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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 22 Nov 2011 12:14:48 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321982107hhc4bjxhhlmj8e9.htm/, Retrieved Sat, 07 Jun 2025 10:17:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146322, Retrieved Sat, 07 Jun 2025 10:17:01 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

272

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D      [Multiple Regression] [ws7-1] [2011-11-22 10:24:02] [f7a862281046b7153543b12c78921b36]
-    D        [Multiple Regression] [ws7-1] [2011-11-22 10:38:43] [f7a862281046b7153543b12c78921b36]
- R  D            [Multiple Regression] [ws7-3] [2011-11-22 17:14:48] [47995d3a8fac585eeb070a274b466f8c] [Current]
-   P               [Multiple Regression] [ws7-3] [2011-11-22 17:19:19] [f7a862281046b7153543b12c78921b36] 
-    D                [Multiple Regression] [paper2-3] [2011-12-21 18:58:43] [f7a862281046b7153543b12c78921b36] 
-   P                   [Multiple Regression] [paper2-4] [2011-12-21 19:12:45] [f7a862281046b7153543b12c78921b36] 
-    D                    [Multiple Regression] [paper2-5] [2011-12-21 19:37:25] [f7a862281046b7153543b12c78921b36] 

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Dataseries X:

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11	14	3	2	3	3	3	7	6
11	8	5	6	0	7	7	2	7
11	12	6	6	0	6	8	3	8
11	7	6	6	6	6	9	8	8
11	10	7	8	5	5	5	7	9
11	9	3	1	0	7	7	7	8
11	16	8	9	8	8	8	9	8
11	7	4	4	0	2	3	2	7
11	14	7	7	0	4	8	4	7
11	6	4	4	9	9	4	4	4
11	16	6	6	6	6	6	6	6
11	11	6	5	6	6	4	4	7
11	17	7	7	5	5	8	9	5
11	12	4	5	4	4	8	8	8
11	7	6	6	0	2	2	7	5
11	13	5	5	0	4	9	4	4
11	9	0	2	2	2	2	2	9
11	15	9	9	6	6	8	8	8
11	7	4	4	0	4	8	4	4
11	9	4	4	4	4	4	4	6
11	7	2	5	5	5	5	2	6
11	14	7	7	7	7	7	9	7
11	15	5	5	5	5	3	3	3
11	7	9	9	4	4	4	4	4
11	13	6	6	6	6	6	6	6
11	17	6	6	6	6	6	6	6
11	15	7	3	0	7	9	7	7
11	14	3	3	1	2	2	2	5
11	14	6	5	0	6	6	6	8
11	8	6	5	4	4	4	4	6
11	8	4	4	4	4	8	2	4
11	12	7	7	7	7	3	9	9
11	14	7	6	7	7	7	7	7
11	8	7	7	0	4	4	4	4
11	11	4	4	4	4	4	4	6
11	16	5	5	5	5	8	7	8
11	11	6	6	0	6	6	6	6
11	8	5	5	5	5	5	5	5
11	14	6	0	1	6	6	6	6
11	16	6	6	2	2	9	2	6
11	14	6	5	0	6	4	2	4
11	5	3	3	9	9	7	7	7
11	8	3	3	3	3	3	3	9
11	10	3	3	0	4	4	4	8
11	8	6	7	6	6	6	6	6
11	13	7	7	1	5	8	5	6
11	15	5	1	5	5	5	7	5
11	6	5	5	0	4	4	4	7
11	12	5	5	0	2	2	2	5
11	14	6	6	0	6	9	6	8
11	5	6	2	6	6	6	9	6
11	15	6	6	7	7	8	8	8
11	11	5	5	0	5	5	5	5
11	8	4	2	4	4	4	4	4
11	13	7	7	5	5	5	2	5
11	14	5	5	1	5	9	9	6
12	12	3	3	4	4	4	4	4
12	16	6	6	9	9	8	6	6
12	10	2	2	2	2	2	2	9
12	15	8	8	8	8	8	8	7
12	8	3	5	3	3	3	3	3
12	16	0	2	1	6	3	3	6
12	19	6	6	0	6	6	7	6
12	14	8	2	6	6	6	2	6
12	7	4	1	0	5	5	9	5
12	13	5	5	0	5	5	5	5
12	15	6	6	6	6	4	4	5
12	7	5	2	2	2	9	2	9
12	13	6	6	1	6	6	6	8
12	4	2	2	5	5	5	5	5
12	14	6	6	5	5	5	5	6
12	13	5	5	5	5	3	9	7
12	11	5	0	5	5	8	2	5
12	14	6	2	6	6	9	6	6
12	12	4	4	6	6	6	6	6
12	15	6	1	0	9	6	6	6
12	14	5	5	0	5	5	5	6
12	13	5	5	1	5	3	3	9
12	7	4	2	7	7	4	2	7
12	5	2	2	2	2	9	2	9
12	7	7	7	4	4	4	4	4
12	13	5	5	0	6	8	8	8
12	13	6	2	5	5	5	5	5
12	11	5	5	5	5	5	9	8
12	6	3	3	3	3	8	2	9
