FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationTue, 28 Dec 2010 11:49:03 +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/28/t1293536824h73go4izwmhtsks.htm/, Retrieved Sun, 05 May 2024 01:50:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116296, Retrieved Sun, 05 May 2024 01:50:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

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] [] [2010-12-28 11:49:03] [82d760768aff8bf374d9817688c406af] [Current]
Feedback Forum

Post a new message

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
1	25	25	3	3	41	41	41	41	22	22	20
1	55	55	5	5	28	28	39	39	41	41	28
1	21	21	3	3	37	37	45	45	25	25	30
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	
1	47	47	5	5	27	27	45	45	35	35	30
1	52	52	1	1	29	29	40	40	32	32	17
1	27	27	2	2	42	42	40	40	24	24	26
1	27	27	4	4	47	47	44	44	38	38	27
1	25	25	4	4	17	17	44	44	36	36	33
1	28	28	4	4	34	34	48	48	24	24	47
1	25	25	3	3	32	32	51	51	18	18	37
1	52	52	4	4	25	25	49	49	34	34	34
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
1	52	52	2	2	46	46	49	49	33	33	22
2	25	0	3	0	35	0	36	0	28	0	
2	44	0	3	0	15	0	53	0	32	0	38
2	43	0	4	0	30	0	45	0	29	0	30
2	47	0	2	0	27	0	47	0	27	0	30
2	41	0	3	0	33	0	42	0	28	0	26
2	47	0	5	0	30	0	40	0	28	0	31
2	40	0	3	0	25	0	45	0	30	0	27
2	46	0	3	0	23	0	40	0	25	0	25
2	49	0	4	0	35	0	47	0	31	0	27
2	25	0	4	0	39	0	31	0	37	0	40
2	41	0	4	0	23	0	46	0	37	0	34
2	26	0	3	0	32	0	34	0	34	0	32
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
2	30	0	3	0	33	0	48	0	33	0	33
2	47	0	2	0	40	0	36	0	31	0	18
2	48	0	1	0	35	0	35	0	22	0	26
2	48	0	3	0	35	0	49	0	29	0	26
2	26	0	4	0	32	0	38	0	24	0	32
2		0	3	0	35	0	36	0	32	0	29
2	50	0	3	0	35	0	47	0	23	0	35
2	47	0	2	0	40	0	53	0	20	0	30
2	45	0	2	0	35	0	39	0	26	0	24
2	41	0	4	0	20	0	55	0	36	0	34
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
2	47	0	4	0	36	0	53	0	36	0	35
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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \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=116296&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/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=116296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
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] = -24.8966504105951 + 0.456806170205309geslacht[t] + 0.0318319415995288leeftijd[t] + 0.318881581238336leeftijd_man[t] + 0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] + 0.567135811953931Neuroticisme[t] + 0.09664060341303Neuroticisme_man[t] + 0.114508410025915Extraversie[t] + 0.267732438537323Extraversie_man[t] -0.0326812144046286Openheid[t] + 0.305178309477321Openheid_man[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intrinsieke_waarden[t] =  -24.8966504105951 +  0.456806170205309geslacht[t] +  0.0318319415995288leeftijd[t] +  0.318881581238336leeftijd_man[t] +  0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] +  0.567135811953931Neuroticisme[t] +  0.09664060341303Neuroticisme_man[t] +  0.114508410025915Extraversie[t] +  0.267732438537323Extraversie_man[t] -0.0326812144046286Openheid[t] +  0.305178309477321Openheid_man[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116296&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intrinsieke_waarden[t] =  -24.8966504105951 +  0.456806170205309geslacht[t] +  0.0318319415995288leeftijd[t] +  0.318881581238336leeftijd_man[t] +  0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] +  0.567135811953931Neuroticisme[t] +  0.09664060341303Neuroticisme_man[t] +  0.114508410025915Extraversie[t] +  0.267732438537323Extraversie_man[t] -0.0326812144046286Openheid[t] +  0.305178309477321Openheid_man[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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] = -24.8966504105951 + 0.456806170205309geslacht[t] + 0.0318319415995288leeftijd[t] + 0.318881581238336leeftijd_man[t] + 0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] + 0.567135811953931Neuroticisme[t] + 0.09664060341303Neuroticisme_man[t] + 0.114508410025915Extraversie[t] + 0.267732438537323Extraversie_man[t] -0.0326812144046286Openheid[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)-24.89665041059514.424988-5.626400
geslacht0.4568061702053090.0663426.885600
leeftijd0.03183194159952880.0796830.39950.6900040.345002
leeftijd_man0.3188815812383360.0923323.45370.0006870.000344
opleiding0.1831195755878830.0789292.32010.0214410.010721
opleiding_man-0.1353537350166680.0883-1.53290.127030.063515
Neuroticisme0.5671358119539310.0726477.806700
Neuroticisme_man0.096640603413030.0936351.03210.3033870.151694
Extraversie0.1145084100259150.0875611.30780.1925940.096297
Extraversie_man0.2677324385373230.1009382.65250.0086930.004346
Openheid-0.03268121440462860.073388-0.44530.6566130.328307
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) & -24.8966504105951 & 4.424988 & -5.6264 & 0 & 0 \tabularnewline
geslacht & 0.456806170205309 & 0.066342 & 6.8856 & 0 & 0 \tabularnewline
leeftijd & 0.0318319415995288 & 0.079683 & 0.3995 & 0.690004 & 0.345002 \tabularnewline
leeftijd_man & 0.318881581238336 & 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 & 0.09664060341303 & 0.093635 & 1.0321 & 0.303387 & 0.151694 \tabularnewline
Extraversie & 0.114508410025915 & 0.087561 & 1.3078 & 0.192594 & 0.096297 \tabularnewline
Extraversie_man & 0.267732438537323 & 0.100938 & 2.6525 & 0.008693 & 0.004346 \tabularnewline
Openheid & -0.0326812144046286 & 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=116296&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]-24.8966504105951[/C][C]4.424988[/C][C]-5.6264[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]0.456806170205309[/C][C]0.066342[/C][C]6.8856[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]0.0318319415995288[/C][C]0.079683[/C][C]0.3995[/C][C]0.690004[/C][C]0.345002[/C][/ROW]
[ROW][C]leeftijd_man[/C][C]0.318881581238336[/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]0.09664060341303[/C][C]0.093635[/C][C]1.0321[/C][C]0.303387[/C][C]0.151694[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.114508410025915[/C][C]0.087561[/C][C]1.3078[/C][C]0.192594[/C][C]0.096297[/C][/ROW]
[ROW][C]Extraversie_man[/C][C]0.267732438537323[/C][C]0.100938[/C][C]2.6525[/C][C]0.008693[/C][C]0.004346[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.0326812144046286[/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=116296&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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)-24.89665041059514.424988-5.626400
geslacht0.4568061702053090.0663426.885600
leeftijd0.03183194159952880.0796830.39950.6900040.345002
leeftijd_man0.3188815812383360.0923323.45370.0006870.000344
opleiding0.1831195755878830.0789292.32010.0214410.010721
opleiding_man-0.1353537350166680.0883-1.53290.127030.063515
Neuroticisme0.5671358119539310.0726477.806700
Neuroticisme_man0.096640603413030.0936351.03210.3033870.151694
Extraversie0.1145084100259150.0875611.30780.1925940.096297
Extraversie_man0.2677324385373230.1009382.65250.0086930.004346
Openheid-0.03268121440462860.073388-0.44530.6566130.328307
Openheid_man0.3051783094773210.0680794.48271.3e-056e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.890589690556144
R-squared0.793149996924889
Adjusted R-squared0.780716390182669
F-TEST (value)63.7908221941469
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.62015225140121
Sum Squared Residuals13598.1855452327

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.890589690556144 \tabularnewline
R-squared & 0.793149996924889 \tabularnewline
Adjusted R-squared & 0.780716390182669 \tabularnewline
F-TEST (value) & 63.7908221941469 \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 & 8.62015225140121 \tabularnewline
Sum Squared Residuals & 13598.1855452327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116296&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]0.793149996924889[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.780716390182669[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]63.7908221941469[/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]8.62015225140121[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13598.1855452327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116296&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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-squared0.793149996924889
Adjusted R-squared0.780716390182669
F-TEST (value)63.7908221941469
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.62015225140121
Sum Squared Residuals13598.1855452327






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14030.79363678577269.20636321422736
24531.437055746049513.5629442539505
33819.986724312439918.0132756875601
42833.0305146423446-5.03051464234461
53938.32933584014950.670664159850525
63739.8904355737311-2.89043557373109
73032.2569408009133-2.25694080091332
82935.4398524337144-6.43985243371439
93922.923190786071516.0768092139285
103537.4672868517959-2.46728685179594
113437.7404406805468-3.74044068054683
123832.4162186766885.58378132331201
132130.6986383675011-9.69863836750114
143529.43288834637665.56711165362338
155214.327021228860637.6729787711394
164657.4710793322765-11.4710793322765
175847.412372702860510.5876272971395
185444.44168406212149.55831593787863
192933.7032742142993-4.70327421429932
204336.63056801714236.36943198285766
214543.91688375562521.08311624437481
224642.8563784960543.14362150394598
232531.5893165154313-6.58931651543126
244739.67614988549017.32385011450992
254140.45377358991710.54622641008292
262934.6584037764682-5.65840377646823
274537.69949157270067.30050842729936
285445.67937306235018.32062693764989
292835.2559259776018-7.25592597760184
303738.8295165851031-1.82951658510309
315643.182630700665812.8173692993342
324327.474275327900815.5257246720992
333431.51328847774412.48671152225594
344241.11039361945610.88960638054387
354638.67255886643017.32744113356994
362529.1650247256869-4.16502472568693
372533.9488329992366-8.94883299923661
382524.15637148958380.843628510416237
394840.43914810072247.56085189927763
402730.5982147565159-3.59821475651589
412838.7505556476681-10.7505556476681
422537.5476513788366-12.5476513788366
432622.65397147409473.3460285259053
445140.862165922387510.1378340776125
452933.7391161628344-4.73911616283443
462928.30105455982320.698945440176777
474335.95444581628697.0455541837131
484437.6891127885636.31088721143696
492535.5111472850445-10.5111472850445
505143.73992711423427.26007288576583
514235.42381403683666.57618596316339
522533.8690116109405-8.86901161094054
535142.40299017608318.59700982391693
544640.96298884545485.03701115454525
552938.3042793950757-9.30427939507572
56331.2085071518208-28.2085071518208
572739.4058303962283-12.4058303962283
582020.5458661261612-0.545866126161166
594027.616091670008312.3839083299917
603327.36254984565815.63745015434195
612421.36031546231012.63968453768987
624128.468936350731212.5310636492688
632825.37504326037112.62495673962891
643727.95039501643269.04960498356739
654626.931598406455619.0684015935444
663940.5743895673292-1.57438956732923
672535.1160904953926-10.1160904953926
68121.2299621625814-20.2299621625814
69126.8200584071965-25.8200584071965
70132.3717235451891-31.3717235451891
714729.603699199278717.3963008007213
725242.18959516723169.81040483276836
732739.1255547727848-12.1255547727848
742725.32501224976821.67498775023181
752534.2390648252485-9.23906482524855
762830.9319082841472-2.93190828414722
772530.725024561518-5.725024561518
785228.933057145689523.0669428543105
794446.169520769564-2.16952076956397
804227.761629730205414.2383702697946
814538.76659469518056.23340530481945
824539.31967438197925.68032561802076
