.76366364749267 & 2.23633635250733 \tabularnewline
186 & 3 & 2.08418869399221 & 0.915811306007787 \tabularnewline
187 & 3 & 8.44244547326481 & -5.44244547326481 \tabularnewline
188 & 4 & 3.61893271088317 & 0.381067289116828 \tabularnewline
189 & 4 & 3.06010849349227 & 0.939891506507734 \tabularnewline
190 & 3 & -1.39357192611549 & 4.39357192611549 \tabularnewline
191 & 3 & 2.91486164413096 & 0.0851383558690433 \tabularnewline
192 & 4 & -3.13049546656043 & 7.13049546656043 \tabularnewline
193 & 5 & 5.50321367004777 & -0.503213670047772 \tabularnewline
194 & 5 & 15.7379695514650 & -10.7379695514650 \tabularnewline
195 & 4 & 26.73659702224 & -22.73659702224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&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]40[/C][C]30.7936367857720[/C][C]9.20636321422803[/C][/ROW]
[ROW][C]2[/C][C]45[/C][C]31.437055746049[/C][C]13.5629442539510[/C][/ROW]
[ROW][C]3[/C][C]38[/C][C]19.9867243124400[/C][C]18.0132756875600[/C][/ROW]
[ROW][C]4[/C][C]28[/C][C]33.0305146423447[/C][C]-5.03051464234472[/C][/ROW]
[ROW][C]5[/C][C]39[/C][C]38.3293358401496[/C][C]0.670664159850432[/C][/ROW]
[ROW][C]6[/C][C]37[/C][C]39.8904355737312[/C][C]-2.89043557373119[/C][/ROW]
[ROW][C]7[/C][C]30[/C][C]32.2569408009132[/C][C]-2.25694080091324[/C][/ROW]
[ROW][C]8[/C][C]29[/C][C]35.4398524337145[/C][C]-6.43985243371447[/C][/ROW]
[ROW][C]9[/C][C]39[/C][C]22.9231907860715[/C][C]16.0768092139285[/C][/ROW]
[ROW][C]10[/C][C]35[/C][C]37.4672868517960[/C][C]-2.46728685179604[/C][/ROW]
[ROW][C]11[/C][C]34[/C][C]37.7404406805469[/C][C]-3.74044068054692[/C][/ROW]
[ROW][C]12[/C][C]38[/C][C]32.4162186766881[/C][C]5.58378132331195[/C][/ROW]
[ROW][C]13[/C][C]21[/C][C]30.6986383675012[/C][C]-9.69863836750121[/C][/ROW]
[ROW][C]14[/C][C]35[/C][C]29.4328883463767[/C][C]5.56711165362329[/C][/ROW]
[ROW][C]15[/C][C]52[/C][C]14.3270212288606[/C][C]37.6729787711394[/C][/ROW]
[ROW][C]16[/C][C]46[/C][C]57.4710793322765[/C][C]-11.4710793322765[/C][/ROW]
[ROW][C]17[/C][C]58[/C][C]47.4123727028605[/C][C]10.5876272971395[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]44.4416840621214[/C][C]9.5583159378786[/C][/ROW]
[ROW][C]19[/C][C]29[/C][C]33.7032742142993[/C][C]-4.70327421429934[/C][/ROW]
[ROW][C]20[/C][C]43[/C][C]36.6305680171423[/C][C]6.36943198285766[/C][/ROW]
[ROW][C]21[/C][C]45[/C][C]43.9168837556252[/C][C]1.08311624437480[/C][/ROW]
[ROW][C]22[/C][C]46[/C][C]42.8563784960541[/C][C]3.14362150394595[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]31.5893165154313[/C][C]-6.58931651543127[/C][/ROW]
[ROW][C]24[/C][C]47[/C][C]39.6761498854901[/C][C]7.32385011450989[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]40.4537735899171[/C][C]0.546226410082895[/C][/ROW]
[ROW][C]26[/C][C]29[/C][C]34.6584037764682[/C][C]-5.65840377646822[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]37.6994915727007[/C][C]7.30050842729935[/C][/ROW]
[ROW][C]28[/C][C]54[/C][C]45.6793730623501[/C][C]8.32062693764987[/C][/ROW]
[ROW][C]29[/C][C]28[/C][C]35.2559259776019[/C][C]-7.25592597760186[/C][/ROW]
[ROW][C]30[/C][C]37[/C][C]38.8295165851031[/C][C]-1.8295165851031[/C][/ROW]
[ROW][C]31[/C][C]56[/C][C]43.1826307006658[/C][C]12.8173692993342[/C][/ROW]
[ROW][C]32[/C][C]43[/C][C]27.4742753279007[/C][C]15.5257246720993[/C][/ROW]
[ROW][C]33[/C][C]34[/C][C]31.5132884777441[/C][C]2.48671152225593[/C][/ROW]
[ROW][C]34[/C][C]42[/C][C]41.1103936194562[/C][C]0.889606380543839[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]38.6725588664301[/C][C]7.32744113356993[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]29.1650247256869[/C][C]-4.16502472568689[/C][/ROW]
[ROW][C]37[/C][C]25[/C][C]33.9488329992366[/C][C]-8.9488329992366[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]24.1563714895838[/C][C]0.84362851041622[/C][/ROW]
[ROW][C]39[/C][C]48[/C][C]40.4391481007224[/C][C]7.56085189927762[/C][/ROW]
[ROW][C]40[/C][C]27[/C][C]30.5982147565159[/C][C]-3.59821475651589[/C][/ROW]
[ROW][C]41[/C][C]28[/C][C]38.7505556476681[/C][C]-10.7505556476681[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]37.5476513788366[/C][C]-12.5476513788366[/C][/ROW]
[ROW][C]43[/C][C]26[/C][C]22.6539714740947[/C][C]3.34602852590534[/C][/ROW]
[ROW][C]44[/C][C]51[/C][C]40.8621659223875[/C][C]10.1378340776125[/C][/ROW]
[ROW][C]45[/C][C]29[/C][C]33.7391161628344[/C][C]-4.73911616283444[/C][/ROW]
[ROW][C]46[/C][C]29[/C][C]28.3010545598232[/C][C]0.698945440176799[/C][/ROW]
[ROW][C]47[/C][C]43[/C][C]35.9544458162869[/C][C]7.0455541837131[/C][/ROW]
[ROW][C]48[/C][C]44[/C][C]37.6891127885631[/C][C]6.31088721143695[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]35.5111472850444[/C][C]-10.5111472850444[/C][/ROW]
[ROW][C]50[/C][C]51[/C][C]43.7399271142342[/C][C]7.26007288576583[/C][/ROW]
[ROW][C]51[/C][C]42[/C][C]35.4238140368366[/C][C]6.57618596316338[/C][/ROW]
[ROW][C]52[/C][C]25[/C][C]33.8690116109406[/C][C]-8.86901161094056[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]42.4029901760831[/C][C]8.59700982391692[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]40.9629888454548[/C][C]5.03701115454523[/C][/ROW]
[ROW][C]55[/C][C]29[/C][C]38.3042793950757[/C][C]-9.30427939507573[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]31.2085071518208[/C][C]-28.2085071518208[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]39.4058303962283[/C][C]-12.4058303962283[/C][/ROW]
[ROW][C]58[/C][C]20[/C][C]20.5458661261612[/C][C]-0.545866126161208[/C][/ROW]
[ROW][C]59[/C][C]40[/C][C]27.6160916700083[/C][C]12.3839083299917[/C][/ROW]
[ROW][C]60[/C][C]33[/C][C]27.362549845658[/C][C]5.63745015434198[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]21.3603154623101[/C][C]2.63968453768991[/C][/ROW]
[ROW][C]62[/C][C]41[/C][C]28.4689363507311[/C][C]12.5310636492689[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]25.3750432603711[/C][C]2.62495673962891[/C][/ROW]
[ROW][C]64[/C][C]37[/C][C]27.9503950164325[/C][C]9.04960498356747[/C][/ROW]
[ROW][C]65[/C][C]46[/C][C]26.9315984064556[/C][C]19.0684015935444[/C][/ROW]
[ROW][C]66[/C][C]39[/C][C]40.5743895673292[/C][C]-1.57438956732918[/C][/ROW]
[ROW][C]67[/C][C]25[/C][C]35.1160904953926[/C][C]-10.1160904953926[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]21.2299621625815[/C][C]-20.2299621625815[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]26.8200584071965[/C][C]-25.8200584071965[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]32.3717235451892[/C][C]-31.3717235451892[/C][/ROW]
[ROW][C]71[/C][C]47[/C][C]29.6036991992786[/C][C]17.3963008007214[/C][/ROW]
[ROW][C]72[/C][C]52[/C][C]42.1895951672316[/C][C]9.81040483276838[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]39.1255547727848[/C][C]-12.1255547727848[/C][/ROW]
[ROW][C]74[/C][C]27[/C][C]25.3250122497682[/C][C]1.67498775023183[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]34.2390648252486[/C][C]-9.23906482524856[/C][/ROW]
[ROW][C]76[/C][C]28[/C][C]30.9319082841473[/C][C]-2.93190828414725[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]30.725024561518[/C][C]-5.72502456151800[/C][/ROW]
[ROW][C]78[/C][C]52[/C][C]28.9330571456895[/C][C]23.0669428543105[/C][/ROW]
[ROW][C]79[/C][C]44[/C][C]46.169520769564[/C][C]-2.16952076956396[/C][/ROW]
[ROW][C]80[/C][C]42[/C][C]27.7616297302053[/C][C]14.2383702697947[/C][/ROW]
[ROW][C]81[/C][C]45[/C][C]38.7665946951805[/C][C]6.23340530481945[/C][/ROW]
[ROW][C]82[/C][C]45[/C][C]39.3196743819792[/C][C]5.68032561802077[/C][/ROW]
[ROW][C]83[/C][C]50[/C][C]35.2875421178641[/C][C]14.7124578821359[/C][/ROW]
[ROW][C]84[/C][C]49[/C][C]40.0373814506392[/C][C]8.96261854936078[/C][/ROW]
[ROW][C]85[/C][C]52[/C][C]43.1811388575619[/C][C]8.81886114243807[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]46.7862884096534[/C][C]-21.7862884096534[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]18.5936384590394[/C][C]-18.5936384590394[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.406880715063305[/C][C]-0.406880715063305[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]5.51867240334858[/C][C]-5.51867240334858[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]1.35335678549965[/C][C]-1.35335678549965[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]5.25700182434596[/C][C]-5.25700182434596[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]4.68709091742028[/C][C]-4.68709091742028[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]2.45572330527534[/C][C]-2.45572330527534[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]2.39159585217417[/C][C]-2.39159585217417[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.481466077780053[/C][C]0.481466077780053[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]11.9029382805405[/C][C]-11.9029382805405[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-1.41354231790664[/C][C]1.41354231790664[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]5.15540528016989[/C][C]-5.15540528016989[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]6.0198133386327[/C][C]-6.0198133386327[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-4