12	12	6	6	0	6	6	6	6
12	8	4	1	4	4	9	4	4
12	11	5	5	9	9	5	5	7
12	12	7	7	0	8	8	8	8
12	9	4	2	4	4	3	3	9
12	12	6	6	2	2	2	2	9
12	13	8	8	7	7	7	7	7
12	16	7	7	7	7	7	7	8
12	16	6	6	6	6	4	9	4
12	11	7	7	0	5	5	5	6
12	8	4	4	5	5	9	5	7
12	4	0	5	6	6	6	2	6
12	7	3	2	0	3	3	3	7
12	14	5	5	5	5	5	5	5
12	11	6	2	9	9	2	2	9
12	17	5	5	0	7	7	7	7
12	15	7	7	7	7	7	7	7
12	14	6	5	1	6	6	6	6
12	5	8	8	3	3	8	3	6
12	4	7	2	7	7	9	3	9
12	19	8	8	8	8	8	2	9
12	11	3	3	0	3	3	3	8
12	15	8	2	5	5	5	5	8
12	10	3	3	3	3	3	3	3
12	9	4	5	0	4	4	4	6
12	12	2	2	5	5	5	5	5
12	15	7	2	7	7	9	7	7
12	7	6	6	0	6	6	6	6
12	13	2	2	0	7	7	7	7
12	14	7	7	0	9	7	2	7
12	14	6	6	6	6	6	6	6
12	14	6	2	0	6	3	9	8
12	8	6	2	6	6	9	4	9
12	15	6	5	6	6	6	6	6
12	15	6	6	2	2	2	2	9
12	9	4	4	5	5	5	2	5
12	16	5	5	0	5	5	5	6
12	9	7	7	4	4	9	4	4
12	15	6	6	0	7	7	7	7
12	15	6	6	6	6	6	6	6
12	6	5	5	5	5	8	7	8
12	8	8	2	8	8	8	8	8
12	15	6	6	6	6	6	6	9
12	10	0	3	5	5	3	3	8
12	9	4	2	0	4	4	4	4
12	14	8	8	8	8	9	8	6
12	12	6	6	0	6	6	9	6
12	8	4	4	9	9	4	2	7
12	11	6	6	5	5	5	5	9
12	13	2	5	0	6	6	6	8
12	9	4	4	0	4	4	4	4
12	15	6	2	0	6	6	6	6
12	13	3	3	3	3	3	3	9
12	15	6	6	6	6	6	6	6
12	14	5	5	0	5	5	5	5
12	16	4	4	4	4	9	8	8
12	12	6	6	6	6	6	6	6
12	14	1	1	0	5	9	5	6
12	10	4	5	4	4	3	3	6
12	10	4	2	7	7	7	2	7
12	4	6	6	0	6	6	6	7
12	8	5	5	5	5	5	5	9
12	17	9	2	6	6	6	6	6
12	16	6	6	6	6	9	6	6
12	12	8	8	8	8	8	9	6
12	12	7	7	2	2	4	4	4
12	15	7	7	7	7	7	7	7
12	9	0	9	0	4	4	4	8
12	13	6	2	0	6	8	7	7
12	14	6	6	5	5	5	5	9
12	11	5	5	0	2	9	2	6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Maand[t] = + 11.3752266077558 + 0.0144484586575041Schoolprestaties[t] -0.0113967864571243Sport[t] -0.0298161733859221GoingOut[t] + 0.00363707178475142Relation[t] + 0.0378445126296983Family[t] + 0.0022994087612922Friends[t] -0.0248645523957983Coach[t] + 0.0301112591737673Job[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Maand[t] =  +  11.3752266077558 +  0.0144484586575041Schoolprestaties[t] -0.0113967864571243Sport[t] -0.0298161733859221GoingOut[t] +  0.00363707178475142Relation[t] +  0.0378445126296983Family[t] +  0.0022994087612922Friends[t] -0.0248645523957983Coach[t] +  0.0301112591737673Job[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Maand[t] =  +  11.3752266077558 +  0.0144484586575041Schoolprestaties[t] -0.0113967864571243Sport[t] -0.0298161733859221GoingOut[t] +  0.00363707178475142Relation[t] +  0.0378445126296983Family[t] +  0.0022994087612922Friends[t] -0.0248645523957983Coach[t] +  0.0301112591737673Job[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Maand[t] = + 11.3752266077558 + 0.0144484586575041Schoolprestaties[t] -0.0113967864571243Sport[t] -0.0298161733859221GoingOut[t] + 0.00363707178475142Relation[t] + 0.0378445126296983Family[t] + 0.0022994087612922Friends[t] -0.0248645523957983Coach[t] + 0.0301112591737673Job[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	11.3752266077558	0.230388	49.3742	0	0
Schoolprestaties	0.0144484586575041	0.012219	1.1824	0.238943	0.119472
Sport	-0.0113967864571243	0.02682	-0.4249	0.671508	0.335754
GoingOut	-0.0298161733859221	0.021361	-1.3958	0.164875	0.082437