835035.287542117864114.7124578821359
844940.03738145063928.96261854936076
855243.1811388575628.81886114243805
862546.7862884096534-21.7862884096534
87018.5936384590394-18.5936384590394
8800.406880715063275-0.406880715063275
8905.51867240334857-5.51867240334857
9001.35335678549963-1.35335678549963
9105.25700182434596-5.25700182434596
9204.68709091742027-4.68709091742027
9302.45572330527533-2.45572330527533
9402.39159585217416-2.39159585217416
950-0.4814660777800410.481466077780041
96011.9029382805405-11.9029382805405
970-1.413542317906651.41354231790665
9805.15540528016988-5.15540528016988
9906.01981333863269-6.01981333863269
1000-4.343533518557154.34353351855715
10105.94237160123038-5.94237160123038
10200.149371857934798-0.149371857934798
10306.80220950107236-6.80220950107236
10406.29772974432684-6.29772974432684
10505.65396348052783-5.65396348052783
1060-1.364815053904741.36481505390474
1073-7.1872069744826510.1872069744827
10833.1802662966728-0.180266296672801
10920.8754534239235081.12454657607649
1102-0.07525647173365882.07525647173366
11140.04839903122494433.95160096877506
11259.1865504788181-4.18655047881809
11330.5680210356863822.43197896431362
11454.300110989983790.699889010016215
11533.47249956504615-0.472499565046153
11646.23064736195614-2.23064736195614
1173-3.113228363570316.11322836357031
11835.53571664827052-2.53571664827052
11933.51608485755938-0.516084857559375
12044.5660333031011-0.566033303101097
12143.297235260317640.702764739682362
12245.53394020983908-1.53394020983908
12334.66957944265322-1.66957944265322
12433.14217475433211-0.14217475433211
12534.87884507651419-1.87884507651419
12651.688901038896283.31109896110372
12755.11168251447321-0.111682514473212
12848.40772233288463-4.40772233288463
12949.83630537611625-5.83630537611625
13049.37159867186852-5.37159867186852
13156.98526958801426-1.98526958801426
13233.19554620666392-0.195546206663925
13332.708176607705590.29182339229441
13428.32172030936433-6.32172030936433
13550.2896447005184764.71035529948152
13625.42581596480556-3.42581596480556
13735.99830328195858-2.99830328195858
13845.52652709164253-1.52652709164253
13941.368103622326232.63189637767377
14043.91707557774060.0829244222593952
14138.99189491077936-5.99189491077936
14257.08647627881006-2.08647627881006
14326.16977155484524-4.16977155484524
14433.17941319474193-0.179413194741927
1453-3.702157287690686.70215728769068
14646.37798244354825-2.37798244354825
14745.7640128495289-1.7640128495289
14843.483762373581090.516237626418909
14920.03947221205769911.9605277879423
15031.419272094259421.58072790574058
1513-1.200508464266184.20050846426618
15231.309473093832161.69052690616784
15336.09248968568503-3.09248968568503
15430.9515661649327122.04843383506729
15532.511530017542070.48846998245793
15643.038923870626410.961076129373592
15746.56092523604318-2.56092523604318
15844.58334999282383-0.583349992823831
15945.1397210743365-1.1397210743365
16038.62129144865626-5.62129144865626
16130.7320053455587612.26799465444124
16252.952228477917752.04777152208225
16338.24336821575895-5.24336821575895
16435.17565630770856-2.17565630770856
16535.00762979573496-2.00762979573496
16641.853360402988432.14663959701157
1674-7.023191517248711.0231915172487
16834.00677340771636-1.00677340771636
16934.12475678834978-1.12475678834978
17045.02088313868326-1.02088313868326
17143.388961110425170.611038889574834
17244.34108699580148-0.341086995801476
17330.896835969993162.10316403000684
17432.941225874712220.0587741252877774
1753-0.9231309488253643.92313094882536
17631.951041226015551.04895877398445
1775-2.081350801413997.08135080141399
17833.24164880550645-0.241648805506448
17945.86718464795906-1.86718464795906
1804-2.153905278805476.15390527880547
18126.42419318666336-4.42419318666336
18231.15478836862341.8452116313766
18334.29512973614624-1.29512973614624
18449.9061909167957-5.90619091679569
18541.76366364749272.2363363525073
18632.084188693992210.915811306007786
18738.44244547326483-5.44244547326483
18843.618932710883210.381067289116789
18943.060108493492260.93989150650774
1903-1.393571926115494.39357192611549
19132.914861644130920.0851383558690758
1924-3.130495466560447.13049546656044
19355.50321367004779-0.503213670047788
194515.737969551465-10.737969551465
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 & 30.7936367857726 & 9.20636321422736 \tabularnewline
2 & 45 & 31.4370557460495 & 13.5629442539505 \tabularnewline
3 & 38 & 19.9867243124399 & 18.0132756875601 \tabularnewline
4 & 28 & 33.0305146423446 & -5.03051464234461 \tabularnewline
5 & 39 & 38.3293358401495 & 0.670664159850525 \tabularnewline
6 & 37 & 39.8904355737311 & -2.89043557373109 \tabularnewline
7 & 30 & 32.2569408009133 & -2.25694080091332 \tabularnewline
8 & 29 & 35.4398524337144 & -6.43985243371439 \tabularnewline
9 & 39 & 22.9231907860715 & 16.0768092139285 \tabularnewline
10 & 35 & 37.4672868517959 & -2.46728685179594 \tabularnewline
11 & 34 & 37.7404406805468 & -3.74044068054683 \tabularnewline
12 & 38 & 32.416218676688 & 5.58378132331201 \tabularnewline
13 & 21 & 30.6986383675011 & -9.69863836750114 \tabularnewline
14 & 35 & 29.4328883463766 & 5.56711165362338 \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 & 9.55831593787863 \tabularnewline
19 & 29 & 33.7032742142993 & -4.70327421429932 \tabularnewline
20 & 43 & 36.6305680171423 & 6.36943198285766 \tabularnewline
21 & 45 & 43.9168837556252 & 1.08311624437481 \tabularnewline
22 & 46 & 42.856378496054 & 3.14362150394598 \tabularnewline
23 & 25 & 31.5893165154313 & -6.58931651543126 \tabularnewline
24 & 47 & 39.6761498854901 & 7.32385011450992 \tabularnewline
25 & 41 & 40.4537735899171 & 0.54622641008292 \tabularnewline
26 & 29 & 34.6584037764682 & -5.65840377646823 \tabularnewline
27 & 45 & 37.6994915727006 & 7.30050842729936 \tabularnewline
28 & 54 & 45.6793730623501 & 8.32062693764989 \tabularnewline
29 & 28 & 35.2559259776018 & -7.25592597760184 \tabularnewline
30 & 37 & 38.8295165851031 & -1.82951658510309 \tabularnewline
31 & 56 & 43.1826307006658 & 12.8173692993342 \tabularnewline
32 & 43 & 27.4742753279008 & 15.5257246720992 \tabularnewline
33 & 34 & 31.5132884777441 & 2.48671152225594 \tabularnewline
34 & 42 & 41.1103936194561 & 0.88960638054387 \tabularnewline
35 & 46 & 38.6725588664301 & 7.32744113356994 \tabularnewline
36 & 25 & 29.1650247256869 & -4.16502472568693 \tabularnewline
37 & 25 & 33.9488329992366 & -8.94883299923661 \tabularnewline
38 & 25 & 24.1563714895838 & 0.843628510416237 \tabularnewline
39 & 48 & 40.4391481007224 & 7.56085189927763 \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 & 3.3460285259053 \tabularnewline
44 & 51 & 40.8621659223875 & 10.1378340776125 \tabularnewline
45 & 29 & 33.7391161628344 & -4.73911616283443 \tabularnewline
46 & 29 & 28.3010545598232 & 0.698945440176777 \tabularnewline
47 & 43 & 35.9544458162869 & 7.0455541837131 \tabularnewline
48 & 44 & 37.689112788563 & 6.31088721143696 \tabularnewline
49 & 25 & 35.5111472850445 & -10.5111472850445 \tabularnewline
50 & 51 & 43.7399271142342 & 7.26007288576583 \tabularnewline
51 & 42 & 35.4238140368366 & 6.57618596316339 \tabularnewline
52 & 25 & 33.8690116109405 & -8.86901161094054 \tabularnewline
53 & 51 & 42.4029901760831 & 8.59700982391693 \tabularnewline
54 & 46 & 40.9629888454548 & 5.03701115454525 \tabularnewline
55 & 29 & 38.3042793950757 & -9.30427939507572 \tabularnewline
56 & 3 & 31.2085071518208 & -28.2085071518208 \tabularnewline
57 & 27 & 39.4058303962283 & -12.4058303962283 \tabularnewline
58 & 20 & 20.5458661261612 & -0.545866126161166 \tabularnewline
59 & 40 & 27.6160916700083 & 12.3839083299917 \tabularnewline
60 & 33 & 27.3625498456581 & 5.63745015434195 \tabularnewline
61 & 24 & 21.3603154623101 & 2.63968453768987 \tabularnewline
62 & 41 & 28.4689363507312 & 12.5310636492688 \tabularnewline
63 & 28 & 25.3750432603711 & 2.62495673962891 \tabularnewline
64 & 37 & 27.9503950164326 & 9.04960498356739 \tabularnewline
65 & 46 & 26.9315984064556 & 19.0684015935444 \tabularnewline
66 & 39 & 40.5743895673292 & -1.57438956732923 \tabularnewline
67 & 25 & 35.1160904953926 & -10.1160904953926 \tabularnewline
68 & 1 & 21.2299621625814 & -20.2299621625814 \tabularnewline
69 & 1 & 26.8200584071965 & -25.8200584071965 \tabularnewline
70 & 1 & 32.3717235451891 & -31.3717235451891 \tabularnewline
71 & 47 & 29.6036991992787 & 17.3963008007213 \tabularnewline
72 & 52 & 42.1895951672316 & 9.81040483276836 \tabularnewline
73 & 27 & 39.1255547727848 & -12.1255547727848 \tabularnewline
74 & 27 & 25.3250122497682 & 1.67498775023181 \tabularnewline
75 & 25 & 34.2390648252485 & -9.23906482524855 \tabularnewline
76 & 28 & 30.9319082841472 & -2.93190828414722 \tabularnewline
77 & 25 & 30.725024561518 & -5.725024561518 \tabularnewline
78 & 52 & 28.9330571456895 & 23.0669428543105 \tabularnewline
79 & 44 & 46.169520769564 & -2.16952076956397 \tabularnewline
80 & 42 & 27.7616297302054 & 14.2383702697946 \tabularnewline
81 & 45 & 38.7665946951805 & 6.23340530481945 \tabularnewline
82 & 45 & 39.3196743819792 & 5.68032561802076 \tabularnewline
83 & 50 & 35.2875421178641 & 14.7124578821359 \tabularnewline
84 & 49 & 40.0373814506392 & 8.96261854936076 \tabularnewline
85 & 52 & 43.181138857562 & 8.81886114243805 \tabularnewline
86 & 25 & 46.7862884096534 & -21.7862884096534 \tabularnewline
87 & 0 & 18.5936384590394 & -18.5936384590394 \tabularnewline
88 & 0 & 0.406880715063275 & -0.406880715063275 \tabularnewline
89 & 0 & 5.51867240334857 & -5.51867240334857 \tabularnewline
90 & 0 & 1.35335678549963 & -1.35335678549963 \tabularnewline
91 & 0 & 5.25700182434596 & -5.25700182434596 \tabularnewline
92 & 0 & 4.68709091742027 & -4.68709091742027 \tabularnewline
93 & 0 & 2.45572330527533 & -2.45572330527533 \tabularnewline
94 & 0 & 2.39159585217416 & -2.39159585217416 \tabularnewline
95 & 0 & -0.481466077780041 & 0.481466077780041 \tabularnewline
96 & 0 & 11.9029382805405 & -11.9029382805405 \tabularnewline
97 & 0 & -1.41354231790665 & 1.41354231790665 \tabularnewline
98 & 0 & 5.15540528016988 & -5.15540528016988 \tabularnewline
99 & 0 & 6.01981333863269 & -6.01981333863269 \tabularnewline
100 & 0 & -4.34353351855715 & 4.34353351855715 \tabularnewline
101 & 0 & 5.94237160123038 & -5.94237160123038 \tabularnewline
102 & 0 & 0.149371857934798 & -0.149371857934798 \tabularnewline
103 & 0 & 6.80220950107236 & -6.80220950107236 \tabularnewline
104 & 0 & 6.29772974432684 & -6.29772974432684 \tabularnewline
105 & 0 & 5.65396348052783 & -5.65396348052783 \tabularnewline
106 & 0 & -1.36481505390474 & 1.36481505390474 \tabularnewline
107 & 3 & -7.18720697448265 & 10.1872069744827 \tabularnewline
108 & 3 & 3.1802662966728 & -0.180266296672801 \tabularnewline
109 & 2 & 0.875453423923508 & 1.12454657607649 \tabularnewline
110 & 2 & -0.0752564717336588 & 2.07525647173366 \tabularnewline
111 & 4 & 0.0483990312249443 & 3.95160096877506 \tabularnewline
112 & 5 & 9.1865504788181 & -4.18655047881809 \tabularnewline
113 & 3 & 0.568021035686382 & 2.43197896431362 \tabularnewline
114 & 5 & 4.30011098998379 & 0.699889010016215 \tabularnewline
115 & 3 & 3.47249956504615 & -0.472499565046153 \tabularnewline
116 & 4 & 6.23064736195614 & -2.23064736195614 \tabularnewline
117 & 3 & -3.11322836357031 & 6.11322836357031 \tabularnewline
118 & 3 & 5.53571664827052 & -2.53571664827052 \tabularnewline
119 & 3 & 3.51608485755938 & -0.516084857559375 \tabularnewline
120 & 4 & 4.5660333031011 & -0.566033303101097 \tabularnewline
121 & 4 & 3.29723526031764 & 0.702764739682362 \tabularnewline
122 & 4 & 5.53394020983908 & -1.53394020983908 \tabularnewline
123 & 3 & 4.66957944265322 & -1.66957944265322 \tabularnewline
124 & 3 & 3.14217475433211 & -0.14217475433211 \tabularnewline