.34353351855715[/C][C]4.34353351855715[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]5.9423716012304[/C][C]-5.9423716012304[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.149371857934789[/C][C]-0.149371857934789[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]6.80220950107238[/C][C]-6.80220950107238[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]6.29772974432683[/C][C]-6.29772974432683[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]5.65396348052784[/C][C]-5.65396348052784[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]-1.36481505390476[/C][C]1.36481505390476[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]-7.18720697448267[/C][C]10.1872069744827[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]3.18026629667281[/C][C]-0.180266296672810[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]0.875453423923533[/C][C]1.12454657607647[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]-0.0752564717336464[/C][C]2.07525647173365[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]0.0483990312249389[/C][C]3.95160096877506[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]9.1865504788181[/C][C]-4.18655047881811[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]0.568021035686385[/C][C]2.43197896431361[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]4.30011098998377[/C][C]0.699889010016229[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]3.47249956504620[/C][C]-0.472499565046196[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]6.23064736195612[/C][C]-2.23064736195612[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]-3.11322836357031[/C][C]6.11322836357031[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]5.53571664827053[/C][C]-2.53571664827053[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.51608485755936[/C][C]-0.516084857559356[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]4.56603330310107[/C][C]-0.566033303101073[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]3.29723526031765[/C][C]0.702764739682347[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]5.53394020983907[/C][C]-1.53394020983907[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]4.66957944265324[/C][C]-1.66957944265324[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]3.14217475433213[/C][C]-0.142174754332125[/C][/ROW]
[ROW][C]125[/C][C]3[/C][C]4.87884507651419[/C][C]-1.87884507651419[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]1.68890103889627[/C][C]3.31109896110373[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]5.11168251447323[/C][C]-0.111682514473229[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]8.40772233288465[/C][C]-4.40772233288465[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]9.83630537611623[/C][C]-5.83630537611623[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]9.3715986718685[/C][C]-5.37159867186849[/C][/ROW]
[ROW][C]131[/C][C]5[/C][C]6.98526958801426[/C][C]-1.98526958801426[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]3.19554620666393[/C][C]-0.195546206663933[/C][/ROW]
[ROW][C]133[/C][C]3[/C][C]2.70817660770559[/C][C]0.291823392294406[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]8.32172030936436[/C][C]-6.32172030936435[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]0.289644700518488[/C][C]4.71035529948151[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]5.42581596480555[/C][C]-3.42581596480555[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]5.99830328195859[/C][C]-2.99830328195859[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]5.52652709164253[/C][C]-1.52652709164253[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]1.36810362232623[/C][C]2.63189637767377[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]3.91707557774056[/C][C]0.0829244222594392[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]8.99189491077935[/C][C]-5.99189491077935[/C][/ROW]
[ROW][C]142[/C][C]5[/C][C]7.08647627881007[/C][C]-2.08647627881007[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]6.16977155484524[/C][C]-4.16977155484524[/C][/ROW]
[ROW][C]144[/C][C]3[/C][C]3.17941319474191[/C][C]-0.179413194741914[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]-3.70215728769067[/C][C]6.70215728769067[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]6.37798244354826[/C][C]-2.37798244354826[/C][/ROW]
[ROW][C]147[/C][C]4[/C][C]5.7640128495289[/C][C]-1.76401284952891[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]3.48376237358110[/C][C]0.516237626418895[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]0.0394722120577229[/C][C]1.96052778794228[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]1.41927209425942[/C][C]1.58072790574058[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]-1.20050846426617[/C][C]4.20050846426617[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]1.30947309383215[/C][C]1.69052690616785[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]6.09248968568503[/C][C]-3.09248968568503[/C][/ROW]
[ROW][C]154[/C][C]3[/C][C]0.951566164932715[/C][C]2.04843383506729[/C][/ROW]
[ROW][C]155[/C][C]3[/C][C]2.51153001754205[/C][C]0.488469982457953[/C][/ROW]
[ROW][C]156[/C][C]4[/C][C]3.03892387062641[/C][C]0.961076129373588[/C][/ROW]
[ROW][C]157[/C][C]4[/C][C]6.56092523604318[/C][C]-2.56092523604318[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]4.58334999282386[/C][C]-0.583349992823856[/C][/ROW]
[ROW][C]159[/C][C]4[/C][C]5.13972107433651[/C][C]-1.13972107433651[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]8.62129144865624[/C][C]-5.62129144865624[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]0.732005345558789[/C][C]2.26799465444121[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]2.95222847791778[/C][C]2.04777152208222[/C][/ROW]
[ROW][C]163[/C][C]3[/C][C]8.24336821575896[/C][C]-5.24336821575896[/C][/ROW]
[ROW][C]164[/C][C]3[/C][C]5.17565630770856[/C][C]-2.17565630770856[/C][/ROW]
[ROW][C]165[/C][C]3[/C][C]5.00762979573495[/C][C]-2.00762979573495[/C][/ROW]
[ROW][C]166[/C][C]4[/C][C]1.85336040298843[/C][C]2.14663959701157[/C][/ROW]
[ROW][C]167[/C][C]4[/C][C]-7.0231915172487[/C][C]11.0231915172487[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]4.00677340771638[/C][C]-1.00677340771638[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]4.1247567883498[/C][C]-1.12475678834980[/C][/ROW]
[ROW][C]170[/C][C]4[/C][C]5.02088313868329[/C][C]-1.02088313868329[/C][/ROW]
[ROW][C]171[/C][C]4[/C][C]3.38896111042517[/C][C]0.611038889574834[/C][/ROW]
[ROW][C]172[/C][C]4[/C][C]4.34108699580148[/C][C]-0.341086995801475[/C][/ROW]
[ROW][C]173[/C][C]3[/C][C]0.896835969993143[/C][C]2.10316403000686[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]2.94122587471224[/C][C]0.0587741252877589[/C][/ROW]
[ROW][C]175[/C][C]3[/C][C]-0.92313094882536[/C][C]3.92313094882536[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]1.95104122601553[/C][C]1.04895877398447[/C][/ROW]
[ROW][C]177[/C][C]5[/C][C]-2.08135080141399[/C][C]7.08135080141399[/C][/ROW]
[ROW][C]178[/C][C]3[/C][C]3.24164880550647[/C][C]-0.241648805506475[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]5.86718464795905[/C][C]-1.86718464795905[/C][/ROW]
[ROW][C]180[/C][C]4[/C][C]-2.15390527880546[/C][C]6.15390527880546[/C][/ROW]
[ROW][C]181[/C][C]2[/C][C]6.42419318666336[/C][C]-4.42419318666336[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]1.15478836862340[/C][C]1.8452116313766[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]4.29512973614627[/C][C]-1.29512973614627[/C][/ROW]
[ROW][C]184[/C][C]4[/C][C]9.9061909167957[/C][C]-5.90619091679569[/C][/ROW]
[ROW][C]185[/C][C]4[/C][C]1.76366364749267[/C][C]2.23633635250733[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]2.08418869399221[/C][C]0.915811306007787[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]8.44244547326481[/C][C]-5.44244547326481[/C][/ROW]
[ROW][C]188[/C][C]4[/C][C]3.61893271088317[/C][C]0.381067289116828[/C][/ROW]
[ROW][C]189[/C][C]4[/C][C]3.06010849349227[/C][C]0.939891506507734[/C][/ROW]
[ROW][C]190[/C][C]3[/C][C]-1.39357192611549[/C][C]4.39357192611549[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]2.91486164413096[/C][C]0.0851383558690433[/C][/ROW]
[ROW][C]192[/C][C]4[/C][C]-3.13049546656043[/C][C]7.13049546656043[/C][/ROW]
[ROW][C]193[/C][C]5[/C][C]5.50321367004777[/C][C]-0.503213670047772[/C][/ROW]
[ROW][C]194[/C][C]5[/C][C]15.7379695514650[/C][C]-10.7379695514650[/C][/ROW]
[ROW][C]195[/C][C]4[/C][C]26.73659702224[/C][C]-22.73659702224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14030.79363678577209.20636321422803
24531.43705574604913.5629442539510
33819.986724312440018.0132756875600
42833.0305146423447-5.03051464234472
53938.32933584014960.670664159850432
63739.8904355737312-2.89043557373119
73032.2569408009132-2.25694080091324
82935.4398524337145-6.43985243371447
93922.923190786071516.0768092139285
103537.4672868517960-2.46728685179604
113437.7404406805469-3.74044068054692
123832.41621867668815.58378132331195
132130.6986383675012-9.69863836750121