Relation	0.00363707178475142	0.015045	0.2418	0.809308	0.404654
Family	0.0378445126296983	0.028378	1.3336	0.184398	0.092199
Friends	0.0022994087612922	0.020582	0.1117	0.911199	0.455599
Coach	-0.0248645523957983	0.021037	-1.182	0.239127	0.119563
Job	0.0301112591737673	0.024848	1.2118	0.227535	0.113768

\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) & 11.3752266077558 & 0.230388 & 49.3742 & 0 & 0 \tabularnewline
Schoolprestaties & 0.0144484586575041 & 0.012219 & 1.1824 & 0.238943 & 0.119472 \tabularnewline
Sport & -0.0113967864571243 & 0.02682 & -0.4249 & 0.671508 & 0.335754 \tabularnewline
GoingOut & -0.0298161733859221 & 0.021361 & -1.3958 & 0.164875 & 0.082437 \tabularnewline
Relation & 0.00363707178475142 & 0.015045 & 0.2418 & 0.809308 & 0.404654 \tabularnewline
Family & 0.0378445126296983 & 0.028378 & 1.3336 & 0.184398 & 0.092199 \tabularnewline
Friends & 0.0022994087612922 & 0.020582 & 0.1117 & 0.911199 & 0.455599 \tabularnewline
Coach & -0.0248645523957983 & 0.021037 & -1.182 & 0.239127 & 0.119563 \tabularnewline
Job & 0.0301112591737673 & 0.024848 & 1.2118 & 0.227535 & 0.113768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&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]11.3752266077558[/C][C]0.230388[/C][C]49.3742[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Schoolprestaties[/C][C]0.0144484586575041[/C][C]0.012219[/C][C]1.1824[/C][C]0.238943[/C][C]0.119472[/C][/ROW]
[ROW][C]Sport[/C][C]-0.0113967864571243[/C][C]0.02682[/C][C]-0.4249[/C][C]0.671508[/C][C]0.335754[/C][/ROW]
[ROW][C]GoingOut[/C][C]-0.0298161733859221[/C][C]0.021361[/C][C]-1.3958[/C][C]0.164875[/C][C]0.082437[/C][/ROW]
[ROW][C]Relation[/C][C]0.00363707178475142[/C][C]0.015045[/C][C]0.2418[/C][C]0.809308[/C][C]0.404654[/C][/ROW]
[ROW][C]Family[/C][C]0.0378445126296983[/C][C]0.028378[/C][C]1.3336[/C][C]0.184398[/C][C]0.092199[/C][/ROW]
[ROW][C]Friends[/C][C]0.0022994087612922[/C][C]0.020582[/C][C]0.1117[/C][C]0.911199[/C][C]0.455599[/C][/ROW]
[ROW][C]Coach[/C][C]-0.0248645523957983[/C][C]0.021037[/C][C]-1.182[/C][C]0.239127[/C][C]0.119563[/C][/ROW]
[ROW][C]Job[/C][C]0.0301112591737673[/C][C]0.024848[/C][C]1.2118[/C][C]0.227535[/C][C]0.113768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	11.3752266077558	0.230388	49.3742	0	0
Schoolprestaties	0.0144484586575041	0.012219	1.1824	0.238943	0.119472
Sport	-0.0113967864571243	0.02682	-0.4249	0.671508	0.335754
GoingOut	-0.0298161733859221	0.021361	-1.3958	0.164875	0.082437
Relation	0.00363707178475142	0.015045	0.2418	0.809308	0.404654
Family	0.0378445126296983	0.028378	1.3336	0.184398	0.092199
Friends	0.0022994087612922	0.020582	0.1117	0.911199	0.455599
Coach	-0.0248645523957983	0.021037	-1.182	0.239127	0.119563
Job	0.0301112591737673	0.024848	1.2118	0.227535	0.113768

Multiple Linear Regression - Regression Statistics
Multiple R	0.242396673375646
R-squared	0.0587561472635794
Adjusted R-squared	0.00753199201261767
F-TEST (value)	1.1470398482067
F-TEST (DF numerator)	8
F-TEST (DF denominator)	147