125 & 3 & 4.87884507651419 & -1.87884507651419 \tabularnewline
126 & 5 & 1.68890103889628 & 3.31109896110372 \tabularnewline
127 & 5 & 5.11168251447321 & -0.111682514473212 \tabularnewline
128 & 4 & 8.40772233288463 & -4.40772233288463 \tabularnewline
129 & 4 & 9.83630537611625 & -5.83630537611625 \tabularnewline
130 & 4 & 9.37159867186852 & -5.37159867186852 \tabularnewline
131 & 5 & 6.98526958801426 & -1.98526958801426 \tabularnewline
132 & 3 & 3.19554620666392 & -0.195546206663925 \tabularnewline
133 & 3 & 2.70817660770559 & 0.29182339229441 \tabularnewline
134 & 2 & 8.32172030936433 & -6.32172030936433 \tabularnewline
135 & 5 & 0.289644700518476 & 4.71035529948152 \tabularnewline
136 & 2 & 5.42581596480556 & -3.42581596480556 \tabularnewline
137 & 3 & 5.99830328195858 & -2.99830328195858 \tabularnewline
138 & 4 & 5.52652709164253 & -1.52652709164253 \tabularnewline
139 & 4 & 1.36810362232623 & 2.63189637767377 \tabularnewline
140 & 4 & 3.9170755777406 & 0.0829244222593952 \tabularnewline
141 & 3 & 8.99189491077936 & -5.99189491077936 \tabularnewline
142 & 5 & 7.08647627881006 & -2.08647627881006 \tabularnewline
143 & 2 & 6.16977155484524 & -4.16977155484524 \tabularnewline
144 & 3 & 3.17941319474193 & -0.179413194741927 \tabularnewline
145 & 3 & -3.70215728769068 & 6.70215728769068 \tabularnewline
146 & 4 & 6.37798244354825 & -2.37798244354825 \tabularnewline
147 & 4 & 5.7640128495289 & -1.7640128495289 \tabularnewline
148 & 4 & 3.48376237358109 & 0.516237626418909 \tabularnewline
149 & 2 & 0.0394722120576991 & 1.9605277879423 \tabularnewline
150 & 3 & 1.41927209425942 & 1.58072790574058 \tabularnewline
151 & 3 & -1.20050846426618 & 4.20050846426618 \tabularnewline
152 & 3 & 1.30947309383216 & 1.69052690616784 \tabularnewline
153 & 3 & 6.09248968568503 & -3.09248968568503 \tabularnewline
154 & 3 & 0.951566164932712 & 2.04843383506729 \tabularnewline
155 & 3 & 2.51153001754207 & 0.48846998245793 \tabularnewline
156 & 4 & 3.03892387062641 & 0.961076129373592 \tabularnewline
157 & 4 & 6.56092523604318 & -2.56092523604318 \tabularnewline
158 & 4 & 4.58334999282383 & -0.583349992823831 \tabularnewline
159 & 4 & 5.1397210743365 & -1.1397210743365 \tabularnewline
160 & 3 & 8.62129144865626 & -5.62129144865626 \tabularnewline
161 & 3 & 0.732005345558761 & 2.26799465444124 \tabularnewline
162 & 5 & 2.95222847791775 & 2.04777152208225 \tabularnewline
163 & 3 & 8.24336821575895 & -5.24336821575895 \tabularnewline
164 & 3 & 5.17565630770856 & -2.17565630770856 \tabularnewline
165 & 3 & 5.00762979573496 & -2.00762979573496 \tabularnewline
166 & 4 & 1.85336040298843 & 2.14663959701157 \tabularnewline
167 & 4 & -7.0231915172487 & 11.0231915172487 \tabularnewline
168 & 3 & 4.00677340771636 & -1.00677340771636 \tabularnewline
169 & 3 & 4.12475678834978 & -1.12475678834978 \tabularnewline
170 & 4 & 5.02088313868326 & -1.02088313868326 \tabularnewline
171 & 4 & 3.38896111042517 & 0.611038889574834 \tabularnewline
172 & 4 & 4.34108699580148 & -0.341086995801476 \tabularnewline
173 & 3 & 0.89683596999316 & 2.10316403000684 \tabularnewline
174 & 3 & 2.94122587471222 & 0.0587741252877774 \tabularnewline
175 & 3 & -0.923130948825364 & 3.92313094882536 \tabularnewline
176 & 3 & 1.95104122601555 & 1.04895877398445 \tabularnewline
177 & 5 & -2.08135080141399 & 7.08135080141399 \tabularnewline
178 & 3 & 3.24164880550645 & -0.241648805506448 \tabularnewline
179 & 4 & 5.86718464795906 & -1.86718464795906 \tabularnewline
180 & 4 & -2.15390527880547 & 6.15390527880547 \tabularnewline
181 & 2 & 6.42419318666336 & -4.42419318666336 \tabularnewline
182 & 3 & 1.1547883686234 & 1.8452116313766 \tabularnewline
183 & 3 & 4.29512973614624 & -1.29512973614624 \tabularnewline
184 & 4 & 9.9061909167957 & -5.90619091679569 \tabularnewline
185 & 4 & 1.7636636474927 & 2.2363363525073 \tabularnewline
186 & 3 & 2.08418869399221 & 0.915811306007786 \tabularnewline
187 & 3 & 8.44244547326483 & -5.44244547326483 \tabularnewline
188 & 4 & 3.61893271088321 & 0.381067289116789 \tabularnewline
189 & 4 & 3.06010849349226 & 0.93989150650774 \tabularnewline
190 & 3 & -1.39357192611549 & 4.39357192611549 \tabularnewline
191 & 3 & 2.91486164413092 & 0.0851383558690758 \tabularnewline
192 & 4 & -3.13049546656044 & 7.13049546656044 \tabularnewline
193 & 5 & 5.50321367004779 & -0.503213670047788 \tabularnewline
194 & 5 & 15.737969551465 & -10.737969551465 \tabularnewline
195 & 4 & 26.73659702224 & -22.73659702224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116296&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]30.7936367857726[/C][C]9.20636321422736[/C][/ROW]
[ROW][C]2[/C][C]45[/C][C]31.4370557460495[/C][C]13.5629442539505[/C][/ROW]
[ROW][C]3[/C][C]38[/C][C]19.9867243124399[/C][C]18.0132756875601[/C][/ROW]
[ROW][C]4[/C][C]28[/C][C]33.0305146423446[/C][C]-5.03051464234461[/C][/ROW]
[ROW][C]5[/C][C]39[/C][C]38.3293358401495[/C][C]0.670664159850525[/C][/ROW]
[ROW][C]6[/C][C]37[/C][C]39.8904355737311[/C][C]-2.89043557373109[/C][/ROW]
[ROW][C]7[/C][C]30[/C][C]32.2569408009133[/C][C]-2.25694080091332[/C][/ROW]
[ROW][C]8[/C][C]29[/C][C]35.4398524337144[/C][C]-6.43985243371439[/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]37.4672868517959[/C][C]-2.46728685179594[/C][/ROW]
[ROW][C]11[/C][C]34[/C][C]37.7404406805468[/C][C]-3.74044068054683[/C][/ROW]
[ROW][C]12[/C][C]38[/C][C]32.416218676688[/C][C]5.58378132331201[/C][/ROW]
[ROW][C]13[/C][C]21[/C][C]30.6986383675011[/C][C]-9.69863836750114[/C][/ROW]
[ROW][C]14[/C][C]35[/C][C]29.4328883463766[/C][C]5.56711165362338[/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]9.55831593787863[/C][/ROW]
[ROW][C]19[/C][C]29[/C][C]33.7032742142993[/C][C]-4.70327421429932[/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]1.08311624437481[/C][/ROW]
[ROW][C]22[/C][C]46[/C][C]42.856378496054[/C][C]3.14362150394598[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]31.5893165154313[/C][C]-6.58931651543126[/C][/ROW]
[ROW][C]24[/C][C]47[/C][C]39.6761498854901[/C][C]7.32385011450992[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]40.4537735899171[/C][C]0.54622641008292[/C][/ROW]
[ROW][C]26[/C][C]29[/C][C]34.6584037764682[/C][C]-5.65840377646823[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]37.6994915727006[/C][C]7.30050842729936[/C][/ROW]
[ROW][C]28[/C][C]54[/C][C]45.6793730623501[/C][C]8.32062693764989[/C][/ROW]
[ROW][C]29[/C][C]28[/C][C]35.2559259776018[/C][C]-7.25592597760184[/C][/ROW]
[ROW][C]30[/C][C]37[/C][C]38.8295165851031[/C][C]-1.82951658510309[/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]27.4742753279008[/C][C]15.5257246720992[/C][/ROW]
[ROW][C]33[/C][C]34[/C][C]31.5132884777441[/C][C]2.48671152225594[/C][/ROW]
[ROW][C]34[/C][C]42[/C][C]41.1103936194561[/C][C]0.88960638054387[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]38.6725588664301[/C][C]7.32744113356994[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]29.1650247256869[/C][C]-4.16502472568693[/C][/ROW]
[ROW][C]37[/C][C]25[/C][C]33.9488329992366[/C][C]-8.94883299923661[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]24.1563714895838[/C][C]0.843628510416237[/C][/ROW]
[ROW][C]39[/C][C]48[/C][C]40.4391481007224[/C][C]7.56085189927763[/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]3.3460285259053[/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]-4.73911616283443[/C][/ROW]
[ROW][C]46[/C][C]29[/C][C]28.3010545598232[/C][C]0.698945440176777[/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]37.689112788563[/C][C]6.31088721143696[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]35.5111472850445[/C][C]-10.5111472850445[/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]6.57618596316339[/C][/ROW]
[ROW][C]52[/C][C]25[/C][C]33.8690116109405[/C][C]-8.86901161094054[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]42.4029901760831[/C][C]8.59700982391693[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]40.9629888454548[/C][C]5.03701115454525[/C][/ROW]
[ROW][C]55[/C][C]29[/C][C]38.3042793950757[/C][C]-9.30427939507572[/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]-0.545866126161166[/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]27.3625498456581[/C][C]5.63745015434195[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]21.3603154623101[/C][C]2.63968453768987[/C][/ROW]
[ROW][C]62[/C][C]41[/C][C]28.4689363507312[/C][C]12.5310636492688[/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]27.9503950164326[/C][C]9.04960498356739[/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]-1.57438956732923[/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]21.2299621625814[/C][C]-20.2299621625814[/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]32.3717235451891[/C][C]-31.3717235451891[/C][/ROW]
[ROW][C]71[/C][C]47[/C][C]29.6036991992787[/C][C]17.3963008007213[/C][/ROW]
[ROW][C]72[/C][C]52[/C][C]42.1895951672316[/C][C]9.81040483276836[/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]1.67498775023181[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]34.2390648252485[/C][C]-9.23906482524855[/C][/ROW]
[ROW][C]76[/C][C]28[/C][C]30.9319082841472[/C][C]-2.93190828414722[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]30.725024561518[/C][C]-5.725024561518[/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]-2.16952076956397[/C][/ROW]
[ROW][C]80[/C][C]42[/C][C]27.7616297302054[/C][C]14.2383702697946[/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]5.68032561802076[/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]8.96261854936076[/C][/ROW]
[ROW][C]85[/C][C]52[/C][C]43.181138857562[/C][C]8.81886114243805[/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]0.406880715063275[/C][C]-0.406880715063275[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]5.51867240334857[/C][C]-5.51867240334857[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]1.35335678549963[/C][C]-1.35335678549963[/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]4.68709091742027[/C][C]-4.68709091742027[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]2.45572330527533[/C][C]-2.45572330527533[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]2.39159585217416[/C][C]-2.39159585217416[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.481466077780041[/C][C]0.481466077780041[/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]-1.41354231790665[/C][C]1.41354231790665[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]5.15540528016988[/C][C]-5.15540528016988[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]6.01981333863269[/C][C]-6.01981333863269[/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]5.94237160123038[/C][C]-5.94237160123038[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.149371857934798[/C][C]-0.149371857934798[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]6.80220950107236[/C][C]-6.80220950107236[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]6.29772974432684[/C][C]-6.29772974432684[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]5.65396348052783[/C][C]-5.65396348052783[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]-1.36481505390474[/C][C]1.36481505390474[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]-7.18720697448265[/C][C]10.1872069744827[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]3.1802662966728[/C][C]-0.180266296672801[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]0.875453423923508[/C][C]1.12454657607649[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]-0.0752564717336588[/C][C]2.07525647173366[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]0.0483990312249443[/C][C]3.95160096877506[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]9.1865504788181[/C][C]-4.18655047881809[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]0.568021035686382[/C][C]2.43197896431362[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]4.30011098998379[/C][C]0.699889010016215[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]3.47249956504615[/C][C]-0.472499565046153[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]6.23064736195614[/C][C]-2.23064736195614[/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]5.53571664827052[/C][C]-2.53571664827052[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.51608485755938[/C][C]-0.516084857559375[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]4.5660333031011[/C][C]-0.566033303101097[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]3.29723526031764[/C][C]0.702764739682362[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]5.53394020983908[/C][C]-1.53394020983908[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]4.66957944265322[/C][C]-1.66957944265322[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]3.14217475433211[/C][C]-0.14217475433211[/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]1.68890103889628[/C][C]3.31109896110372[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]5.11168251447321[/C][C]-0.111682514473212[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]8.40772233288463[/C][C]-4.40772233288463[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]9.83630537611625[/C][C]-5.83630537611625[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]9.37159867186852[/C][C]-5.37159867186852[/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]3.19554620666392[/C][C]-0.195546206663925[/C][/ROW]