143529.43288834637675.56711165362329
155214.327021228860637.6729787711394
164657.4710793322765-11.4710793322765
175847.412372702860510.5876272971395
185444.44168406212149.5583159378786
192933.7032742142993-4.70327421429934
204336.63056801714236.36943198285766
214543.91688375562521.08311624437480
224642.85637849605413.14362150394595
232531.5893165154313-6.58931651543127
244739.67614988549017.32385011450989
254140.45377358991710.546226410082895
262934.6584037764682-5.65840377646822
274537.69949157270077.30050842729935
285445.67937306235018.32062693764987
292835.2559259776019-7.25592597760186
303738.8295165851031-1.8295165851031
315643.182630700665812.8173692993342
324327.474275327900715.5257246720993
333431.51328847774412.48671152225593
344241.11039361945620.889606380543839
354638.67255886643017.32744113356993
362529.1650247256869-4.16502472568689
372533.9488329992366-8.9488329992366
382524.15637148958380.84362851041622
394840.43914810072247.56085189927762
402730.5982147565159-3.59821475651589
412838.7505556476681-10.7505556476681
422537.5476513788366-12.5476513788366
432622.65397147409473.34602852590534
445140.862165922387510.1378340776125
452933.7391161628344-4.73911616283444
462928.30105455982320.698945440176799
474335.95444581628697.0455541837131
484437.68911278856316.31088721143695
492535.5111472850444-10.5111472850444
505143.73992711423427.26007288576583
514235.42381403683666.57618596316338
522533.8690116109406-8.86901161094056
535142.40299017608318.59700982391692
544640.96298884545485.03701115454523
552938.3042793950757-9.30427939507573
56331.2085071518208-28.2085071518208
572739.4058303962283-12.4058303962283
582020.5458661261612-0.545866126161208
594027.616091670008312.3839083299917
603327.3625498456585.63745015434198
612421.36031546231012.63968453768991
624128.468936350731112.5310636492689
632825.37504326037112.62495673962891
643727.95039501643259.04960498356747
654626.931598406455619.0684015935444
663940.5743895673292-1.57438956732918
672535.1160904953926-10.1160904953926
68121.2299621625815-20.2299621625815
69126.8200584071965-25.8200584071965
70132.3717235451892-31.3717235451892
714729.603699199278617.3963008007214
725242.18959516723169.81040483276838
732739.1255547727848-12.1255547727848
742725.32501224976821.67498775023183
752534.2390648252486-9.23906482524856
762830.9319082841473-2.93190828414725
772530.725024561518-5.72502456151800
785228.933057145689523.0669428543105
794446.169520769564-2.16952076956396
804227.761629730205314.2383702697947
814538.76659469518056.23340530481945
824539.31967438197925.68032561802077
835035.287542117864114.7124578821359
844940.03738145063928.96261854936078
855243.18113885756198.81886114243807
862546.7862884096534-21.7862884096534
87018.5936384590394-18.5936384590394
8800.406880715063305-0.406880715063305
8905.51867240334858-5.51867240334858
9001.35335678549965-1.35335678549965
9105.25700182434596-5.25700182434596
9204.68709091742028-4.68709091742028
9302.45572330527534-2.45572330527534
9402.39159585217417-2.39159585217417
950-0.4814660777800530.481466077780053
96011.9029382805405-11.9029382805405
970-1.413542317906641.41354231790664
9805.15540528016989-5.15540528016989
9906.0198133386327-6.0198133386327
1000-4.343533518557154.34353351855715
10105.9423716012304-5.9423716012304
10200.149371857934789-0.149371857934789
10306.80220950107238-6.80220950107238
10406.29772974432683-6.29772974432683
10505.65396348052784-5.65396348052784
1060-1.364815053904761.36481505390476
1073-7.1872069744826710.1872069744827
10833.18026629667281-0.180266296672810
10920.8754534239235331.12454657607647
1102-0.07525647173364642.07525647173365
11140.04839903122493893.95160096877506
11259.1865504788181-4.18655047881811
11330.5680210356863852.43197896431361
11454.300110989983770.699889010016229
11533.47249956504620-0.472499565046196
11646.23064736195612-2.23064736195612
1173-3.113228363570316.11322836357031
11835.53571664827053-2.53571664827053
11933.51608485755936-0.516084857559356
12044.56603330310107-0.566033303101073
12143.297235260317650.702764739682347
12245.53394020983907-1.53394020983907
12334.66957944265324-1.66957944265324
12433.14217475433213-0.142174754332125
12534.87884507651419-1.87884507651419
12651.688901038896273.31109896110373
12755.11168251447323-0.111682514473229
12848.40772233288465-4.40772233288465
12949.83630537611623-5.83630537611623
13049.3715986718685-5.37159867186849
13156.98526958801426-1.98526958801426
13233.19554620666393-0.195546206663933
13332.708176607705590.291823392294406
13428.32172030936436-6.32172030936435
13550.2896447005184884.71035529948151
13625.42581596480555-3.42581596480555
13735.99830328195859-2.99830328195859
13845.52652709164253-1.52652709164253
13941.368103622326232.63189637767377
14043.917075577740560.0829244222594392
14138.99189491077935-5.99189491077935
14257.08647627881007-2.08647627881007
14326.16977155484524-4.16977155484524
14433.17941319474191-0.179413194741914
1453-3.702157287690676.70215728769067
14646.37798244354826-2.37798244354826
14745.7640128495289-1.76401284952891
14843.483762373581100.516237626418895
14920.03947221205772291.96052778794228
15031.419272094259421.58072790574058
1513-1.200508464266174.20050846426617
15231.309473093832151.69052690616785
15336.09248968568503-3.09248968568503
15430.9515661649327152.04843383506729
15532.511530017542050.488469982457953
15643.038923870626410.961076129373588
15746.56092523604318-2.56092523604318
15844.58334999282386-0.583349992823856
15945.13972107433651-1.13972107433651
16038.62129144865624-5.62129144865624
16130.7320053455587892.26799465444121
16252.952228477917782.04777152208222
16338.24336821575896-5.24336821575896
16435.17565630770856-2.17565630770856
16535.00762979573495-2.00762979573495
16641.853360402988432.14663959701157
1674-7.023191517248711.0231915172487
16834.00677340771638-1.00677340771638
16934.1247567883498-1.12475678834980
17045.02088313868329-1.02088313868329
17143.388961110425170.611038889574834
17244.34108699580148-0.341086995801475
17330.8968359699931432.10316403000686
17432.941225874712240.0587741252877589
1753-0.923130948825363.92313094882536
17631.951041226015531.04895877398447
1775-2.081350801413997.08135080141399
17833.24164880550647-0.241648805506475
17945.86718464795905-1.86718464795905
1804-2.153905278805466.15390527880546
18126.42419318666336-4.42419318666336
18231.154788368623401.8452116313766
18334.29512973614627-1.29512973614627
18449.9061909167957-5.90619091679569
18541.763663647492672.23633635250733
18632.084188693992210.915811306007787
18738.44244547326481-5.44244547326481
18843.618932710883170.381067289116828
18943.060108493492270.939891506507734
1903-1.393571926115494.39357192611549
19132.914861644130960.0851383558690433
1924-3.130495466560437.13049546656043
19355.50321367004777-0.503213670047772
194515.7379695514650-10.7379695514650
195426.73659702224-22.73659702224






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.7260228495784110.5479543008431780.273977150421589
160.5865021759789910.8269956480420180.413497824021009
170.4483585959271450.896717191854290.551641404072855
180.3279286619883620.6558573239767240.672071338011638
190.2227837425665190.4455674851330380.777216257433481
200.1453570473314200.2907140946628410.85464295266858
210.08992492210249450.1798498442049890.910075077897506
220.05394651538434640.1078930307686930.946053484615654
230.03071975505699580.06143951011399150.969280244943004
240.01785721841822200.03571443683644410.982142781581778
250.009616597684187320.01923319536837460.990383402315813
260.005037551227187850.01007510245437570.994962448772812
270.002623013370360250.00524602674072050.99737698662964
280.001481285987564670.002962571975129340.998518714012435
290.0007019111249576180.001403822249915240.999298088875042
300.0003454334271685120.0006908668543370250.999654566572831
310.0002178939428256840.0004357878856513680.999782106057174
320.0001111967669430270.0002223935338860540.999888803233057
335.38781460423723e-050.0001077562920847450.999946121853958
343.35881149093987e-056.71762298187974e-050.99996641188509
351.77708100972868e-053.55416201945736e-050.999982229189903
368.92005580103997e-061.78401116020799e-050.999991079944199
374.30002820651557e-068.60005641303114e-060.999995699971793
381.87643309057254e-063.75286618114508e-060.99999812356691
399.7520780595726e-071.95041561191452e-060.999999024792194
403.9022624727108e-077.8045249454216e-070.999999609773753
412.51238558385337e-075.02477116770674e-070.999999748761442
421.04002302771591e-072.08004605543181e-070.999999895997697
435.9695121246782e-081.19390242493564e-070.999999940304879
443.58909148641945e-087.1781829728389e-080.999999964109085
451.47867839119145e-082.95735678238291e-080.999999985213216
465.608676522942e-091.1217353045884e-080.999999994391323
473.12584522371815e-096.2516904474363e-090.999999996874155
481.77995168098924e-093.55990336197848e-090.999999998220048
496.91450467674064e-101.38290093534813e-090.99999999930855
505.30693739602393e-101.06138747920479e-090.999999999469306