p-value	0.335506684638227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.479428804610458
Sum Squared Residuals	33.7882408674613

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.242396673375646 \tabularnewline
R-squared & 0.0587561472635794 \tabularnewline
Adjusted R-squared & 0.00753199201261767 \tabularnewline
F-TEST (value) & 1.1470398482067 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0.335506684638227 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.479428804610458 \tabularnewline
Sum Squared Residuals & 33.7882408674613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.242396673375646[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0587561472635794[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00753199201261767[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.1470398482067[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0.335506684638227[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.479428804610458[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.7882408674613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.242396673375646
R-squared	0.0587561472635794
Adjusted R-squared	0.00753199201261767
F-TEST (value)	1.1470398482067
F-TEST (DF numerator)	8
F-TEST (DF denominator)	147
p-value	0.335506684638227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.479428804610458
Sum Squared Residuals	33.7882408674613

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.6216409906169	-0.621640990616871
2	11	11.6969904635764	-0.696990463576375
3	11	11.7130891146588	-0.71308911465883
4	11	11.5406458988621	-0.540645898862118
5	11	11.5172587337156	-0.517258733715609
6	11	11.7891018592725	-0.789101859272514
7	11	11.6142391413794	-0.614239141379449
8	11	11.5551509399542	-0.555150939954179
9	11	11.5701082353018	-0.57010823530183
10	11	11.7005842422157	-0.700584242215719
11	11	11.6532903869398	-0.653290386939841
12	11	11.686105813481	-0.686105813481022
13	11	11.4849382025013	-0.484938202501271
14	11	11.5802353608596	-0.580235360859618
15	11	11.2858803311803	-0.285880331180268
16	11	11.5500513275704	-0.550051327570409
17	11	11.7544646030253	-0.754464603025274
18	11	11.5302952798317	-0.53029527983172
19	11	11.5022741267071	-0.502274126707138
20	11	11.5967442144635	-0.596744214463518
21	11	11.6543347946442	-0.654334794644174
22	11	11.5824791049439	-0.582479104943901
23	11	11.6159349570931	-0.61593495709315
24	11	11.3015599795857	-0.301559979585743
25	11	11.6099450109673	-0.609945010967329
26	11	11.6677388455973	-0.667738845597345
27	11	11.745060676966	-0.745060676966014
28	11	11.6386182550757	-0.638618255075679
29	11	11.6926097306498	-0.692609730649781
30	11	11.5296860095058	-0.529686009505843
31	11	11.5809999772952	-0.580999977295244
32	11	11.6046070709313	-0.604607070931258
33	11	11.6620243831214	-0.662024383121419
34	11	11.3838860707903	-0.383886070790334
35	11	11.6256411317785	-0.625641131778526
36	11	11.6929785458428	-0.692978545842758
37	11	11.5592256629438	-0.559225662943812
38	11	11.5298879775691	-0.529887977569143
39	11	11.7851051510166	-0.785105151016608