[ROW][C]133[/C][C]3[/C][C]2.70817660770559[/C][C]0.29182339229441[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]8.32172030936433[/C][C]-6.32172030936433[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]0.289644700518476[/C][C]4.71035529948152[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]5.42581596480556[/C][C]-3.42581596480556[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]5.99830328195858[/C][C]-2.99830328195858[/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]3.9170755777406[/C][C]0.0829244222593952[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]8.99189491077936[/C][C]-5.99189491077936[/C][/ROW]
[ROW][C]142[/C][C]5[/C][C]7.08647627881006[/C][C]-2.08647627881006[/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]3.17941319474193[/C][C]-0.179413194741927[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]-3.70215728769068[/C][C]6.70215728769068[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]6.37798244354825[/C][C]-2.37798244354825[/C][/ROW]
[ROW][C]147[/C][C]4[/C][C]5.7640128495289[/C][C]-1.7640128495289[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]3.48376237358109[/C][C]0.516237626418909[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]0.0394722120576991[/C][C]1.9605277879423[/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]-1.20050846426618[/C][C]4.20050846426618[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]1.30947309383216[/C][C]1.69052690616784[/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]0.951566164932712[/C][C]2.04843383506729[/C][/ROW]
[ROW][C]155[/C][C]3[/C][C]2.51153001754207[/C][C]0.48846998245793[/C][/ROW]
[ROW][C]156[/C][C]4[/C][C]3.03892387062641[/C][C]0.961076129373592[/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]4.58334999282383[/C][C]-0.583349992823831[/C][/ROW]
[ROW][C]159[/C][C]4[/C][C]5.1397210743365[/C][C]-1.1397210743365[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]8.62129144865626[/C][C]-5.62129144865626[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]0.732005345558761[/C][C]2.26799465444124[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]2.95222847791775[/C][C]2.04777152208225[/C][/ROW]
[ROW][C]163[/C][C]3[/C][C]8.24336821575895[/C][C]-5.24336821575895[/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]5.00762979573496[/C][C]-2.00762979573496[/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]4.00677340771636[/C][C]-1.00677340771636[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]4.12475678834978[/C][C]-1.12475678834978[/C][/ROW]
[ROW][C]170[/C][C]4[/C][C]5.02088313868326[/C][C]-1.02088313868326[/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]-0.341086995801476[/C][/ROW]
[ROW][C]173[/C][C]3[/C][C]0.89683596999316[/C][C]2.10316403000684[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]2.94122587471222[/C][C]0.0587741252877774[/C][/ROW]
[ROW][C]175[/C][C]3[/C][C]-0.923130948825364[/C][C]3.92313094882536[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]1.95104122601555[/C][C]1.04895877398445[/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]3.24164880550645[/C][C]-0.241648805506448[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]5.86718464795906[/C][C]-1.86718464795906[/C][/ROW]
[ROW][C]180[/C][C]4[/C][C]-2.15390527880547[/C][C]6.15390527880547[/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]1.1547883686234[/C][C]1.8452116313766[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]4.29512973614624[/C][C]-1.29512973614624[/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]1.7636636474927[/C][C]2.2363363525073[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]2.08418869399221[/C][C]0.915811306007786[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]8.44244547326483[/C][C]-5.44244547326483[/C][/ROW]
[ROW][C]188[/C][C]4[/C][C]3.61893271088321[/C][C]0.381067289116789[/C][/ROW]
[ROW][C]189[/C][C]4[/C][C]3.06010849349226[/C][C]0.93989150650774[/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]2.91486164413092[/C][C]0.0851383558690758[/C][/ROW]
[ROW][C]192[/C][C]4[/C][C]-3.13049546656044[/C][C]7.13049546656044[/C][/ROW]
[ROW][C]193[/C][C]5[/C][C]5.50321367004779[/C][C]-0.503213670047788[/C][/ROW]
[ROW][C]194[/C][C]5[/C][C]15.737969551465[/C][C]-10.737969551465[/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=116296&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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
14030.79363678577269.20636321422736
24531.437055746049513.5629442539505
33819.986724312439918.0132756875601
42833.0305146423446-5.03051464234461
53938.32933584014950.670664159850525
63739.8904355737311-2.89043557373109
73032.2569408009133-2.25694080091332
82935.4398524337144-6.43985243371439
93922.923190786071516.0768092139285
103537.4672868517959-2.46728685179594
113437.7404406805468-3.74044068054683
123832.4162186766885.58378132331201
132130.6986383675011-9.69863836750114
143529.43288834637665.56711165362338
155214.327021228860637.6729787711394
164657.4710793322765-11.4710793322765
175847.412372702860510.5876272971395
185444.44168406212149.55831593787863
192933.7032742142993-4.70327421429932
204336.63056801714236.36943198285766
214543.91688375562521.08311624437481
224642.8563784960543.14362150394598
232531.5893165154313-6.58931651543126
244739.67614988549017.32385011450992
254140.45377358991710.54622641008292
262934.6584037764682-5.65840377646823
274537.69949157270067.30050842729936
285445.67937306235018.32062693764989
292835.2559259776018-7.25592597760184
303738.8295165851031-1.82951658510309
315643.182630700665812.8173692993342
324327.474275327900815.5257246720992
333431.51328847774412.48671152225594
344241.11039361945610.88960638054387
354638.67255886643017.32744113356994
362529.1650247256869-4.16502472568693
372533.9488329992366-8.94883299923661
382524.15637148958380.843628510416237
394840.43914810072247.56085189927763
402730.5982147565159-3.59821475651589
412838.7505556476681-10.7505556476681
422537.5476513788366-12.5476513788366
432622.65397147409473.3460285259053
445140.862165922387510.1378340776125
452933.7391161628344-4.73911616283443
462928.30105455982320.698945440176777
474335.95444581628697.0455541837131
484437.6891127885636.31088721143696
492535.5111472850445-10.5111472850445
505143.73992711423427.26007288576583
514235.42381403683666.57618596316339
522533.8690116109405-8.86901161094054
535142.40299017608318.59700982391693
544640.96298884545485.03701115454525
552938.3042793950757-9.30427939507572
56331.2085071518208-28.2085071518208
572739.4058303962283-12.4058303962283
582020.5458661261612-0.545866126161166
594027.616091670008312.3839083299917
603327.36254984565815.63745015434195
612421.36031546231012.63968453768987
624128.468936350731212.5310636492688
632825.37504326037112.62495673962891
643727.95039501643269.04960498356739
654626.931598406455619.0684015935444
663940.5743895673292-1.57438956732923
672535.1160904953926-10.1160904953926
68121.2299621625814-20.2299621625814
69126.8200584071965-25.8200584071965
70132.3717235451891-31.3717235451891
714729.603699199278717.3963008007213
725242.18959516723169.81040483276836
732739.1255547727848-12.1255547727848
742725.32501224976821.67498775023181
752534.2390648252485-9.23906482524855
762830.9319082841472-2.93190828414722
772530.725024561518-5.725024561518
785228.933057145689523.0669428543105
794446.169520769564-2.16952076956397
804227.761629730205414.2383702697946
814538.76659469518056.23340530481945
824539.31967438197925.68032561802076
835035.287542117864114.7124578821359
844940.03738145063928.96261854936076
855243.1811388575628.81886114243805
862546.7862884096534-21.7862884096534
87018.5936384590394-18.5936384590394
8800.406880715063275-0.406880715063275
8905.51867240334857-5.51867240334857
9001.35335678549963-1.35335678549963
9105.25700182434596-5.25700182434596
9204.68709091742027-4.68709091742027
9302.45572330527533-2.45572330527533
9402.39159585217416-2.39159585217416
950-0.4814660777800410.481466077780041
96011.9029382805405-11.9029382805405
970-1.413542317906651.41354231790665
9805.15540528016988-5.15540528016988
9906.01981333863269-6.01981333863269
1000-4.343533518557154.34353351855715
10105.94237160123038-5.94237160123038
10200.149371857934798-0.149371857934798
10306.80220950107236-6.80220950107236
10406.29772974432684-6.29772974432684
10505.65396348052783-5.65396348052783
1060-1.364815053904741.36481505390474
1073-7.1872069744826510.1872069744827
10833.1802662966728-0.180266296672801
10920.8754534239235081.12454657607649
1102-0.07525647173365882.07525647173366
11140.04839903122494433.95160096877506
11259.1865504788181-4.18655047881809
11330.5680210356863822.43197896431362
11454.300110989983790.699889010016215
11533.47249956504615-0.472499565046153
11646.23064736195614-2.23064736195614
1173-3.113228363570316.11322836357031
11835.53571664827052-2.53571664827052
11933.51608485755938-0.516084857559375
12044.5660333031011-0.566033303101097
12143.297235260317640.702764739682362
12245.53394020983908-1.53394020983908
12334.66957944265322-1.66957944265322
12433.14217475433211-0.14217475433211
12534.87884507651419-1.87884507651419
12651.688901038896283.31109896110372
12755.11168251447321-0.111682514473212
12848.40772233288463-4.40772233288463
12949.83630537611625-5.83630537611625
13049.37159867186852-5.37159867186852
13156.98526958801426-1.98526958801426
13233.19554620666392-0.195546206663925
13332.708176607705590.29182339229441
13428.32172030936433-6.32172030936433
13550.2896447005184764.71035529948152
13625.42581596480556-3.42581596480556
13735.99830328195858-2.99830328195858
13845.52652709164253-1.52652709164253
13941.368103622326232.63189637767377
14043.91707557774060.0829244222593952
14138.99189491077936-5.99189491077936
14257.08647627881006-2.08647627881006
14326.16977155484524-4.16977155484524
14433.17941319474193-0.179413194741927
1453-3.702157287690686.70215728769068
14646.37798244354825-2.37798244354825
14745.7640128495289-1.7640128495289
14843.483762373581090.516237626418909
14920.03947221205769911.9605277879423