514.95843526817186e-109.91687053634373e-100.999999999504156
522.10804087657222e-104.21608175314444e-100.999999999789196
537.87832038312907e-101.57566407662581e-090.999999999212168
546.48539063629822e-091.29707812725964e-080.99999999351461
559.69230749034394e-091.93846149806879e-080.999999990307692
565.11438217917104e-050.0001022876435834210.999948856178208
570.0005321482319345840.001064296463869170.999467851768065
580.0003640229124301950.000728045824860390.99963597708757
590.004962578447781680.009925156895563350.995037421552218
600.003733242906124220.007466485812248440.996266757093876
610.004332316279090960.008664632558181920.99566768372091
620.01208152998582360.02416305997164730.987918470014176
630.01866453804803450.0373290760960690.981335461951966
640.01518884759583700.03037769519167400.984811152404163
650.03475957461267150.0695191492253430.965240425387328
660.6446274329175450.7107451341649090.355372567082455
670.8279599759492070.3440800481015850.172040024050793
680.995498806324550.009002387350901590.00450119367545079
690.9999995079695139.84060974674766e-074.92030487337383e-07
7011.75266381181848e-158.76331905909242e-16
7112.41501192897529e-191.20750596448765e-19
7211.31156119108330e-226.55780595541648e-23
7314.26047181738420e-262.13023590869210e-26
7411.16521809713750e-255.82609048568748e-26
7518.9833119088852e-264.4916559544426e-26
7611.19475099814726e-255.97375499073629e-26
7712.20036477666735e-271.10018238833368e-27
7815.21148960204935e-352.60574480102467e-35
7915.01729086429437e-362.50864543214719e-36
8013.32773286230618e-361.66386643115309e-36
8112.60227300593048e-371.30113650296524e-37
8212.61395175304350e-371.30697587652175e-37
8313.63126769915888e-411.81563384957944e-41
8412.84601866934973e-451.42300933467487e-45
8512.49046336878838e-781.24523168439419e-78
8612.19569407871294e-781.09784703935647e-78
8711.72976671580449e-838.64883357902245e-84
8811.41434594042334e-827.0717297021167e-83
8911.51755672465563e-817.58778362327814e-82
9011.69986073336014e-808.4993036668007e-81
9112.15905800105628e-791.07952900052814e-79
9211.25468568967617e-786.27342844838083e-79
9311.73454075378975e-778.67270376894877e-78
9412.10406774039786e-761.05203387019893e-76
9511.61649458817144e-758.08247294085722e-76
9611.97646728004634e-749.88233640023172e-75
9712.36542352007095e-731.18271176003547e-73
9813.18223477861625e-721.59111738930813e-72
9911.79087920402007e-718.95439602010037e-72
10011.94125578219323e-709.70627891096615e-71
10113.83878111263210e-701.91939055631605e-70
10215.71341880132168e-702.85670940066084e-70
10316.86751301840809e-693.43375650920405e-69
10418.67527452672934e-684.33763726336467e-68
10511.10627778300260e-665.53138891501298e-67
10611.37616289168856e-656.88081445844278e-66
10718.93188057097164e-654.46594028548582e-65
10814.99181380072592e-642.49590690036296e-64
10911.48075370128354e-637.4037685064177e-64
11014.50330024823391e-632.25165012411695e-63
11115.12525248701847e-622.56262624350924e-62
11217.44598605241934e-623.72299302620967e-62
11318.17720742779436e-614.08860371389718e-61
11413.35826688998422e-601.67913344499211e-60
11513.56328773196152e-591.78164386598076e-59
11613.43126549698685e-581.71563274849342e-58
11713.6039378857279e-571.80196894286395e-57
11813.83105609837453e-561.91552804918726e-56
11913.80001257458069e-551.90000628729035e-55
12014.34957321334738e-542.17478660667369e-54
12114.37287574767189e-532.18643787383594e-53
12213.84728910153595e-521.92364455076797e-52
12314.11387141142108e-512.05693570571054e-51
12414.26033009014125e-502.13016504507062e-50
12513.76094943254631e-491.88047471627315e-49
12611.25928982296381e-486.29644911481904e-49
12713.6213483221012e-481.8106741610506e-48
12813.04148487575758e-471.52074243787879e-47
12912.53149867144638e-461.26574933572319e-46
13012.35081473931609e-451.17540736965804e-45
13113.69215345068546e-451.84607672534273e-45
13214.11922165779359e-442.05961082889679e-44
13314.36378457523325e-432.18189228761663e-43
13411.50277493017172e-427.51387465085858e-43
13513.47862137457045e-421.73931068728522e-42
13618.64212623953196e-424.32106311976598e-42
13718.67672746054375e-414.33836373027188e-41
13818.85315146480257e-404.42657573240128e-40
13918.59778997447555e-394.29889498723778e-39
14018.21588729971402e-384.10794364985701e-38
14118.15168636954534e-374.07584318477267e-37
14211.67428932473843e-368.37144662369216e-37
14312.09138467186446e-361.04569233593223e-36
14412.23460731429781e-351.11730365714890e-35
14512.12190401115084e-341.06095200557542e-34
14612.36483068396902e-331.18241534198451e-33
14712.10185601813747e-321.05092800906873e-32
14812.03719627982217e-311.01859813991109e-31
14914.50919079680286e-312.25459539840143e-31
15014.84096202912957e-302.42048101456478e-30
15113.80080197658487e-291.90040098829244e-29
15213.63353321065016e-281.81676660532508e-28
15313.78864658892413e-271.89432329446207e-27
15412.26706698664794e-261.13353349332397e-26
15512.62724353429181e-251.31362176714590e-25
15612.40116182938439e-241.20058091469220e-24
15711.96452098714578e-239.8226049357289e-24
15811.72628350504972e-228.6314175252486e-23
15911.42553112085325e-217.12765560426626e-22
16011.56696689193833e-207.83483445969164e-21
16111.73786362259832e-198.68931811299161e-20
16212.59062256866741e-191.29531128433371e-19
16312.90734916838291e-181.45367458419145e-18
16412.042321156875e-171.0211605784375e-17
16511.75300264226726e-168.7650132113363e-17
1660.9999999999999992.07332466429811e-151.03666233214905e-15
1670.999999999999992.13026496974066e-141.06513248487033e-14
1680.9999999999999461.08856315509954e-135.4428157754977e-14
1690.999999999999411.17858597814817e-125.89292989074085e-13
1700.9999999999951669.66847469413007e-124.83423734706504e-12
1710.9999999999527869.44283310520991e-114.72141655260496e-11
1720.9999999995391639.2167412350288e-104.6083706175144e-10
1730.9999999963180787.3638436430116e-093.6819218215058e-09
1740.999999967263786.54724395095859e-083.27362197547929e-08
1750.9999996928741936.14251614105641e-073.07125807052820e-07
1760.9999980001720543.99965589189032e-061.99982794594516e-06
1770.9999882329150362.35341699279646e-051.17670849639823e-05
1780.9998983347074260.0002033305851476950.000101665292573848
1790.9991721478236190.001655704352762190.000827852176381093
1800.9941237359700310.01175252805993710.00587626402996856

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.726022849578411 & 0.547954300843178 & 0.273977150421589 \tabularnewline
16 & 0.586502175978991 & 0.826995648042018 & 0.413497824021009 \tabularnewline
17 & 0.448358595927145 & 0.89671719185429 & 0.551641404072855 \tabularnewline
18 & 0.327928661988362 & 0.655857323976724 & 0.672071338011638 \tabularnewline
19 & 0.222783742566519 & 0.445567485133038 & 0.777216257433481 \tabularnewline
20 & 0.145357047331420 & 0.290714094662841 & 0.85464295266858 \tabularnewline
21 & 0.0899249221024945 & 0.179849844204989 & 0.910075077897506 \tabularnewline
22 & 0.0539465153843464 & 0.107893030768693 & 0.946053484615654 \tabularnewline
23 & 0.0307197550569958 & 0.0614395101139915 & 0.969280244943004 \tabularnewline
24 & 0.0178572184182220 & 0.0357144368364441 & 0.982142781581778 \tabularnewline
25 & 0.00961659768418732 & 0.0192331953683746 & 0.990383402315813 \tabularnewline
26 & 0.00503755122718785 & 0.0100751024543757 & 0.994962448772812 \tabularnewline
27 & 0.00262301337036025 & 0.0052460267407205 & 0.99737698662964 \tabularnewline
28 & 0.00148128598756467 & 0.00296257197512934 & 0.998518714012435 \tabularnewline
29 & 0.000701911124957618 & 0.00140382224991524 & 0.999298088875042 \tabularnewline
30 & 0.000345433427168512 & 0.000690866854337025 & 0.999654566572831 \tabularnewline
31 & 0.000217893942825684 & 0.000435787885651368 & 0.999782106057174 \tabularnewline
32 & 0.000111196766943027 & 0.000222393533886054 & 0.999888803233057 \tabularnewline
33 & 5.38781460423723e-05 & 0.000107756292084745 & 0.999946121853958 \tabularnewline
34 & 3.35881149093987e-05 & 6.71762298187974e-05 & 0.99996641188509 \tabularnewline
35 & 1.77708100972868e-05 & 3.55416201945736e-05 & 0.999982229189903 \tabularnewline
36 & 8.92005580103997e-06 & 1.78401116020799e-05 & 0.999991079944199 \tabularnewline
37 & 4.30002820651557e-06 & 8.60005641303114e-06 & 0.999995699971793 \tabularnewline
38 & 1.87643309057254e-06 & 3.75286618114508e-06 & 0.99999812356691 \tabularnewline
39 & 9.7520780595726e-07 & 1.95041561191452e-06 & 0.999999024792194 \tabularnewline
40 & 3.9022624727108e-07 & 7.8045249454216e-07 & 0.999999609773753 \tabularnewline
41 & 2.51238558385337e-07 & 5.02477116770674e-07 & 0.999999748761442 \tabularnewline
42 & 1.04002302771591e-07 & 2.08004605543181e-07 & 0.999999895997697 \tabularnewline
43 & 5.9695121246782e-08 & 1.19390242493564e-07 & 0.999999940304879 \tabularnewline
44 & 3.58909148641945e-08 & 7.1781829728389e-08 & 0.999999964109085 \tabularnewline
45 & 1.47867839119145e-08 & 2.95735678238291e-08 & 0.999999985213216 \tabularnewline
46 & 5.608676522942e-09 & 1.1217353045884e-08 & 0.999999994391323 \tabularnewline