40	11	11.5937204851491	-0.593720485149112
41	11	11.6670240860153	-0.667024086015321
42	11	11.749987090019	-0.749987090019045
43	11	11.6949260523904	-0.694926052390418
44	11	11.6980798641726	-0.698079864172597
45	11	11.5078865442939	-0.507886544293886
46	11	11.5421655494892	-0.542165549489209
47	11	11.7005627769238	-0.700562776923764
48	11	11.5277488506827	-0.52774885068272
49	11	11.5236583462898	-0.523658346289826
50	11	11.6696917835477	-0.669691783547736
51	11	11.5390283780636	-0.539028378063589
52	11	11.6954157437753	-0.695415743775309
53	11	11.5550479946179	-0.555047994617898
54	11	11.5817055842303	-0.581705584230323
55	11	11.594298008358	-0.594298008357966
56	11	11.5418811270109	-0.541881127010905
57	12	11.6210800319315	0.378919968068459
58	12	11.7823339577058	0.217666042294226
59	12	11.7461194887685	0.253880511231471
60	12	11.6243601493299	0.375639850670102
61	12	11.454626150576	0.54537384942403
62	12	11.890445871206	0.109554128793964
63	12	11.649948779808	0.350051220191954
64	12	11.8203227998375	0.179677200162534
65	12	11.5284574304055	0.471542569594499
66	12	11.5839449119329	0.416055088067093
67	12	11.6538609563776	0.346139043622418
68	12	11.6846796147537	0.31532038524631
69	12	11.6519821703911	0.348017829608894
70	12	11.5957330224683	0.404266977531734
71	12	11.6054770288449	0.394522971155111
72	12	11.5582957620984	0.441704237901579
73	12	11.8038061039425	0.196193896057462
74	12	11.7505563894524	0.249443610547602
75	12	11.6779224719959	0.322077528004083
76	12	11.8796339023925	0.120366097607466
77	12	11.6285046297642	0.371495370235822
78	12	11.7531573076817	0.24684269231826
79	12	11.8317647611291	0.168235238870933
80	12	11.6899730568101	0.310026943189946
81	12	11.3839858992718	0.616014100728164
82	12	11.6444277711804	0.355572228819612
83	12	11.6801820045573	0.319817995442694
84	12	11.5641089214798	0.435891078520236
85	12	11.7023907312777	0.297609268722331
86	12	11.5736741216013	0.426325878398684
87	12	11.6230188014227	0.376981198577294
88	12	11.799382209547	0.200617790453011
89	12	11.6232424180962	0.376757581903812
90	12	11.7692754823912	0.23072451760883
91	12	11.6101645667114	0.389835433288648
92	12	11.5765467912349	0.423453208765053
93	12	11.6912163862243	0.308783613775727
94	12	11.5138753938823	0.486124606117673
95	12	11.5027333341056	0.497266665894427
96	12	11.6405210908049	0.359478909195107
97	12	11.6775639847617	0.322436015238348
98	12	11.639160033417	0.360839966582953
99	12	11.6165787295142	0.383421270485832
100	12	12.0053518924987	-0.00535189249868441
101	12	11.7325200029008	0.267479997099158
102	12	11.646656668393	0.353343331606999
103	12	11.636024284087	0.363975715913002
104	12	11.3666791434878	0.633320856512167
105	12	11.8010840355434	0.198915964456621
106	12	11.8915638166822	0.10843618331776
107	12	11.6972489538349	0.302751046165091
108	12	11.7766191264794	0.223380873520632
109	12	11.5431554146628	0.456844585337178
110	12	11.5523797539386	0.44762024606141
111	12	11.7113206917283	0.288679308271701
112	12	11.8003363528452	0.199663647154804