15031.419272094259421.58072790574058
1513-1.200508464266184.20050846426618
15231.309473093832161.69052690616784
15336.09248968568503-3.09248968568503
15430.9515661649327122.04843383506729
15532.511530017542070.48846998245793
15643.038923870626410.961076129373592
15746.56092523604318-2.56092523604318
15844.58334999282383-0.583349992823831
15945.1397210743365-1.1397210743365
16038.62129144865626-5.62129144865626
16130.7320053455587612.26799465444124
16252.952228477917752.04777152208225
16338.24336821575895-5.24336821575895
16435.17565630770856-2.17565630770856
16535.00762979573496-2.00762979573496
16641.853360402988432.14663959701157
1674-7.023191517248711.0231915172487
16834.00677340771636-1.00677340771636
16934.12475678834978-1.12475678834978
17045.02088313868326-1.02088313868326
17143.388961110425170.611038889574834
17244.34108699580148-0.341086995801476
17330.896835969993162.10316403000684
17432.941225874712220.0587741252877774
1753-0.9231309488253643.92313094882536
17631.951041226015551.04895877398445
1775-2.081350801413997.08135080141399
17833.24164880550645-0.241648805506448
17945.86718464795906-1.86718464795906
1804-2.153905278805476.15390527880547
18126.42419318666336-4.42419318666336
18231.15478836862341.8452116313766
18334.29512973614624-1.29512973614624
18449.9061909167957-5.90619091679569
18541.76366364749272.2363363525073
18632.084188693992210.915811306007786
18738.44244547326483-5.44244547326483
18843.618932710883210.381067289116789
18943.060108493492260.93989150650774
1903-1.393571926115494.39357192611549
19132.914861644130920.0851383558690758
1924-3.130495466560447.13049546656044
19355.50321367004779-0.503213670047788
194515.737969551465-10.737969551465
195426.73659702224-22.73659702224






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.7260228495784120.5479543008431750.273977150421588
160.5865021759789910.8269956480420180.413497824021009
170.4483585959271430.8967171918542850.551641404072857
180.3279286619883630.6558573239767260.672071338011637
190.2227837425665170.4455674851330340.777216257433483
200.1453570473314190.2907140946628380.854642952668581
210.08992492210249420.1798498442049880.910075077897506
220.05394651538434730.1078930307686950.946053484615653
230.0307197550569960.06143951011399210.969280244943004
240.01785721841822210.03571443683644420.982142781581778
250.00961659768418750.0192331953683750.990383402315812
260.005037551227187850.01007510245437570.994962448772812
270.002623013370360260.005246026740720530.99737698662964
280.001481285987564590.002962571975129170.998518714012435
290.0007019111249576440.001403822249915290.999298088875042
300.0003454334271684850.000690866854336970.999654566572831
310.00021789394282570.00043578788565140.999782106057174
320.0001111967669430280.0002223935338860550.999888803233057
335.38781460423764e-050.0001077562920847530.999946121853958
343.35881149093968e-056.71762298187937e-050.99996641188509
351.77708100972846e-053.55416201945692e-050.999982229189903
368.92005580104038e-061.78401116020808e-050.9999910799442
374.30002820651524e-068.60005641303049e-060.999995699971793
381.87643309057237e-063.75286618114475e-060.99999812356691
399.75207805957436e-071.95041561191487e-060.999999024792194
403.90226247271059e-077.80452494542119e-070.999999609773753
412.51238558385365e-075.02477116770731e-070.999999748761442
421.04002302771598e-072.08004605543197e-070.999999895997697
435.96951212467888e-081.19390242493578e-070.999999940304879
443.58909148641954e-087.17818297283908e-080.999999964109085
451.478678391191e-082.957356782382e-080.999999985213216
465.60867652294127e-091.12173530458825e-080.999999994391323
473.1258452237175e-096.251690447435e-090.999999996874155
481.77995168098964e-093.55990336197928e-090.999999998220048
496.9145046767396e-101.38290093534792e-090.99999999930855
505.30693739602464e-101.06138747920493e-090.999999999469306
514.95843526817165e-109.9168705363433e-100.999999999504157
522.10804087657194e-104.21608175314388e-100.999999999789196
537.87832038312808e-101.57566407662562e-090.999999999212168
546.4853906362962e-091.29707812725924e-080.99999999351461
559.69230749034174e-091.93846149806835e-080.999999990307692
565.1143821791711e-050.0001022876435834220.999948856178208
570.0005321482319345750.001064296463869150.999467851768065
580.0003640229124301790.0007280458248603580.99963597708757
590.004962578447781210.009925156895562420.995037421552219
600.003733242906124210.007466485812248430.996266757093876
610.004332316279090760.008664632558181520.99566768372091
620.01208152998582360.02416305997164720.987918470014176
630.01866453804803430.03732907609606870.981335461951966
640.0151888475958370.03037769519167390.984811152404163
650.03475957461267490.06951914922534980.965240425387325
660.6446274329175420.7107451341649160.355372567082458
670.8279599759491980.3440800481016030.172040024050802
680.995498806324550.009002387350901750.00450119367545087
690.9999995079695139.8406097467456e-074.9203048733728e-07
7011.75266381181876e-158.7633190590938e-16
7112.41501192897529e-191.20750596448765e-19
7211.3115611910831e-226.55780595541548e-23
7314.26047181738403e-262.13023590869201e-26
7411.16521809713759e-255.82609048568796e-26
7518.98331190888704e-264.49165595444352e-26
7611.19475099814744e-255.97375499073722e-26
7712.20036477666766e-271.10018238833383e-27
7815.21148960204894e-352.60574480102447e-35
7915.01729086429533e-362.50864543214767e-36
8013.32773286230554e-361.66386643115277e-36
8112.60227300593062e-371.30113650296531e-37
8212.61395175304279e-371.3069758765214e-37
8313.63126769916008e-411.81563384958004e-41
8412.846018669348e-451.423009334674e-45
8512.49046336878845e-781.24523168439423e-78
8612.19569407871288e-781.09784703935644e-78
8711.72976671580459e-838.64883357902294e-84
8811.41434594042342e-827.07172970211711e-83
8911.51755672465563e-817.58778362327814e-82
9011.69986073335995e-808.49930366679975e-81
9112.15905800105628e-791.07952900052814e-79
9211.25468568967617e-786.27342844838083e-79
9311.73454075378966e-778.67270376894828e-78
9412.10406774039786e-761.05203387019893e-76
9511.6164945881714e-758.082472940857e-76
9611.97646728004629e-749.88233640023144e-75
9712.36542352007081e-731.18271176003541e-73
9813.18223477861644e-721.59111738930822e-72
9911.79087920402013e-718.95439602010063e-72
10011.94125578219306e-709.70627891096532e-71
10113.83878111263253e-701.91939055631627e-70
10215.71341880132168e-702.85670940066084e-70
10316.86751301840809e-693.43375650920405e-69
10418.6752745267291e-684.33763726336455e-68
10511.10627778300253e-665.53138891501267e-67
10611.37616289168879e-656.88081445844395e-66
10718.93188057097138e-654.46594028548569e-65
10814.99181380072563e-642.49590690036282e-64
10911.48075370128362e-637.40376850641812e-64
11014.50330024823416e-632.25165012411708e-63
11115.12525248701876e-622.56262624350938e-62
11217.4459860524187e-623.72299302620935e-62
11318.1772074277939e-614.08860371389695e-61
11413.35826688998412e-601.67913344499206e-60
11513.56328773196142e-591.78164386598071e-59
11613.43126549698685e-581.71563274849342e-58
11713.60393788572785e-571.80196894286392e-57
11813.83105609837442e-561.91552804918721e-56
11913.80001257458069e-551.90000628729035e-55
12014.34957321334726e-542.17478660667363e-54
12114.37287574767176e-532.18643787383588e-53
12213.84728910153567e-521.92364455076784e-52
12314.11387141142132e-512.05693570571066e-51
12414.26033009014125e-502.13016504507062e-50
12513.76094943254647e-491.88047471627323e-49
12611.25928982296388e-486.2964491148194e-49
12713.62134832210156e-481.81067416105078e-48
12813.04148487575771e-471.52074243787885e-47
12912.53149867144649e-461.26574933572325e-46
13012.35081473931622e-451.17540736965811e-45
13113.69215345068567e-451.84607672534284e-45
13214.1192216577937e-442.05961082889685e-44
13314.3637845752335e-432.18189228761675e-43
13411.50277493017161e-427.51387465085804e-43
13513.47862137457059e-421.7393106872853e-42
13618.64212623953184e-424.32106311976592e-42
13718.67672746054363e-414.33836373027182e-41
13818.85315146480307e-404.42657573240153e-40
13918.59778997447567e-394.29889498723784e-39
14018.21588729971402e-384.10794364985701e-38
14118.15168636954464e-374.07584318477232e-37
14211.67428932473831e-368.37144662369157e-37
14312.09138467186452e-361.04569233593226e-36
14412.2346073142979e-351.11730365714895e-35
14512.12190401115087e-341.06095200557543e-34
14612.36483068396898e-331.18241534198449e-33
14712.10185601813744e-321.05092800906872e-32
14812.03719627982232e-311.01859813991116e-31
14914.50919079680292e-312.25459539840146e-31
15014.84096202912991e-302.42048101456496e-30
15113.80080197658476e-291.90040098829238e-29
15213.63353321065011e-281.81676660532506e-28
15313.78864658892408e-271.89432329446204e-27
15412.26706698664797e-261.13353349332399e-26
15512.62724353429186e-251.31362176714593e-25
15612.40116182938443e-241.20058091469221e-24
15711.96452098714571e-239.82260493572855e-24
15811.72628350504968e-228.63141752524842e-23
15911.42553112085323e-217.12765560426616e-22
16011.56696689193834e-207.8348344596917e-21
16111.73786362259842e-198.6893181129921e-20
16212.59062256866739e-191.2953112843337e-19
16312.90734916838291e-181.45367458419145e-18
16412.04232115687496e-171.02116057843748e-17
16511.75300264226723e-168.76501321133617e-17
16612.0733246642981e-151.03666233214905e-15
1670.999999999999992.13026496974066e-141.06513248487033e-14
1680.9999999999999461.08856315509955e-135.44281577549774e-14
1690.999999999999411.17858597814816e-125.89292989074081e-13
1700.9999999999951669.66847469413014e-124.83423734706507e-12
1710.9999999999527869.44283310520995e-114.72141655260497e-11
1720.9999999995391639.2167412350288e-104.6083706175144e-10
1730.9999999963180787.36384364301155e-093.68192182150578e-09
1740.999999967263786.54724395095851e-083.27362197547925e-08
1750.9999996928741936.14251614105645e-073.07125807052823e-07
1760.9999980001720543.99965589189037e-061.99982794594518e-06
1770.9999882329150362.35341699279647e-051.17670849639824e-05
1780.9998983347074260.0002033305851476950.000101665292573848
1790.9991721478236190.001655704352762190.000827852176381096
1800.9941237359700310.01175252805993710.00587626402996855

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.726022849578412 & 0.547954300843175 & 0.273977150421588 \tabularnewline
16 & 0.586502175978991 & 0.826995648042018 & 0.413497824021009 \tabularnewline
17 & 0.448358595927143 & 0.896717191854285 & 0.551641404072857 \tabularnewline
18 & 0.327928661988363 & 0.655857323976726 & 0.672071338011637 \tabularnewline
19 & 0.222783742566517 & 0.445567485133034 & 0.777216257433483 \tabularnewline
20 & 0.145357047331419 & 0.290714094662838 & 0.854642952668581 \tabularnewline
21 & 0.0899249221024942 & 0.179849844204988 & 0.910075077897506 \tabularnewline
22 & 0.0539465153843473 & 0.107893030768695 & 0.946053484615653 \tabularnewline
23 & 0.030719755056996 & 0.0614395101139921 & 0.969280244943004 \tabularnewline
24 & 0.0178572184182221 & 0.0357144368364442 & 0.982142781581778 \tabularnewline
25 & 0.0096165976841875 & 0.019233195368375 & 0.990383402315812 \tabularnewline
26 & 0.00503755122718785 & 0.0100751024543757 & 0.994962448772812 \tabularnewline
27 & 0.00262301337036026 & 0.00524602674072053 & 0.99737698662964 \tabularnewline
28 & 0.00148128598756459 & 0.00296257197512917 & 0.998518714012435 \tabularnewline
29 & 0.000701911124957644 & 0.00140382224991529 & 0.999298088875042 \tabularnewline
30 & 0.000345433427168485 & 0.00069086685433697 & 0.999654566572831 \tabularnewline
31 & 0.0002178939428257 & 0.0004357878856514 & 0.999782106057174 \tabularnewline
32 & 0.000111196766943028 & 0.000222393533886055 & 0.999888803233057 \tabularnewline