47 & 3.12584522371815e-09 & 6.2516904474363e-09 & 0.999999996874155 \tabularnewline
48 & 1.77995168098924e-09 & 3.55990336197848e-09 & 0.999999998220048 \tabularnewline
49 & 6.91450467674064e-10 & 1.38290093534813e-09 & 0.99999999930855 \tabularnewline
50 & 5.30693739602393e-10 & 1.06138747920479e-09 & 0.999999999469306 \tabularnewline
51 & 4.95843526817186e-10 & 9.91687053634373e-10 & 0.999999999504156 \tabularnewline
52 & 2.10804087657222e-10 & 4.21608175314444e-10 & 0.999999999789196 \tabularnewline
53 & 7.87832038312907e-10 & 1.57566407662581e-09 & 0.999999999212168 \tabularnewline
54 & 6.48539063629822e-09 & 1.29707812725964e-08 & 0.99999999351461 \tabularnewline
55 & 9.69230749034394e-09 & 1.93846149806879e-08 & 0.999999990307692 \tabularnewline
56 & 5.11438217917104e-05 & 0.000102287643583421 & 0.999948856178208 \tabularnewline
57 & 0.000532148231934584 & 0.00106429646386917 & 0.999467851768065 \tabularnewline
58 & 0.000364022912430195 & 0.00072804582486039 & 0.99963597708757 \tabularnewline
59 & 0.00496257844778168 & 0.00992515689556335 & 0.995037421552218 \tabularnewline
60 & 0.00373324290612422 & 0.00746648581224844 & 0.996266757093876 \tabularnewline
61 & 0.00433231627909096 & 0.00866463255818192 & 0.99566768372091 \tabularnewline
62 & 0.0120815299858236 & 0.0241630599716473 & 0.987918470014176 \tabularnewline
63 & 0.0186645380480345 & 0.037329076096069 & 0.981335461951966 \tabularnewline
64 & 0.0151888475958370 & 0.0303776951916740 & 0.984811152404163 \tabularnewline
65 & 0.0347595746126715 & 0.069519149225343 & 0.965240425387328 \tabularnewline
66 & 0.644627432917545 & 0.710745134164909 & 0.355372567082455 \tabularnewline
67 & 0.827959975949207 & 0.344080048101585 & 0.172040024050793 \tabularnewline
68 & 0.99549880632455 & 0.00900238735090159 & 0.00450119367545079 \tabularnewline
69 & 0.999999507969513 & 9.84060974674766e-07 & 4.92030487337383e-07 \tabularnewline
70 & 1 & 1.75266381181848e-15 & 8.76331905909242e-16 \tabularnewline
71 & 1 & 2.41501192897529e-19 & 1.20750596448765e-19 \tabularnewline
72 & 1 & 1.31156119108330e-22 & 6.55780595541648e-23 \tabularnewline
73 & 1 & 4.26047181738420e-26 & 2.13023590869210e-26 \tabularnewline
74 & 1 & 1.16521809713750e-25 & 5.82609048568748e-26 \tabularnewline
75 & 1 & 8.9833119088852e-26 & 4.4916559544426e-26 \tabularnewline
76 & 1 & 1.19475099814726e-25 & 5.97375499073629e-26 \tabularnewline
77 & 1 & 2.20036477666735e-27 & 1.10018238833368e-27 \tabularnewline
78 & 1 & 5.21148960204935e-35 & 2.60574480102467e-35 \tabularnewline
79 & 1 & 5.01729086429437e-36 & 2.50864543214719e-36 \tabularnewline
80 & 1 & 3.32773286230618e-36 & 1.66386643115309e-36 \tabularnewline
81 & 1 & 2.60227300593048e-37 & 1.30113650296524e-37 \tabularnewline
82 & 1 & 2.61395175304350e-37 & 1.30697587652175e-37 \tabularnewline
83 & 1 & 3.63126769915888e-41 & 1.81563384957944e-41 \tabularnewline
84 & 1 & 2.84601866934973e-45 & 1.42300933467487e-45 \tabularnewline
85 & 1 & 2.49046336878838e-78 & 1.24523168439419e-78 \tabularnewline
86 & 1 & 2.19569407871294e-78 & 1.09784703935647e-78 \tabularnewline
87 & 1 & 1.72976671580449e-83 & 8.64883357902245e-84 \tabularnewline
88 & 1 & 1.41434594042334e-82 & 7.0717297021167e-83 \tabularnewline
89 & 1 & 1.51755672465563e-81 & 7.58778362327814e-82 \tabularnewline
90 & 1 & 1.69986073336014e-80 & 8.4993036668007e-81 \tabularnewline
91 & 1 & 2.15905800105628e-79 & 1.07952900052814e-79 \tabularnewline
92 & 1 & 1.25468568967617e-78 & 6.27342844838083e-79 \tabularnewline
93 & 1 & 1.73454075378975e-77 & 8.67270376894877e-78 \tabularnewline
94 & 1 & 2.10406774039786e-76 & 1.05203387019893e-76 \tabularnewline
95 & 1 & 1.61649458817144e-75 & 8.08247294085722e-76 \tabularnewline
96 & 1 & 1.97646728004634e-74 & 9.88233640023172e-75 \tabularnewline
97 & 1 & 2.36542352007095e-73 & 1.18271176003547e-73 \tabularnewline
98 & 1 & 3.18223477861625e-72 & 1.59111738930813e-72 \tabularnewline
99 & 1 & 1.79087920402007e-71 & 8.95439602010037e-72 \tabularnewline
100 & 1 & 1.94125578219323e-70 & 9.70627891096615e-71 \tabularnewline
101 & 1 & 3.83878111263210e-70 & 1.91939055631605e-70 \tabularnewline
102 & 1 & 5.71341880132168e-70 & 2.85670940066084e-70 \tabularnewline
103 & 1 & 6.86751301840809e-69 & 3.43375650920405e-69 \tabularnewline
104 & 1 & 8.67527452672934e-68 & 4.33763726336467e-68 \tabularnewline
105 & 1 & 1.10627778300260e-66 & 5.53138891501298e-67 \tabularnewline
106 & 1 & 1.37616289168856e-65 & 6.88081445844278e-66 \tabularnewline
107 & 1 & 8.93188057097164e-65 & 4.46594028548582e-65 \tabularnewline
108 & 1 & 4.99181380072592e-64 & 2.49590690036296e-64 \tabularnewline
109 & 1 & 1.48075370128354e-63 & 7.4037685064177e-64 \tabularnewline
110 & 1 & 4.50330024823391e-63 & 2.25165012411695e-63 \tabularnewline
111 & 1 & 5.12525248701847e-62 & 2.56262624350924e-62 \tabularnewline
112 & 1 & 7.44598605241934e-62 & 3.72299302620967e-62 \tabularnewline
113 & 1 & 8.17720742779436e-61 & 4.08860371389718e-61 \tabularnewline
114 & 1 & 3.35826688998422e-60 & 1.67913344499211e-60 \tabularnewline
115 & 1 & 3.56328773196152e-59 & 1.78164386598076e-59 \tabularnewline
116 & 1 & 3.43126549698685e-58 & 1.71563274849342e-58 \tabularnewline
117 & 1 & 3.6039378857279e-57 & 1.80196894286395e-57 \tabularnewline
118 & 1 & 3.83105609837453e-56 & 1.91552804918726e-56 \tabularnewline
119 & 1 & 3.80001257458069e-55 & 1.90000628729035e-55 \tabularnewline
120 & 1 & 4.34957321334738e-54 & 2.17478660667369e-54 \tabularnewline
121 & 1 & 4.37287574767189e-53 & 2.18643787383594e-53 \tabularnewline
122 & 1 & 3.84728910153595e-52 & 1.92364455076797e-52 \tabularnewline
123 & 1 & 4.11387141142108e-51 & 2.05693570571054e-51 \tabularnewline
124 & 1 & 4.26033009014125e-50 & 2.13016504507062e-50 \tabularnewline
125 & 1 & 3.76094943254631e-49 & 1.88047471627315e-49 \tabularnewline
126 & 1 & 1.25928982296381e-48 & 6.29644911481904e-49 \tabularnewline
127 & 1 & 3.6213483221012e-48 & 1.8106741610506e-48 \tabularnewline
128 & 1 & 3.04148487575758e-47 & 1.52074243787879e-47 \tabularnewline
129 & 1 & 2.53149867144638e-46 & 1.26574933572319e-46 \tabularnewline
130 & 1 & 2.35081473931609e-45 & 1.17540736965804e-45 \tabularnewline
131 & 1 & 3.69215345068546e-45 & 1.84607672534273e-45 \tabularnewline
132 & 1 & 4.11922165779359e-44 & 2.05961082889679e-44 \tabularnewline
133 & 1 & 4.36378457523325e-43 & 2.18189228761663e-43 \tabularnewline
134 & 1 & 1.50277493017172e-42 & 7.51387465085858e-43 \tabularnewline
135 & 1 & 3.47862137457045e-42 & 1.73931068728522e-42 \tabularnewline
136 & 1 & 8.64212623953196e-42 & 4.32106311976598e-42 \tabularnewline
137 & 1 & 8.67672746054375e-41 & 4.33836373027188e-41 \tabularnewline
138 & 1 & 8.85315146480257e-40 & 4.42657573240128e-40 \tabularnewline
139 & 1 & 8.59778997447555e-39 & 4.29889498723778e-39 \tabularnewline
140 & 1 & 8.21588729971402e-38 & 4.10794364985701e-38 \tabularnewline
141 & 1 & 8.15168636954534e-37 & 4.07584318477267e-37 \tabularnewline
142 & 1 & 1.67428932473843e-36 & 8.37144662369216e-37 \tabularnewline
143 & 1 & 2.09138467186446e-36 & 1.04569233593223e-36 \tabularnewline
144 & 1 & 2.23460731429781e-35 & 1.11730365714890e-35 \tabularnewline
145 & 1 & 2.12190401115084e-34 & 1.06095200557542e-34 \tabularnewline
146 & 1 & 2.36483068396902e-33 & 1.18241534198451e-33 \tabularnewline
147 & 1 & 2.10185601813747e-32 & 1.05092800906873e-32 \tabularnewline
148 & 1 & 2.03719627982217e-31 & 1.01859813991109e-31 \tabularnewline
149 & 1 & 4.50919079680286e-31 & 2.25459539840143e-31 \tabularnewline
150 & 1 & 4.84096202912957e-30 & 2.42048101456478e-30 \tabularnewline
151 & 1 & 3.80080197658487e-29 & 1.90040098829244e-29 \tabularnewline
152 & 1 & 3.63353321065016e-28 & 1.81676660532508e-28 \tabularnewline
153 & 1 & 3.78864658892413e-27 & 1.89432329446207e-27 \tabularnewline
154 & 1 & 2.26706698664794e-26 & 1.13353349332397e-26 \tabularnewline
155 & 1 & 2.62724353429181e-25 & 1.31362176714590e-25 \tabularnewline
156 & 1 & 2.40116182938439e-24 & 1.20058091469220e-24 \tabularnewline
157 & 1 & 1.96452098714578e-23 & 9.8226049357289e-24 \tabularnewline
158 & 1 & 1.72628350504972e-22 & 8.6314175252486e-23 \tabularnewline
159 & 1 & 1.42553112085325e-21 & 7.12765560426626e-22 \tabularnewline
160 & 1 & 1.56696689193833e-20 & 7.83483445969164e-21 \tabularnewline
161 & 1 & 1.73786362259832e-19 & 8.68931811299161e-20 \tabularnewline
162 & 1 & 2.59062256866741e-19 & 1.29531128433371e-19 \tabularnewline
163 & 1 & 2.90734916838291e-18 & 1.45367458419145e-18 \tabularnewline
164 & 1 & 2.042321156875e-17 & 1.0211605784375e-17 \tabularnewline
165 & 1 & 1.75300264226726e-16 & 8.7650132113363e-17 \tabularnewline
166 & 0.999999999999999 & 2.07332466429811e-15 & 1.03666233214905e-15 \tabularnewline
167 & 0.99999999999999 & 2.13026496974066e-14 & 1.06513248487033e-14 \tabularnewline
168 & 0.999999999999946 & 1.08856315509954e-13 & 5.4428157754977e-14 \tabularnewline
169 & 0.99999999999941 & 1.17858597814817e-12 & 5.89292989074085e-13 \tabularnewline
170 & 0.999999999995166 & 9.66847469413007e-12 & 4.83423734706504e-12 \tabularnewline