113	12	11.5014318283138	0.498568171686205
114	12	11.7983650478	0.201634952200035
115	12	11.8067604944806	0.193239505519375
116	12	11.6243934696248	0.375606530375168
117	12	11.7005663673363	0.299433632663725
118	12	11.8039285198203	0.196071480179729
119	12	11.6686581016683	0.331341898331741
120	12	11.6535099426839	0.346490057316136
121	12	11.6601430532571	0.339856946742911
122	12	11.6574015470792	0.342598452920814
123	12	11.4243798603933	0.575620139606695
124	12	11.6624101257428	0.337589874257212
125	12	11.6388419282823	0.361158071717663
126	12	11.5484939592677	0.451506040732283
127	12	11.7322292382167	0.26777076178333
128	12	11.7291757058036	0.270824294196361
129	12	11.8108652387319	0.189134761268069
130	12	11.5816057557488	0.418394244251179
131	12	11.5820998402599	0.417900159740081
132	12	11.4990804644139	0.500919535586079
133	12	11.8695440418436	0.130455958156374
134	12	11.6524654303937	0.347534569606321
135	12	11.7237484178208	0.276251582179226
136	12	11.521973408977	0.478026591023023
137	12	11.7362841911175	0.263715808882484
138	12	11.7671683456779	0.232831654322061
139	12	11.6388419282823	0.361158071717663
140	12	11.5983933705904	0.401606629409589
141	12	11.6701447776368	0.329855222363151
142	12	11.5954965523098	0.404503447690176
143	12	11.8025541041815	0.197445895818468
144	12	11.6039416433696	0.396058356630394
145	12	11.8820083633855	0.117991636614544
146	12	11.4881977115151	0.51180228848495
147	12	11.6503330142642	0.349666985735787
148	12	11.7528131797697	0.24718682023034
149	12	11.6601886132237	0.339811386776283
150	12	11.5260389617878	0.47396103821218
151	12	11.3732650237305	0.626734976269543
152	12	11.646656668393	0.353343331606999
153	12	11.5389247245709	0.461075275429067
154	12	11.7172327981031	0.282767201896938
155	12	11.6958108063662	0.304189193633809
156	12	11.5554170081351	0.444582991864865

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 11.6216409906169 & -0.621640990616871 \tabularnewline
2 & 11 & 11.6969904635764 & -0.696990463576375 \tabularnewline
3 & 11 & 11.7130891146588 & -0.71308911465883 \tabularnewline
4 & 11 & 11.5406458988621 & -0.540645898862118 \tabularnewline
5 & 11 & 11.5172587337156 & -0.517258733715609 \tabularnewline
6 & 11 & 11.7891018592725 & -0.789101859272514 \tabularnewline
7 & 11 & 11.6142391413794 & -0.614239141379449 \tabularnewline
8 & 11 & 11.5551509399542 & -0.555150939954179 \tabularnewline
9 & 11 & 11.5701082353018 & -0.57010823530183 \tabularnewline
10 & 11 & 11.7005842422157 & -0.700584242215719 \tabularnewline
11 & 11 & 11.6532903869398 & -0.653290386939841 \tabularnewline
12 & 11 & 11.686105813481 & -0.686105813481022 \tabularnewline
13 & 11 & 11.4849382025013 & -0.484938202501271 \tabularnewline
14 & 11 & 11.5802353608596 & -0.580235360859618 \tabularnewline
15 & 11 & 11.2858803311803 & -0.285880331180268 \tabularnewline
16 & 11 & 11.5500513275704 & -0.550051327570409 \tabularnewline
17 & 11 & 11.7544646030253 & -0.754464603025274 \tabularnewline
18 & 11 & 11.5302952798317 & -0.53029527983172 \tabularnewline