33 & 5.38781460423764e-05 & 0.000107756292084753 & 0.999946121853958 \tabularnewline
34 & 3.35881149093968e-05 & 6.71762298187937e-05 & 0.99996641188509 \tabularnewline
35 & 1.77708100972846e-05 & 3.55416201945692e-05 & 0.999982229189903 \tabularnewline
36 & 8.92005580104038e-06 & 1.78401116020808e-05 & 0.9999910799442 \tabularnewline
37 & 4.30002820651524e-06 & 8.60005641303049e-06 & 0.999995699971793 \tabularnewline
38 & 1.87643309057237e-06 & 3.75286618114475e-06 & 0.99999812356691 \tabularnewline
39 & 9.75207805957436e-07 & 1.95041561191487e-06 & 0.999999024792194 \tabularnewline
40 & 3.90226247271059e-07 & 7.80452494542119e-07 & 0.999999609773753 \tabularnewline
41 & 2.51238558385365e-07 & 5.02477116770731e-07 & 0.999999748761442 \tabularnewline
42 & 1.04002302771598e-07 & 2.08004605543197e-07 & 0.999999895997697 \tabularnewline
43 & 5.96951212467888e-08 & 1.19390242493578e-07 & 0.999999940304879 \tabularnewline
44 & 3.58909148641954e-08 & 7.17818297283908e-08 & 0.999999964109085 \tabularnewline
45 & 1.478678391191e-08 & 2.957356782382e-08 & 0.999999985213216 \tabularnewline
46 & 5.60867652294127e-09 & 1.12173530458825e-08 & 0.999999994391323 \tabularnewline
47 & 3.1258452237175e-09 & 6.251690447435e-09 & 0.999999996874155 \tabularnewline
48 & 1.77995168098964e-09 & 3.55990336197928e-09 & 0.999999998220048 \tabularnewline
49 & 6.9145046767396e-10 & 1.38290093534792e-09 & 0.99999999930855 \tabularnewline
50 & 5.30693739602464e-10 & 1.06138747920493e-09 & 0.999999999469306 \tabularnewline
51 & 4.95843526817165e-10 & 9.9168705363433e-10 & 0.999999999504157 \tabularnewline
52 & 2.10804087657194e-10 & 4.21608175314388e-10 & 0.999999999789196 \tabularnewline
53 & 7.87832038312808e-10 & 1.57566407662562e-09 & 0.999999999212168 \tabularnewline
54 & 6.4853906362962e-09 & 1.29707812725924e-08 & 0.99999999351461 \tabularnewline
55 & 9.69230749034174e-09 & 1.93846149806835e-08 & 0.999999990307692 \tabularnewline
56 & 5.1143821791711e-05 & 0.000102287643583422 & 0.999948856178208 \tabularnewline
57 & 0.000532148231934575 & 0.00106429646386915 & 0.999467851768065 \tabularnewline
58 & 0.000364022912430179 & 0.000728045824860358 & 0.99963597708757 \tabularnewline
59 & 0.00496257844778121 & 0.00992515689556242 & 0.995037421552219 \tabularnewline
60 & 0.00373324290612421 & 0.00746648581224843 & 0.996266757093876 \tabularnewline
61 & 0.00433231627909076 & 0.00866463255818152 & 0.99566768372091 \tabularnewline
62 & 0.0120815299858236 & 0.0241630599716472 & 0.987918470014176 \tabularnewline
63 & 0.0186645380480343 & 0.0373290760960687 & 0.981335461951966 \tabularnewline
64 & 0.015188847595837 & 0.0303776951916739 & 0.984811152404163 \tabularnewline
65 & 0.0347595746126749 & 0.0695191492253498 & 0.965240425387325 \tabularnewline
66 & 0.644627432917542 & 0.710745134164916 & 0.355372567082458 \tabularnewline
67 & 0.827959975949198 & 0.344080048101603 & 0.172040024050802 \tabularnewline
68 & 0.99549880632455 & 0.00900238735090175 & 0.00450119367545087 \tabularnewline
69 & 0.999999507969513 & 9.8406097467456e-07 & 4.9203048733728e-07 \tabularnewline
70 & 1 & 1.75266381181876e-15 & 8.7633190590938e-16 \tabularnewline
71 & 1 & 2.41501192897529e-19 & 1.20750596448765e-19 \tabularnewline
72 & 1 & 1.3115611910831e-22 & 6.55780595541548e-23 \tabularnewline
73 & 1 & 4.26047181738403e-26 & 2.13023590869201e-26 \tabularnewline
74 & 1 & 1.16521809713759e-25 & 5.82609048568796e-26 \tabularnewline
75 & 1 & 8.98331190888704e-26 & 4.49165595444352e-26 \tabularnewline
76 & 1 & 1.19475099814744e-25 & 5.97375499073722e-26 \tabularnewline
77 & 1 & 2.20036477666766e-27 & 1.10018238833383e-27 \tabularnewline
78 & 1 & 5.21148960204894e-35 & 2.60574480102447e-35 \tabularnewline
79 & 1 & 5.01729086429533e-36 & 2.50864543214767e-36 \tabularnewline
80 & 1 & 3.32773286230554e-36 & 1.66386643115277e-36 \tabularnewline
81 & 1 & 2.60227300593062e-37 & 1.30113650296531e-37 \tabularnewline
82 & 1 & 2.61395175304279e-37 & 1.3069758765214e-37 \tabularnewline
83 & 1 & 3.63126769916008e-41 & 1.81563384958004e-41 \tabularnewline
84 & 1 & 2.846018669348e-45 & 1.423009334674e-45 \tabularnewline
85 & 1 & 2.49046336878845e-78 & 1.24523168439423e-78 \tabularnewline
86 & 1 & 2.19569407871288e-78 & 1.09784703935644e-78 \tabularnewline
87 & 1 & 1.72976671580459e-83 & 8.64883357902294e-84 \tabularnewline
88 & 1 & 1.41434594042342e-82 & 7.07172970211711e-83 \tabularnewline
89 & 1 & 1.51755672465563e-81 & 7.58778362327814e-82 \tabularnewline
90 & 1 & 1.69986073335995e-80 & 8.49930366679975e-81 \tabularnewline
91 & 1 & 2.15905800105628e-79 & 1.07952900052814e-79 \tabularnewline
92 & 1 & 1.25468568967617e-78 & 6.27342844838083e-79 \tabularnewline
93 & 1 & 1.73454075378966e-77 & 8.67270376894828e-78 \tabularnewline
94 & 1 & 2.10406774039786e-76 & 1.05203387019893e-76 \tabularnewline
95 & 1 & 1.6164945881714e-75 & 8.082472940857e-76 \tabularnewline
96 & 1 & 1.97646728004629e-74 & 9.88233640023144e-75 \tabularnewline
97 & 1 & 2.36542352007081e-73 & 1.18271176003541e-73 \tabularnewline
98 & 1 & 3.18223477861644e-72 & 1.59111738930822e-72 \tabularnewline
99 & 1 & 1.79087920402013e-71 & 8.95439602010063e-72 \tabularnewline
100 & 1 & 1.94125578219306e-70 & 9.70627891096532e-71 \tabularnewline
101 & 1 & 3.83878111263253e-70 & 1.91939055631627e-70 \tabularnewline
102 & 1 & 5.71341880132168e-70 & 2.85670940066084e-70 \tabularnewline
103 & 1 & 6.86751301840809e-69 & 3.43375650920405e-69 \tabularnewline
104 & 1 & 8.6752745267291e-68 & 4.33763726336455e-68 \tabularnewline
105 & 1 & 1.10627778300253e-66 & 5.53138891501267e-67 \tabularnewline
106 & 1 & 1.37616289168879e-65 & 6.88081445844395e-66 \tabularnewline
107 & 1 & 8.93188057097138e-65 & 4.46594028548569e-65 \tabularnewline
108 & 1 & 4.99181380072563e-64 & 2.49590690036282e-64 \tabularnewline
109 & 1 & 1.48075370128362e-63 & 7.40376850641812e-64 \tabularnewline
110 & 1 & 4.50330024823416e-63 & 2.25165012411708e-63 \tabularnewline
111 & 1 & 5.12525248701876e-62 & 2.56262624350938e-62 \tabularnewline
112 & 1 & 7.4459860524187e-62 & 3.72299302620935e-62 \tabularnewline
113 & 1 & 8.1772074277939e-61 & 4.08860371389695e-61 \tabularnewline
114 & 1 & 3.35826688998412e-60 & 1.67913344499206e-60 \tabularnewline
115 & 1 & 3.56328773196142e-59 & 1.78164386598071e-59 \tabularnewline
116 & 1 & 3.43126549698685e-58 & 1.71563274849342e-58 \tabularnewline
117 & 1 & 3.60393788572785e-57 & 1.80196894286392e-57 \tabularnewline
118 & 1 & 3.83105609837442e-56 & 1.91552804918721e-56 \tabularnewline
119 & 1 & 3.80001257458069e-55 & 1.90000628729035e-55 \tabularnewline
120 & 1 & 4.34957321334726e-54 & 2.17478660667363e-54 \tabularnewline
121 & 1 & 4.37287574767176e-53 & 2.18643787383588e-53 \tabularnewline
122 & 1 & 3.84728910153567e-52 & 1.92364455076784e-52 \tabularnewline
123 & 1 & 4.11387141142132e-51 & 2.05693570571066e-51 \tabularnewline
124 & 1 & 4.26033009014125e-50 & 2.13016504507062e-50 \tabularnewline
125 & 1 & 3.76094943254647e-49 & 1.88047471627323e-49 \tabularnewline
126 & 1 & 1.25928982296388e-48 & 6.2964491148194e-49 \tabularnewline
127 & 1 & 3.62134832210156e-48 & 1.81067416105078e-48 \tabularnewline
128 & 1 & 3.04148487575771e-47 & 1.52074243787885e-47 \tabularnewline
129 & 1 & 2.53149867144649e-46 & 1.26574933572325e-46 \tabularnewline
130 & 1 & 2.35081473931622e-45 & 1.17540736965811e-45 \tabularnewline
131 & 1 & 3.69215345068567e-45 & 1.84607672534284e-45 \tabularnewline
132 & 1 & 4.1192216577937e-44 & 2.05961082889685e-44 \tabularnewline
133 & 1 & 4.3637845752335e-43 & 2.18189228761675e-43 \tabularnewline
134 & 1 & 1.50277493017161e-42 & 7.51387465085804e-43 \tabularnewline
135 & 1 & 3.47862137457059e-42 & 1.7393106872853e-42 \tabularnewline
136 & 1 & 8.64212623953184e-42 & 4.32106311976592e-42 \tabularnewline
137 & 1 & 8.67672746054363e-41 & 4.33836373027182e-41 \tabularnewline
138 & 1 & 8.85315146480307e-40 & 4.42657573240153e-40 \tabularnewline
139 & 1 & 8.59778997447567e-39 & 4.29889498723784e-39 \tabularnewline
140 & 1 & 8.21588729971402e-38 & 4.10794364985701e-38 \tabularnewline
141 & 1 & 8.15168636954464e-37 & 4.07584318477232e-37 \tabularnewline
142 & 1 & 1.67428932473831e-36 & 8.37144662369157e-37 \tabularnewline
143 & 1 & 2.09138467186452e-36 & 1.04569233593226e-36 \tabularnewline
144 & 1 & 2.2346073142979e-35 & 1.11730365714895e-35 \tabularnewline
145 & 1 & 2.12190401115087e-34 & 1.06095200557543e-34 \tabularnewline
146 & 1 & 2.36483068396898e-33 & 1.18241534198449e-33 \tabularnewline
147 & 1 & 2.10185601813744e-32 & 1.05092800906872e-32 \tabularnewline
148 & 1 & 2.03719627982232e-31 & 1.01859813991116e-31 \tabularnewline
149 & 1 & 4.50919079680292e-31 & 2.25459539840146e-31 \tabularnewline
150 & 1 & 4.84096202912991e-30 & 2.42048101456496e-30 \tabularnewline
151 & 1 & 3.80080197658476e-29 & 1.90040098829238e-29 \tabularnewline
152 & 1 & 3.63353321065011e-28 & 1.81676660532506e-28 \tabularnewline
153 & 1 & 3.78864658892408e-27 & 1.89432329446204e-27 \tabularnewline
154 & 1 & 2.26706698664797e-26 & 1.13353349332399e-26 \tabularnewline
155 & 1 & 2.62724353429186e-25 & 1.31362176714593e-25 \tabularnewline
156 & 1 & 2.40116182938443e-24 & 1.20058091469221e-24 \tabularnewline
157 & 1 & 1.96452098714571e-23 & 9.82260493572855e-24 \tabularnewline
158 & 1 & 1.72628350504968e-22 & 8.63141752524842e-23 \tabularnewline
159 & 1 & 1.42553112085323e-21 & 7.12765560426616e-22 \tabularnewline
160 & 1 & 1.56696689193834e-20 & 7.8348344596917e-21 \tabularnewline
161 & 1 & 1.73786362259842e-19 & 8.6893181129921e-20 \tabularnewline
162 & 1 & 2.59062256866739e-19 & 1.2953112843337e-19 \tabularnewline
163 & 1 & 2.90734916838291e-18 & 1.45367458419145e-18 \tabularnewline
164 & 1 & 2.04232115687496e-17 & 1.02116057843748e-17 \tabularnewline
165 & 1 & 1.75300264226723e-16 & 8.76501321133617e-17 \tabularnewline
166 & 1 & 2.0733246642981e-15 & 1.03666233214905e-15 \tabularnewline
167 & 0.99999999999999 & 2.13026496974066e-14 & 1.06513248487033e-14 \tabularnewline
168 & 0.999999999999946 & 1.08856315509955e-13 & 5.44281577549774e-14 \tabularnewline
169 & 0.99999999999941 & 1.17858597814816e-12 & 5.89292989074081e-13 \tabularnewline
170 & 0.999999999995166 & 9.66847469413014e-12 & 4.83423734706507e-12 \tabularnewline
171 & 0.999999999952786 & 9.44283310520995e-11 & 4.72141655260497e-11 \tabularnewline
172 & 0.999999999539163 & 9.2167412350288e-10 & 4.6083706175144e-10 \tabularnewline
173 & 0.999999996318078 & 7.36384364301155e-09 & 3.68192182150578e-09 \tabularnewline
174 & 0.99999996726378 & 6.54724395095851e-08 & 3.27362197547925e-08 \tabularnewline
175 & 0.999999692874193 & 6.14251614105645e-07 & 3.07125807052823e-07 \tabularnewline
176 & 0.999998000172054 & 3.99965589189037e-06 & 1.99982794594518e-06 \tabularnewline
177 & 0.999988232915036 & 2.35341699279647e-05 & 1.17670849639824e-05 \tabularnewline
178 & 0.999898334707426 & 0.000203330585147695 & 0.000101665292573848 \tabularnewline
179 & 0.999172147823619 & 0.00165570435276219 & 0.000827852176381096 \tabularnewline
180 & 0.994123735970031 & 0.0117525280599371 & 0.00587626402996855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116296&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]0.726022849578412[/C][C]0.547954300843175[/C][C]0.273977150421588[/C][/ROW]
[ROW][C]16[/C][C]0.586502175978991[/C][C]0.826995648042018[/C][C]0.413497824021009[/C][/ROW]
[ROW][C]17[/C][C]0.448358595927143[/C][C]0.896717191854285[/C][C]0.551641404072857[/C][/ROW]
[ROW][C]18[/C][C]0.327928661988363[/C][C]0.655857323976726[/C][C]0.672071338011637[/C][/ROW]
[ROW][C]19[/C][C]0.222783742566517[/C][C]0.445567485133034[/C][C]0.777216257433483[/C][/ROW]
[ROW][C]20[/C][C]0.145357047331419[/C][C]0.290714094662838[/C][C]0.854642952668581[/C][/ROW]