171 & 0.999999999952786 & 9.44283310520991e-11 & 4.72141655260496e-11 \tabularnewline
172 & 0.999999999539163 & 9.2167412350288e-10 & 4.6083706175144e-10 \tabularnewline
173 & 0.999999996318078 & 7.3638436430116e-09 & 3.6819218215058e-09 \tabularnewline
174 & 0.99999996726378 & 6.54724395095859e-08 & 3.27362197547929e-08 \tabularnewline
175 & 0.999999692874193 & 6.14251614105641e-07 & 3.07125807052820e-07 \tabularnewline
176 & 0.999998000172054 & 3.99965589189032e-06 & 1.99982794594516e-06 \tabularnewline
177 & 0.999988232915036 & 2.35341699279646e-05 & 1.17670849639823e-05 \tabularnewline
178 & 0.999898334707426 & 0.000203330585147695 & 0.000101665292573848 \tabularnewline
179 & 0.999172147823619 & 0.00165570435276219 & 0.000827852176381093 \tabularnewline
180 & 0.994123735970031 & 0.0117525280599371 & 0.00587626402996856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&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]15[/C][C]0.726022849578411[/C][C]0.547954300843178[/C][C]0.273977150421589[/C][/ROW]
[ROW][C]16[/C][C]0.586502175978991[/C][C]0.826995648042018[/C][C]0.413497824021009[/C][/ROW]
[ROW][C]17[/C][C]0.448358595927145[/C][C]0.89671719185429[/C][C]0.551641404072855[/C][/ROW]
[ROW][C]18[/C][C]0.327928661988362[/C][C]0.655857323976724[/C][C]0.672071338011638[/C][/ROW]
[ROW][C]19[/C][C]0.222783742566519[/C][C]0.445567485133038[/C][C]0.777216257433481[/C][/ROW]
[ROW][C]20[/C][C]0.145357047331420[/C][C]0.290714094662841[/C][C]0.85464295266858[/C][/ROW]
[ROW][C]21[/C][C]0.0899249221024945[/C][C]0.179849844204989[/C][C]0.910075077897506[/C][/ROW]
[ROW][C]22[/C][C]0.0539465153843464[/C][C]0.107893030768693[/C][C]0.946053484615654[/C][/ROW]
[ROW][C]23[/C][C]0.0307197550569958[/C][C]0.0614395101139915[/C][C]0.969280244943004[/C][/ROW]
[ROW][C]24[/C][C]0.0178572184182220[/C][C]0.0357144368364441[/C][C]0.982142781581778[/C][/ROW]
[ROW][C]25[/C][C]0.00961659768418732[/C][C]0.0192331953683746[/C][C]0.990383402315813[/C][/ROW]
[ROW][C]26[/C][C]0.00503755122718785[/C][C]0.0100751024543757[/C][C]0.994962448772812[/C][/ROW]
[ROW][C]27[/C][C]0.00262301337036025[/C][C]0.0052460267407205[/C][C]0.99737698662964[/C][/ROW]
[ROW][C]28[/C][C]0.00148128598756467[/C][C]0.00296257197512934[/C][C]0.998518714012435[/C][/ROW]
[ROW][C]29[/C][C]0.000701911124957618[/C][C]0.00140382224991524[/C][C]0.999298088875042[/C][/ROW]
[ROW][C]30[/C][C]0.000345433427168512[/C][C]0.000690866854337025[/C][C]0.999654566572831[/C][/ROW]
[ROW][C]31[/C][C]0.000217893942825684[/C][C]0.000435787885651368[/C][C]0.999782106057174[/C][/ROW]
[ROW][C]32[/C][C]0.000111196766943027[/C][C]0.000222393533886054[/C][C]0.999888803233057[/C][/ROW]
[ROW][C]33[/C][C]5.38781460423723e-05[/C][C]0.000107756292084745[/C][C]0.999946121853958[/C][/ROW]
[ROW][C]34[/C][C]3.35881149093987e-05[/C][C]6.71762298187974e-05[/C][C]0.99996641188509[/C][/ROW]
[ROW][C]35[/C][C]1.77708100972868e-05[/C][C]3.55416201945736e-05[/C][C]0.999982229189903[/C][/ROW]
[ROW][C]36[/C][C]8.92005580103997e-06[/C][C]1.78401116020799e-05[/C][C]0.999991079944199[/C][/ROW]
[ROW][C]37[/C][C]4.30002820651557e-06[/C][C]8.60005641303114e-06[/C][C]0.999995699971793[/C][/ROW]
[ROW][C]38[/C][C]1.87643309057254e-06[/C][C]3.75286618114508e-06[/C][C]0.99999812356691[/C][/ROW]
[ROW][C]39[/C][C]9.7520780595726e-07[/C][C]1.95041561191452e-06[/C][C]0.999999024792194[/C][/ROW]
[ROW][C]40[/C][C]3.9022624727108e-07[/C][C]7.8045249454216e-07[/C][C]0.999999609773753[/C][/ROW]
[ROW][C]41[/C][C]2.51238558385337e-07[/C][C]5.02477116770674e-07[/C][C]0.999999748761442[/C][/ROW]
[ROW][C]42[/C][C]1.04002302771591e-07[/C][C]2.08004605543181e-07[/C][C]0.999999895997697[/C][/ROW]
[ROW][C]43[/C][C]5.9695121246782e-08[/C][C]1.19390242493564e-07[/C][C]0.999999940304879[/C][/ROW]
[ROW][C]44[/C][C]3.58909148641945e-08[/C][C]7.1781829728389e-08[/C][C]0.999999964109085[/C][/ROW]
[ROW][C]45[/C][C]1.47867839119145e-08[/C][C]2.95735678238291e-08[/C][C]0.999999985213216[/C][/ROW]
[ROW][C]46[/C][C]5.608676522942e-09[/C][C]1.1217353045884e-08[/C][C]0.999999994391323[/C][/ROW]
[ROW][C]47[/C][C]3.12584522371815e-09[/C][C]6.2516904474363e-09[/C][C]0.999999996874155[/C][/ROW]
[ROW][C]48[/C][C]1.77995168098924e-09[/C][C]3.55990336197848e-09[/C][C]0.999999998220048[/C][/ROW]
[ROW][C]49[/C][C]6.91450467674064e-10[/C][C]1.38290093534813e-09[/C][C]0.99999999930855[/C][/ROW]
[ROW][C]50[/C][C]5.30693739602393e-10[/C][C]1.06138747920479e-09[/C][C]0.999999999469306[/C][/ROW]
[ROW][C]51[/C][C]4.95843526817186e-10[/C][C]9.91687053634373e-10[/C][C]0.999999999504156[/C][/ROW]
[ROW][C]52[/C][C]2.10804087657222e-10[/C][C]4.21608175314444e-10[/C][C]0.999999999789196[/C][/ROW]
[ROW][C]53[/C][C]7.87832038312907e-10[/C][C]1.57566407662581e-09[/C][C]0.999999999212168[/C][/ROW]
[ROW][C]54[/C][C]6.48539063629822e-09[/C][C]1.29707812725964e-08[/C][C]0.99999999351461[/C][/ROW]
[ROW][C]55[/C][C]9.69230749034394e-09[/C][C]1.93846149806879e-08[/C][C]0.999999990307692[/C][/ROW]
[ROW][C]56[/C][C]5.11438217917104e-05[/C][C]0.000102287643583421[/C][C]0.999948856178208[/C][/ROW]
[ROW][C]57[/C][C]0.000532148231934584[/C][C]0.00106429646386917[/C][C]0.999467851768065[/C][/ROW]
[ROW][C]58[/C][C]0.000364022912430195[/C][C]0.00072804582486039[/C][C]0.99963597708757[/C][/ROW]
[ROW][C]59[/C][C]0.00496257844778168[/C][C]0.00992515689556335[/C][C]0.995037421552218[/C][/ROW]
[ROW][C]60[/C][C]0.00373324290612422[/C][C]0.00746648581224844[/C][C]0.996266757093876[/C][/ROW]
[ROW][C]61[/C][C]0.00433231627909096[/C][C]0.00866463255818192[/C][C]0.99566768372091[/C][/ROW]
[ROW][C]62[/C][C]0.0120815299858236[/C][C]0.0241630599716473[/C][C]0.987918470014176[/C][/ROW]
[ROW][C]63[/C][C]0.0186645380480345[/C][C]0.037329076096069[/C][C]0.981335461951966[/C][/ROW]
[ROW][C]64[/C][C]0.0151888475958370[/C][C]0.0303776951916740[/C][C]0.984811152404163[/C][/ROW]
[ROW][C]65[/C][C]0.0347595746126715[/C][C]0.069519149225343[/C][C]0.965240425387328[/C][/ROW]
[ROW][C]66[/C][C]0.644627432917545[/C][C]0.710745134164909[/C][C]0.355372567082455[/C][/ROW]
[ROW][C]67[/C][C]0.827959975949207[/C][C]0.344080048101585[/C][C]0.172040024050793[/C][/ROW]
[ROW][C]68[/C][C]0.99549880632455[/C][C]0.00900238735090159[/C][C]0.00450119367545079[/C][/ROW]
[ROW][C]69[/C][C]0.999999507969513[/C][C]9.84060974674766e-07[/C][C]4.92030487337383e-07[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.75266381181848e-15[/C][C]8.76331905909242e-16[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]2.41501192897529e-19[/C][C]1.20750596448765e-19[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.31156119108330e-22[/C][C]6.55780595541648e-23[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]4.26047181738420e-26[/C][C]2.13023590869210e-26[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.16521809713750e-25[/C][C]5.82609048568748e-26[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]8.9833119088852e-26[/C][C]4.4916559544426e-26[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.19475099814726e-25[/C][C]5.97375499073629e-26[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]2.20036477666735e-27[/C][C]1.10018238833368e-27[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]5.21148960204935e-35[/C][C]2.60574480102467e-35[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]5.01729086429437e-36[/C][C]2.50864543214719e-36[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]3.32773286230618e-36[/C][C]1.66386643115309e-36[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.60227300593048e-37[/C][C]1.30113650296524e-37[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]2.61395175304350e-37[/C][C]1.30697587652175e-37[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]3.63126769915888e-41[/C][C]1.81563384957944e-41[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]2.84601866934973e-45[/C][C]1.42300933467487e-45[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]2.49046336878838e-78[/C][C]1.24523168439419e-78[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]2.19569407871294e-78[/C][C]1.09784703935647e-78[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.72976671580449e-83[/C][C]8.64883357902245e-84[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.41434594042334e-82[/C][C]7.0717297021167e-83[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.51755672465563e-81[/C][C]7.58778362327814e-82[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.69986073336014e-80[/C][C]8.4993036668007e-81[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]2.15905800105628e-79[/C][C]1.07952900052814e-79[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.25468568967617e-78[/C][C]6.27342844838083e-79[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.73454075378975e-77[/C][C]8.67270376894877e-78[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]2.10406774039786e-76[/C][C]1.05203387019893e-76[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.61649458817144e-75[/C][C]8.08247294085722e-76[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.97646728004634e-74[/C][C]9.88233640023172e-75[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]2.36542352007095e-73[/C][C]1.18271176003547e-73[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]3.18223477861625e-72[/C][C]1.59111738930813e-72[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.79087920402007e-71[/C][C]8.95439602010037e-72[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.94125578219323e-70[/C][C]9.70627891096615e-71[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]3.83878111263210e-70[/C][C]1.91939055631605e-70[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]5.71341880132168e-70[/C][C]2.85670940066084e-70[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]6.86751301840809e-69[/C][C]3.43375650920405e-69[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]8.67527452672934e-68[/C][C]4.33763726336467e-68[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.10627778300260e-66[/C][C]5.53138891501298e-67[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.37616289168856e-65[/C][C]6.88081445844278e-66[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]8.93188057097164e-65[/C][C]4.46594028548582e-65[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]4.99181380072592e-64[/C][C]2.49590690036296e-64[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.48075370128354e-63[/C][C]7.4037685064177e-64[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]4.50330024823391e-63[/C][C]2.25165012411695e-63[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]5.12525248701847e-62[/C][C]2.56262624350924e-62[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]7.44598605241934e-62[/C][C]3.72299302620967e-62[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]8.17720742779436e-61[/C][C]4.08860371389718e-61[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]3.35826688998422e-60[/C][C]1.67913344499211e-60[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]3.56328773196152e-59[/C][C]1.78164386598076e-59[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]3.43126549698685e-58[/C][C]1.71563274849342e-58[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]3.6039378857279e-57[/C][C]1.80196894286395e-57[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]3.83105609837453e-56[/C][C]1.91552804918726e-56[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]3.80001257458069e-55[/C][C]1.90000628729035e-55[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]4.34957321334738e-54[/C][C]2.17478660667369e-54[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]4.37287574767189e-53[/C][C]2.18643787383594e-53[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]3.84728910153595e-52[/C][C]1.92364455076797e-52[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]4.11387141142108e-51[/C][C]2.05693570571054e-51[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]4.26033009014125e-50[/C][C]2.13016504507062e-50[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]3.76094943254631e-49[/C][C]1.88047471627315e-49[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.25928982296381e-48[/C][C]6.29644911481904e-49[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]3.6213483221012e-48[/C][C]1.8106741610506e-48[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]3.04148487575758e-47[/C][C]1.52074243787879e-47[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]2.53149867144638e-46[/C][C]1.26574933572319e-46[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]2.35081473931609e-45[/C][C]1.17540736965804e-45[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]3.69215345068546e-45[/C][C]1.84607672534273e-45[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.11922165779359e-44[/C][C]2.05961082889679e-44[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]4.36378457523325e-43[/C][C]2.18189228761663e-43[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.50277493017172e-42[/C][C]7.51387465085858e-43[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]3.47862137457045e-42[/C][C]1.73931068728522e-42[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]8.64212623953196e-42[/C][C]4.32106311976598e-42[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]8.67672746054375e-41[/C][C]4.33836373027188e-41[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]8.85315146480257e-40[/C][C]4.42657573240128e-40[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]8.59778997447555e-39[/C][C]4.29889498723778e-39[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]8.21588729971402e-38[/C][C]4.10794364985701e-38[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]8.15168636954534e-37[/C][C]4.07584318477267e-37[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.67428932473843e-36[/C][C]8.37144662369216e-37[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]2.09138467186446e-36[/C][C]1.04569233593223e-36[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]2.23460731429781e-35[/C][C]1.11730365714890e-35[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]2.12190401115084e-34[/C][C]1.06095200557542e-34[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]2.36483068396902e-33[/C][C]1.18241534198451e-33[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]2.10185601813747e-32[/C][C]1.05092800906873e-32[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]2.03719627982217e-31[/C][C]1.01859813991109e-31[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]4.50919079680286e-31[/C][C]2.25459539840143e-31[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]4.84096202912957e-30[/C][C]2.42048101456478e-30[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]3.80080197658487e-29[/C][C]1.90040098829244e-29[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]3.63353321065016e-28[/C][C]1.81676660532508e-28[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]3.78864658892413e-27[/C][C]1.89432329446207e-27[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]2.26706698664794e-26[/C][C]1.13353349332397e-26[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]2.62724353429181e-25[/C][C]1.31362176714590e-25[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]2.40116182938439e-24[/C][C]1.20058091469220e-24[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.96452098714578e-23[/C][C]9.8226049357289e-24[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.72628350504972e-22[/C][C]8.6314175252486e-23[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.42553112085325e-21[/C][C]7.12765560426626e-22[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.56696689193833e-20[/C][C]7.83483445969164e-21[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.73786362259832e-19[/C][C]8.68931811299161e-20[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]2.59062256866741e-19[/C][C]1.29531128433371e-19[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]2.90734916838291e-18[/C][C]1.45367458419145e-18[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]2.042321156875e-17[/C][C]1.0211605784375e-17[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.75300264226726e-16[/C][C]8.7650132113363e-17[/C][/ROW]
[ROW][C]166[/C][C]0.999999999999999[/C][C]2.07332466429811e-15[/C][C]1.03666233214905e-15[/C][/ROW]
[ROW][C]167[/C][C]0.99999999999999[/C][C]2.13026496974066e-14[/C][C]1.06513248487033e-14[/C][/ROW]
[ROW][C]168[/C][C]0.999999999999946[/C][C]1.08856315509954e-13[/C][C]5.4428157754977e-14[/C][/ROW]
[ROW][C]169[/C][C]0.99999999999941[/C][C]1.17858597814817e-12[/C][C]5.89292989074085e-13[/C][/ROW]
[ROW][C]170[/C][C]0.999999999995166[/C][C]9.66847469413007e-12[/C][C]4.83423734706504e-12[/C][/ROW]
[ROW][C]171[/C][C]0.999999999952786[/C][C]9.44283310520991e-11[/C][C]4.72141655260496e-11[/C][/ROW]
[ROW][C]172[/C][C]0.999999999539163[/C][C]9.2167412350288e-10[/C][C]4.6083706175144e-10[/C][/ROW]
[ROW][C]173[/C][C]0.999999996318078[/C][C]7.3638436430116e-09[/C][C]3.6819218215058e-09[/C][/ROW]
[ROW][C]174[/C][C]0.99999996726378[/C][C]6.54724395095859e-08[/C][C]3.27362197547929e-08[/C][/ROW]
[ROW][C]175[/C][C]0.999999692874193[/C][C]6.14251614105641e-07[/C][C]3.07125807052820e-07[/C][/ROW]
[ROW][C]176[/C][C]0.999998000172054[/C][C]3.99965589189032e-06[/C][C]1.99982794594516e-06[/C][/ROW]
[ROW][C]177[/C][C]0.999988232915036[/C][C]2.35341699279646e-05[/C][C]1.17670849639823e-05[/C][/ROW]
[ROW][C]178[/C][C]0.999898334707426[/C][C]0.000203330585147695[/C][C]0.000101665292573848[/C][/ROW]
[ROW][C]179[/C][C]0.999172147823619[/C][C]0.00165570435276219[/C][C]0.000827852176381093[/C][/ROW]
[ROW][C]180[/C][C]0.994123735970031[/C][C]0.0117525280599371[/C][C]0.00587626402996856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.7260228495784110.5479543008431780.273977150421589
160.5865021759789910.8269956480420180.413497824021009
170.4483585959271450.896717191854290.551641404072855
180.3279286619883620.6558573239767240.672071338011638
190.2227837425665190.4455674851330380.777216257433481
200.1453570473314200.2907140946628410.85464295266858
210.08992492210249450.1798498442049890.910075077897506
220.05394651538434640.1078930307686930.946053484615654
230.03071975505699580.06143951011399150.969280244943004