[ROW][C]21[/C][C]0.0899249221024942[/C][C]0.179849844204988[/C][C]0.910075077897506[/C][/ROW]
[ROW][C]22[/C][C]0.0539465153843473[/C][C]0.107893030768695[/C][C]0.946053484615653[/C][/ROW]
[ROW][C]23[/C][C]0.030719755056996[/C][C]0.0614395101139921[/C][C]0.969280244943004[/C][/ROW]
[ROW][C]24[/C][C]0.0178572184182221[/C][C]0.0357144368364442[/C][C]0.982142781581778[/C][/ROW]
[ROW][C]25[/C][C]0.0096165976841875[/C][C]0.019233195368375[/C][C]0.990383402315812[/C][/ROW]
[ROW][C]26[/C][C]0.00503755122718785[/C][C]0.0100751024543757[/C][C]0.994962448772812[/C][/ROW]
[ROW][C]27[/C][C]0.00262301337036026[/C][C]0.00524602674072053[/C][C]0.99737698662964[/C][/ROW]
[ROW][C]28[/C][C]0.00148128598756459[/C][C]0.00296257197512917[/C][C]0.998518714012435[/C][/ROW]
[ROW][C]29[/C][C]0.000701911124957644[/C][C]0.00140382224991529[/C][C]0.999298088875042[/C][/ROW]
[ROW][C]30[/C][C]0.000345433427168485[/C][C]0.00069086685433697[/C][C]0.999654566572831[/C][/ROW]
[ROW][C]31[/C][C]0.0002178939428257[/C][C]0.0004357878856514[/C][C]0.999782106057174[/C][/ROW]
[ROW][C]32[/C][C]0.000111196766943028[/C][C]0.000222393533886055[/C][C]0.999888803233057[/C][/ROW]
[ROW][C]33[/C][C]5.38781460423764e-05[/C][C]0.000107756292084753[/C][C]0.999946121853958[/C][/ROW]
[ROW][C]34[/C][C]3.35881149093968e-05[/C][C]6.71762298187937e-05[/C][C]0.99996641188509[/C][/ROW]
[ROW][C]35[/C][C]1.77708100972846e-05[/C][C]3.55416201945692e-05[/C][C]0.999982229189903[/C][/ROW]
[ROW][C]36[/C][C]8.92005580104038e-06[/C][C]1.78401116020808e-05[/C][C]0.9999910799442[/C][/ROW]
[ROW][C]37[/C][C]4.30002820651524e-06[/C][C]8.60005641303049e-06[/C][C]0.999995699971793[/C][/ROW]
[ROW][C]38[/C][C]1.87643309057237e-06[/C][C]3.75286618114475e-06[/C][C]0.99999812356691[/C][/ROW]
[ROW][C]39[/C][C]9.75207805957436e-07[/C][C]1.95041561191487e-06[/C][C]0.999999024792194[/C][/ROW]
[ROW][C]40[/C][C]3.90226247271059e-07[/C][C]7.80452494542119e-07[/C][C]0.999999609773753[/C][/ROW]
[ROW][C]41[/C][C]2.51238558385365e-07[/C][C]5.02477116770731e-07[/C][C]0.999999748761442[/C][/ROW]
[ROW][C]42[/C][C]1.04002302771598e-07[/C][C]2.08004605543197e-07[/C][C]0.999999895997697[/C][/ROW]
[ROW][C]43[/C][C]5.96951212467888e-08[/C][C]1.19390242493578e-07[/C][C]0.999999940304879[/C][/ROW]
[ROW][C]44[/C][C]3.58909148641954e-08[/C][C]7.17818297283908e-08[/C][C]0.999999964109085[/C][/ROW]
[ROW][C]45[/C][C]1.478678391191e-08[/C][C]2.957356782382e-08[/C][C]0.999999985213216[/C][/ROW]
[ROW][C]46[/C][C]5.60867652294127e-09[/C][C]1.12173530458825e-08[/C][C]0.999999994391323[/C][/ROW]
[ROW][C]47[/C][C]3.1258452237175e-09[/C][C]6.251690447435e-09[/C][C]0.999999996874155[/C][/ROW]
[ROW][C]48[/C][C]1.77995168098964e-09[/C][C]3.55990336197928e-09[/C][C]0.999999998220048[/C][/ROW]
[ROW][C]49[/C][C]6.9145046767396e-10[/C][C]1.38290093534792e-09[/C][C]0.99999999930855[/C][/ROW]
[ROW][C]50[/C][C]5.30693739602464e-10[/C][C]1.06138747920493e-09[/C][C]0.999999999469306[/C][/ROW]
[ROW][C]51[/C][C]4.95843526817165e-10[/C][C]9.9168705363433e-10[/C][C]0.999999999504157[/C][/ROW]
[ROW][C]52[/C][C]2.10804087657194e-10[/C][C]4.21608175314388e-10[/C][C]0.999999999789196[/C][/ROW]
[ROW][C]53[/C][C]7.87832038312808e-10[/C][C]1.57566407662562e-09[/C][C]0.999999999212168[/C][/ROW]
[ROW][C]54[/C][C]6.4853906362962e-09[/C][C]1.29707812725924e-08[/C][C]0.99999999351461[/C][/ROW]
[ROW][C]55[/C][C]9.69230749034174e-09[/C][C]1.93846149806835e-08[/C][C]0.999999990307692[/C][/ROW]
[ROW][C]56[/C][C]5.1143821791711e-05[/C][C]0.000102287643583422[/C][C]0.999948856178208[/C][/ROW]
[ROW][C]57[/C][C]0.000532148231934575[/C][C]0.00106429646386915[/C][C]0.999467851768065[/C][/ROW]
[ROW][C]58[/C][C]0.000364022912430179[/C][C]0.000728045824860358[/C][C]0.99963597708757[/C][/ROW]
[ROW][C]59[/C][C]0.00496257844778121[/C][C]0.00992515689556242[/C][C]0.995037421552219[/C][/ROW]
[ROW][C]60[/C][C]0.00373324290612421[/C][C]0.00746648581224843[/C][C]0.996266757093876[/C][/ROW]
[ROW][C]61[/C][C]0.00433231627909076[/C][C]0.00866463255818152[/C][C]0.99566768372091[/C][/ROW]
[ROW][C]62[/C][C]0.0120815299858236[/C][C]0.0241630599716472[/C][C]0.987918470014176[/C][/ROW]
[ROW][C]63[/C][C]0.0186645380480343[/C][C]0.0373290760960687[/C][C]0.981335461951966[/C][/ROW]
[ROW][C]64[/C][C]0.015188847595837[/C][C]0.0303776951916739[/C][C]0.984811152404163[/C][/ROW]
[ROW][C]65[/C][C]0.0347595746126749[/C][C]0.0695191492253498[/C][C]0.965240425387325[/C][/ROW]
[ROW][C]66[/C][C]0.644627432917542[/C][C]0.710745134164916[/C][C]0.355372567082458[/C][/ROW]
[ROW][C]67[/C][C]0.827959975949198[/C][C]0.344080048101603[/C][C]0.172040024050802[/C][/ROW]
[ROW][C]68[/C][C]0.99549880632455[/C][C]0.00900238735090175[/C][C]0.00450119367545087[/C][/ROW]
[ROW][C]69[/C][C]0.999999507969513[/C][C]9.8406097467456e-07[/C][C]4.9203048733728e-07[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.75266381181876e-15[/C][C]8.7633190590938e-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]1.3115611910831e-22[/C][C]6.55780595541548e-23[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]4.26047181738403e-26[/C][C]2.13023590869201e-26[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.16521809713759e-25[/C][C]5.82609048568796e-26[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]8.98331190888704e-26[/C][C]4.49165595444352e-26[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.19475099814744e-25[/C][C]5.97375499073722e-26[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]2.20036477666766e-27[/C][C]1.10018238833383e-27[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]5.21148960204894e-35[/C][C]2.60574480102447e-35[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]5.01729086429533e-36[/C][C]2.50864543214767e-36[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]3.32773286230554e-36[/C][C]1.66386643115277e-36[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.60227300593062e-37[/C][C]1.30113650296531e-37[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]2.61395175304279e-37[/C][C]1.3069758765214e-37[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]3.63126769916008e-41[/C][C]1.81563384958004e-41[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]2.846018669348e-45[/C][C]1.423009334674e-45[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]2.49046336878845e-78[/C][C]1.24523168439423e-78[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]2.19569407871288e-78[/C][C]1.09784703935644e-78[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.72976671580459e-83[/C][C]8.64883357902294e-84[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.41434594042342e-82[/C][C]7.07172970211711e-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]1.69986073335995e-80[/C][C]8.49930366679975e-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]1.73454075378966e-77[/C][C]8.67270376894828e-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]1.6164945881714e-75[/C][C]8.082472940857e-76[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.97646728004629e-74[/C][C]9.88233640023144e-75[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]2.36542352007081e-73[/C][C]1.18271176003541e-73[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]3.18223477861644e-72[/C][C]1.59111738930822e-72[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.79087920402013e-71[/C][C]8.95439602010063e-72[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.94125578219306e-70[/C][C]9.70627891096532e-71[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]3.83878111263253e-70[/C][C]1.91939055631627e-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]8.6752745267291e-68[/C][C]4.33763726336455e-68[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.10627778300253e-66[/C][C]5.53138891501267e-67[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.37616289168879e-65[/C][C]6.88081445844395e-66[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]8.93188057097138e-65[/C][C]4.46594028548569e-65[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]4.99181380072563e-64[/C][C]2.49590690036282e-64[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.48075370128362e-63[/C][C]7.40376850641812e-64[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]4.50330024823416e-63[/C][C]2.25165012411708e-63[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]5.12525248701876e-62[/C][C]2.56262624350938e-62[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]7.4459860524187e-62[/C][C]3.72299302620935e-62[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]8.1772074277939e-61[/C][C]4.08860371389695e-61[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]3.35826688998412e-60[/C][C]1.67913344499206e-60[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]3.56328773196142e-59[/C][C]1.78164386598071e-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]3.60393788572785e-57[/C][C]1.80196894286392e-57[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]3.83105609837442e-56[/C][C]1.91552804918721e-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]4.34957321334726e-54[/C][C]2.17478660667363e-54[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]4.37287574767176e-53[/C][C]2.18643787383588e-53[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]3.84728910153567e-52[/C][C]1.92364455076784e-52[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]4.11387141142132e-51[/C][C]2.05693570571066e-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]3.76094943254647e-49[/C][C]1.88047471627323e-49[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.25928982296388e-48[/C][C]6.2964491148194e-49[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]3.62134832210156e-48[/C][C]1.81067416105078e-48[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]3.04148487575771e-47[/C][C]1.52074243787885e-47[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]2.53149867144649e-46[/C][C]1.26574933572325e-46[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]2.35081473931622e-45[/C][C]1.17540736965811e-45[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]3.69215345068567e-45[/C][C]1.84607672534284e-45[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.1192216577937e-44[/C][C]2.05961082889685e-44[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]4.3637845752335e-43[/C][C]2.18189228761675e-43[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.50277493017161e-42[/C][C]7.51387465085804e-43[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]3.47862137457059e-42[/C][C]1.7393106872853e-42[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]8.64212623953184e-42[/C][C]4.32106311976592e-42[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]8.67672746054363e-41[/C][C]4.33836373027182e-41[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]8.85315146480307e-40[/C][C]4.42657573240153e-40[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]8.59778997447567e-39[/C][C]4.29889498723784e-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]8.15168636954464e-37[/C][C]4.07584318477232e-37[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.67428932473831e-36[/C][C]8.37144662369157e-37[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]2.09138467186452e-36[/C][C]1.04569233593226e-36[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]2.2346073142979e-35[/C][C]1.11730365714895e-35[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]2.12190401115087e-34[/C][C]1.06095200557543e-34[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]2.36483068396898e-33[/C][C]1.18241534198449e-33[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]2.10185601813744e-32[/C][C]1.05092800906872e-32[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]2.03719627982232e-31[/C][C]1.01859813991116e-31[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]4.50919079680292e-31[/C][C]2.25459539840146e-31[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]4.84096202912991e-30[/C][C]2.42048101456496e-30[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]3.80080197658476e-29[/C][C]1.90040098829238e-29[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]3.63353321065011e-28[/C][C]1.81676660532506e-28[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]3.78864658892408e-27[/C][C]1.89432329446204e-27[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]2.26706698664797e-26[/C][C]1.13353349332399e-26[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]2.62724353429186e-25[/C][C]1.31362176714593e-25[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]2.40116182938443e-24[/C][C]1.20058091469221e-24[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.96452098714571e-23[/C][C]9.82260493572855e-24[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.72628350504968e-22[/C][C]8.63141752524842e-23[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.42553112085323e-21[/C][C]7.12765560426616e-22[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.56696689193834e-20[/C][C]7.8348344596917e-21[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.73786362259842e-19[/C][C]8.6893181129921e-20[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]2.59062256866739e-19[/C][C]1.2953112843337e-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]2.04232115687496e-17[/C][C]1.02116057843748e-17[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.75300264226723e-16[/C][C]8.76501321133617e-17[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]2.0733246642981e-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]1.08856315509955e-13[/C][C]5.44281577549774e-14[/C][/ROW]
[ROW][C]169[/C][C]0.99999999999941[/C][C]1.17858597814816e-12[/C][C]5.89292989074081e-13[/C][/ROW]
[ROW][C]170[/C][C]0.999999999995166[/C][C]9.66847469413014e-12[/C][C]4.83423734706507e-12[/C][/ROW]
[ROW][C]171[/C][C]0.999999999952786[/C][C]9.44283310520995e-11[/C][C]4.72141655260497e-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]7.36384364301155e-09[/C][C]3.68192182150578e-09[/C][/ROW]
[ROW][C]174[/C][C]0.99999996726378[/C][C]6.54724395095851e-08[/C][C]3.27362197547925e-08[/C][/ROW]
[ROW][C]175[/C][C]0.999999692874193[/C][C]6.14251614105645e-07[/C][C]3.07125807052823e-07[/C][/ROW]
[ROW][C]176[/C][C]0.999998000172054[/C][C]3.99965589189037e-06[/C][C]1.99982794594518e-06[/C][/ROW]
[ROW][C]177[/C][C]0.999988232915036[/C][C]2.35341699279647e-05[/C][C]1.17670849639824e-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]0.000827852176381096[/C][/ROW]
[ROW][C]180[/C][C]0.994123735970031[/C][C]0.0117525280599371[/C][C]0.00587626402996855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116296&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&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
150.7260228495784120.5479543008431750.273977150421588
160.5865021759789910.8269956480420180.413497824021009
170.4483585959271430.8967171918542850.551641404072857
180.3279286619883630.6558573239767260.672071338011637
190.2227837425665170.4455674851330340.777216257433483
200.1453570473314190.2907140946628380.854642952668581
210.08992492210249420.1798498442049880.910075077897506
220.05394651538434730.1078930307686950.946053484615653
230.0307197550569960.06143951011399210.969280244943004
240.01785721841822210.03571443683644420.982142781581778
250.00961659768418750.0192331953683750.990383402315812
260.005037551227187850.01007510245437570.994962448772812
270.002623013370360260.005246026740720530.99737698662964
280.001481285987564590.002962571975129170.998518714012435
290.0007019111249576440.001403822249915290.999298088875042
300.0003454334271684850.000690866854336970.999654566572831
310.00021789394282570.00043578788565140.999782106057174
320.0001111967669430280.0002223935338860550.999888803233057
335.38781460423764e-050.0001077562920847530.999946121853958
343.35881149093968e-056.71762298187937e-050.99996641188509
351.77708100972846e-053.55416201945692e-050.999982229189903
368.92005580104038e-061.78401116020808e-050.9999910799442
374.30002820651524e-068.60005641303049e-060.999995699971793
381.87643309057237e-063.75286618114475e-060.99999812356691
399.75207805957436e-071.95041561191487e-060.999999024792194
403.90226247271059e-077.80452494542119e-070.999999609773753
412.51238558385365e-075.02477116770731e-070.999999748761442
421.04002302771598e-072.08004605543197e-070.999999895997697
435.96951212467888e-081.19390242493578e-070.999999940304879
443.58909148641954e-087.17818297283908e-080.999999964109085
451.478678391191e-082.957356782382e-080.999999985213216
465.60867652294127e-091.12173530458825e-080.999999994391323
473.1258452237175e-096.251690447435e-090.999999996874155
481.77995168098964e-093.55990336197928e-090.999999998220048
496.9145046767396e-101.38290093534792e-090.99999999930855
505.30693739602464e-101.06138747920493e-090.999999999469306
514.95843526817165e-109.9168705363433e-100.999999999504157
522.10804087657194e-104.21608175314388e-100.999999999789196
537.87832038312808e-101.57566407662562e-090.999999999212168
546.4853906362962e-091.29707812725924e-080.99999999351461
559.69230749034174e-091.93846149806835e-080.999999990307692
565.1143821791711e-050.0001022876435834220.999948856178208
570.0005321482319345750.001064296463869150.999467851768065
580.0003640229124301790.0007280458248603580.99963597708757
590.004962578447781210.009925156895562420.995037421552219
600.003733242906124210.007466485812248430.996266757093876
610.004332316279090760.008664632558181520.99566768372091
620.01208152998582360.02416305997164720.987918470014176
630.01866453804803430.03732907609606870.981335461951966
640.0151888475958370.03037769519167390.984811152404163
650.03475957461267490.06951914922534980.965240425387325
660.6446274329175420.7107451341649160.355372567082458
670.8279599759491980.3440800481016030.172040024050802
680.995498806324550.009002387350901750.00450119367545087
690.9999995079695139.8406097467456e-074.9203048733728e-07
7011.75266381181876e-158.7633190590938e-16
7112.41501192897529e-191.20750596448765e-19
7211.3115611910831e-226.55780595541548e-23
7314.26047181738403e-262.13023590869201e-26
7411.16521809713759e-255.82609048568796e-26
7518.98331190888704e-264.49165595444352e-26
7611.19475099814744e-255.97375499073722e-26
7712.20036477666766e-271.10018238833383e-27
7815.21148960204894e-352.60574480102447e-35
7915.01729086429533e-362.50864543214767e-36
8013.32773286230554e-361.66386643115277e-36
8112.60227300593062e-371.30113650296531e-37
8212.61395175304279e-371.3069758765214e-37
8313.63126769916008e-411.81563384958004e-41
8412.846018669348e-451.423009334674e-45
8512.49046336878845e-781.24523168439423e-78
8612.19569407871288e-781.09784703935644e-78
8711.72976671580459e-838.64883357902294e-84
8811.41434594042342e-827.07172970211711e-83
8911.51755672465563e-817.58778362327814e-82
9011.69986073335995e-808.49930366679975e-81
9112.15905800105628e-791.07952900052814e-79
9211.25468568967617e-786.27342844838083e-79
9311.73454075378966e-778.67270376894828e-78
9412.10406774039786e-761.05203387019893e-76
9511.6164945881714e-758.082472940857e-76
9611.97646728004629e-749.88233640023144e-75
9712.36542352007081e-731.18271176003541e-73
9813.18223477861644e-721.59111738930822e-72
9911.79087920402013e-718.95439602010063e-72
10011.94125578219306e-709.70627891096532e-71
10113.83878111263253e-701.91939055631627e-70
10215.71341880132168e-702.85670940066084e-70
10316.86751301840809e-693.43375650920405e-69
10418.6752745267291e-684.33763726336455e-68
10511.10627778300253e-665.53138891501267e-67
10611.37616289168879e-656.88081445844395e-66
10718.93188057097138e-654.46594028548569e-65
10814.99181380072563e-642.49590690036282e-64
10911.48075370128362e-637.40376850641812e-64
11014.50330024823416e-632.25165012411708e-63
11115.12525248701876e-622.56262624350938e-62
11217.4459860524187e-623.72299302620935e-62
11318.1772074277939e-614.08860371389695e-61
11413.35826688998412e-601.67913344499206e-60
11513.56328773196142e-591.78164386598071e-59
11613.43126549698685e-581.71563274849342e-58
11713.60393788572785e-571.80196894286392e-57
11813.83105609837442e-561.91552804918721e-56
11913.80001257458069e-551.90000628729035e-55
12014.34957321334726e-542.17478660667363e-54
12114.37287574767176e-532.18643787383588e-53
12213.84728910153567e-521.92364455076784e-52
12314.11387141142132e-512.05693570571066e-51
12414.26033009014125e-502.13016504507062e-50
12513.76094943254647e-491.88047471627323e-49
12611.25928982296388e-486.2964491148194e-49
12713.62134832210156e-481.81067416105078e-48
12813.04148487575771e-471.52074243787885e-47
12912.53149867144649e-461.26574933572325e-46
13012.35081473931622e-451.17540736965811e-45
13113.69215345068567e-451.84607672534284e-45
13214.1192216577937e-442.05961082889685e-44
13314.3637845752335e-432.18189228761675e-43
13411.50277493017161e-427.51387465085804e-43
13513.47862137457059e-421.7393106872853e-42
13618.64212623953184e-424.32106311976592e-42
13718.67672746054363e-414.33836373027182e-41
13818.85315146480307e-404.42657573240153e-40
13918.59778997447567e-394.29889498723784e-39
14018.21588729971402e-384.10794364985701e-38
14118.15168636954464e-374.07584318477232e-37
14211.67428932473831e-368.37144662369157e-37
14312.09138467186452e-361.04569233593226e-36
14412.2346073142979e-351.11730365714895e-35
14512.12190401115087e-341.06095200557543e-34
14612.36483068396898e-331.18241534198449e-33
14712.10185601813744e-321.05092800906872e-32
14812.03719627982232e-311.01859813991116e-31
14914.50919079680292e-312.25459539840146e-31
15014.84096202912991e-302.42048101456496e-30
15113.80080197658476e-291.90040098829238e-29
15213.63353321065011e-281.81676660532506e-28
15313.78864658892408e-271.89432329446204e-27
15412.26706698664797e-261.13353349332399e-26
15512.62724353429186e-251.31362176714593e-25
15612.40116182938443e-241.20058091469221e-24
15711.96452098714571e-239.82260493572855e-24
15811.72628350504968e-228.63141752524842e-23
15911.42553112085323e-217.12765560426616e-22
16011.56696689193834e-207.8348344596917e-21
16111.73786362259842e-198.6893181129921e-20
16212.59062256866739e-191.2953112843337e-19
16312.90734916838291e-181.45367458419145e-18
16412.04232115687496e-171.02116057843748e-17
16511.75300264226723e-168.76501321133617e-17
16612.0733246642981e-151.03666233214905e-15
1670.999999999999992.13026496974066e-141.06513248487033e-14
1680.9999999999999461.08856315509955e-135.44281577549774e-14
1690.999999999999411.17858597814816e-125.89292989074081e-13
1700.9999999999951669.66847469413014e-124.83423734706507e-12
1710.9999999999527869.44283310520995e-114.72141655260497e-11
1720.9999999995391639.2167412350288e-104.6083706175144e-10
1730.9999999963180787.36384364301155e-093.68192182150578e-09
1740.999999967263786.54724395095851e-083.27362197547925e-08
1750.9999996928741936.14251614105645e-073.07125807052823e-07
1760.9999980001720543.99965589189037e-061.99982794594518e-06
1770.9999882329150362.35341699279647e-051.17670849639824e-05
1780.9998983347074260.0002033305851476950.000101665292573848
1790.9991721478236190.001655704352762190.000827852176381096
1800.9941237359700310.01175252805993710.00587626402996855






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=116296&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=116296&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116296&T=6

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


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

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


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}