240.01785721841822200.03571443683644410.982142781581778
250.009616597684187320.01923319536837460.990383402315813
260.005037551227187850.01007510245437570.994962448772812
270.002623013370360250.00524602674072050.99737698662964
280.001481285987564670.002962571975129340.998518714012435
290.0007019111249576180.001403822249915240.999298088875042
300.0003454334271685120.0006908668543370250.999654566572831
310.0002178939428256840.0004357878856513680.999782106057174
320.0001111967669430270.0002223935338860540.999888803233057
335.38781460423723e-050.0001077562920847450.999946121853958
343.35881149093987e-056.71762298187974e-050.99996641188509
351.77708100972868e-053.55416201945736e-050.999982229189903
368.92005580103997e-061.78401116020799e-050.999991079944199
374.30002820651557e-068.60005641303114e-060.999995699971793
381.87643309057254e-063.75286618114508e-060.99999812356691
399.7520780595726e-071.95041561191452e-060.999999024792194
403.9022624727108e-077.8045249454216e-070.999999609773753
412.51238558385337e-075.02477116770674e-070.999999748761442
421.04002302771591e-072.08004605543181e-070.999999895997697
435.9695121246782e-081.19390242493564e-070.999999940304879
443.58909148641945e-087.1781829728389e-080.999999964109085
451.47867839119145e-082.95735678238291e-080.999999985213216
465.608676522942e-091.1217353045884e-080.999999994391323
473.12584522371815e-096.2516904474363e-090.999999996874155
481.77995168098924e-093.55990336197848e-090.999999998220048
496.91450467674064e-101.38290093534813e-090.99999999930855
505.30693739602393e-101.06138747920479e-090.999999999469306
514.95843526817186e-109.91687053634373e-100.999999999504156
522.10804087657222e-104.21608175314444e-100.999999999789196
537.87832038312907e-101.57566407662581e-090.999999999212168
546.48539063629822e-091.29707812725964e-080.99999999351461
559.69230749034394e-091.93846149806879e-080.999999990307692
565.11438217917104e-050.0001022876435834210.999948856178208
570.0005321482319345840.001064296463869170.999467851768065
580.0003640229124301950.000728045824860390.99963597708757
590.004962578447781680.009925156895563350.995037421552218
600.003733242906124220.007466485812248440.996266757093876
610.004332316279090960.008664632558181920.99566768372091
620.01208152998582360.02416305997164730.987918470014176
630.01866453804803450.0373290760960690.981335461951966
640.01518884759583700.03037769519167400.984811152404163
650.03475957461267150.0695191492253430.965240425387328
660.6446274329175450.7107451341649090.355372567082455
670.8279599759492070.3440800481015850.172040024050793
680.995498806324550.009002387350901590.00450119367545079
690.9999995079695139.84060974674766e-074.92030487337383e-07
7011.75266381181848e-158.76331905909242e-16
7112.41501192897529e-191.20750596448765e-19
7211.31156119108330e-226.55780595541648e-23
7314.26047181738420e-262.13023590869210e-26
7411.16521809713750e-255.82609048568748e-26
7518.9833119088852e-264.4916559544426e-26
7611.19475099814726e-255.97375499073629e-26
7712.20036477666735e-271.10018238833368e-27
7815.21148960204935e-352.60574480102467e-35
7915.01729086429437e-362.50864543214719e-36
8013.32773286230618e-361.66386643115309e-36
8112.60227300593048e-371.30113650296524e-37
8212.61395175304350e-371.30697587652175e-37
8313.63126769915888e-411.81563384957944e-41
8412.84601866934973e-451.42300933467487e-45
8512.49046336878838e-781.24523168439419e-78
8612.19569407871294e-781.09784703935647e-78
8711.72976671580449e-838.64883357902245e-84
8811.41434594042334e-827.0717297021167e-83
8911.51755672465563e-817.58778362327814e-82
9011.69986073336014e-808.4993036668007e-81
9112.15905800105628e-791.07952900052814e-79
9211.25468568967617e-786.27342844838083e-79
9311.73454075378975e-778.67270376894877e-78
9412.10406774039786e-761.05203387019893e-76
9511.61649458817144e-758.08247294085722e-76
9611.97646728004634e-749.88233640023172e-75
9712.36542352007095e-731.18271176003547e-73
9813.18223477861625e-721.59111738930813e-72
9911.79087920402007e-718.95439602010037e-72
10011.94125578219323e-709.70627891096615e-71
10113.83878111263210e-701.91939055631605e-70
10215.71341880132168e-702.85670940066084e-70
10316.86751301840809e-693.43375650920405e-69
10418.67527452672934e-684.33763726336467e-68
10511.10627778300260e-665.53138891501298e-67
10611.37616289168856e-656.88081445844278e-66
10718.93188057097164e-654.46594028548582e-65
10814.99181380072592e-642.49590690036296e-64
10911.48075370128354e-637.4037685064177e-64
11014.50330024823391e-632.25165012411695e-63
11115.12525248701847e-622.56262624350924e-62
11217.44598605241934e-623.72299302620967e-62
11318.17720742779436e-614.08860371389718e-61
11413.35826688998422e-601.67913344499211e-60
11513.56328773196152e-591.78164386598076e-59
11613.43126549698685e-581.71563274849342e-58
11713.6039378857279e-571.80196894286395e-57
11813.83105609837453e-561.91552804918726e-56
11913.80001257458069e-551.90000628729035e-55
12014.34957321334738e-542.17478660667369e-54
12114.37287574767189e-532.18643787383594e-53
12213.84728910153595e-521.92364455076797e-52
12314.11387141142108e-512.05693570571054e-51
12414.26033009014125e-502.13016504507062e-50
12513.76094943254631e-491.88047471627315e-49
12611.25928982296381e-486.29644911481904e-49
12713.6213483221012e-481.8106741610506e-48
12813.04148487575758e-471.52074243787879e-47
12912.53149867144638e-461.26574933572319e-46
13012.35081473931609e-451.17540736965804e-45
13113.69215345068546e-451.84607672534273e-45
13214.11922165779359e-442.05961082889679e-44
13314.36378457523325e-432.18189228761663e-43
13411.50277493017172e-427.51387465085858e-43
13513.47862137457045e-421.73931068728522e-42
13618.64212623953196e-424.32106311976598e-42
13718.67672746054375e-414.33836373027188e-41
13818.85315146480257e-404.42657573240128e-40
13918.59778997447555e-394.29889498723778e-39
14018.21588729971402e-384.10794364985701e-38
14118.15168636954534e-374.07584318477267e-37
14211.67428932473843e-368.37144662369216e-37
14312.09138467186446e-361.04569233593223e-36
14412.23460731429781e-351.11730365714890e-35
14512.12190401115084e-341.06095200557542e-34
14612.36483068396902e-331.18241534198451e-33
14712.10185601813747e-321.05092800906873e-32
14812.03719627982217e-311.01859813991109e-31
14914.50919079680286e-312.25459539840143e-31
15014.84096202912957e-302.42048101456478e-30
15113.80080197658487e-291.90040098829244e-29
15213.63353321065016e-281.81676660532508e-28
15313.78864658892413e-271.89432329446207e-27
15412.26706698664794e-261.13353349332397e-26
15512.62724353429181e-251.31362176714590e-25
15612.40116182938439e-241.20058091469220e-24
15711.96452098714578e-239.8226049357289e-24
15811.72628350504972e-228.6314175252486e-23
15911.42553112085325e-217.12765560426626e-22
16011.56696689193833e-207.83483445969164e-21
16111.73786362259832e-198.68931811299161e-20
16212.59062256866741e-191.29531128433371e-19
16312.90734916838291e-181.45367458419145e-18
16412.042321156875e-171.0211605784375e-17
16511.75300264226726e-168.7650132113363e-17
1660.9999999999999992.07332466429811e-151.03666233214905e-15
1670.999999999999992.13026496974066e-141.06513248487033e-14
1680.9999999999999461.08856315509954e-135.4428157754977e-14
1690.999999999999411.17858597814817e-125.89292989074085e-13
1700.9999999999951669.66847469413007e-124.83423734706504e-12
1710.9999999999527869.44283310520991e-114.72141655260496e-11
1720.9999999995391639.2167412350288e-104.6083706175144e-10
1730.9999999963180787.3638436430116e-093.6819218215058e-09
1740.999999967263786.54724395095859e-083.27362197547929e-08
1750.9999996928741936.14251614105641e-073.07125807052820e-07
1760.9999980001720543.99965589189032e-061.99982794594516e-06
1770.9999882329150362.35341699279646e-051.17670849639823e-05
1780.9998983347074260.0002033305851476950.000101665292573848
1790.9991721478236190.001655704352762190.000827852176381093
1800.9941237359700310.01175252805993710.00587626402996856






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1470.885542168674699NOK
5% type I error level1540.927710843373494NOK
10% type I error level1560.939759036144578NOK

\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 & 147 & 0.885542168674699 & NOK \tabularnewline
5% type I error level & 154 & 0.927710843373494 & NOK \tabularnewline
10% type I error level & 156 & 0.939759036144578 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116147&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]147[/C][C]0.885542168674699[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]154[/C][C]0.927710843373494[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]156[/C][C]0.939759036144578[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116147&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116147&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1470.885542168674699NOK
5% type I error level1540.927710843373494NOK
10% type I error level1560.939759036144578NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 12 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}