19 & 11 & 11.5022741267071 & -0.502274126707138 \tabularnewline
20 & 11 & 11.5967442144635 & -0.596744214463518 \tabularnewline
21 & 11 & 11.6543347946442 & -0.654334794644174 \tabularnewline
22 & 11 & 11.5824791049439 & -0.582479104943901 \tabularnewline
23 & 11 & 11.6159349570931 & -0.61593495709315 \tabularnewline
24 & 11 & 11.3015599795857 & -0.301559979585743 \tabularnewline
25 & 11 & 11.6099450109673 & -0.609945010967329 \tabularnewline
26 & 11 & 11.6677388455973 & -0.667738845597345 \tabularnewline
27 & 11 & 11.745060676966 & -0.745060676966014 \tabularnewline
28 & 11 & 11.6386182550757 & -0.638618255075679 \tabularnewline
29 & 11 & 11.6926097306498 & -0.692609730649781 \tabularnewline
30 & 11 & 11.5296860095058 & -0.529686009505843 \tabularnewline
31 & 11 & 11.5809999772952 & -0.580999977295244 \tabularnewline
32 & 11 & 11.6046070709313 & -0.604607070931258 \tabularnewline
33 & 11 & 11.6620243831214 & -0.662024383121419 \tabularnewline
34 & 11 & 11.3838860707903 & -0.383886070790334 \tabularnewline
35 & 11 & 11.6256411317785 & -0.625641131778526 \tabularnewline
36 & 11 & 11.6929785458428 & -0.692978545842758 \tabularnewline
37 & 11 & 11.5592256629438 & -0.559225662943812 \tabularnewline
38 & 11 & 11.5298879775691 & -0.529887977569143 \tabularnewline
39 & 11 & 11.7851051510166 & -0.785105151016608 \tabularnewline
40 & 11 & 11.5937204851491 & -0.593720485149112 \tabularnewline
41 & 11 & 11.6670240860153 & -0.667024086015321 \tabularnewline
42 & 11 & 11.749987090019 & -0.749987090019045 \tabularnewline
43 & 11 & 11.6949260523904 & -0.694926052390418 \tabularnewline
44 & 11 & 11.6980798641726 & -0.698079864172597 \tabularnewline
45 & 11 & 11.5078865442939 & -0.507886544293886 \tabularnewline
46 & 11 & 11.5421655494892 & -0.542165549489209 \tabularnewline
47 & 11 & 11.7005627769238 & -0.700562776923764 \tabularnewline
48 & 11 & 11.5277488506827 & -0.52774885068272 \tabularnewline
49 & 11 & 11.5236583462898 & -0.523658346289826 \tabularnewline
50 & 11 & 11.6696917835477 & -0.669691783547736 \tabularnewline
51 & 11 & 11.5390283780636 & -0.539028378063589 \tabularnewline
52 & 11 & 11.6954157437753 & -0.695415743775309 \tabularnewline
53 & 11 & 11.5550479946179 & -0.555047994617898 \tabularnewline
54 & 11 & 11.5817055842303 & -0.581705584230323 \tabularnewline
55 & 11 & 11.594298008358 & -0.594298008357966 \tabularnewline
56 & 11 & 11.5418811270109 & -0.541881127010905 \tabularnewline
57 & 12 & 11.6210800319315 & 0.378919968068459 \tabularnewline
58 & 12 & 11.7823339577058 & 0.217666042294226 \tabularnewline
59 & 12 & 11.7461194887685 & 0.253880511231471 \tabularnewline
60 & 12 & 11.6243601493299 & 0.375639850670102 \tabularnewline
61 & 12 & 11.454626150576 & 0.54537384942403 \tabularnewline
62 & 12 & 11.890445871206 & 0.109554128793964 \tabularnewline
63 & 12 & 11.649948779808 & 0.350051220191954 \tabularnewline
64 & 12 & 11.8203227998375 & 0.179677200162534 \tabularnewline
65 & 12 & 11.5284574304055 & 0.471542569594499 \tabularnewline
66 & 12 & 11.5839449119329 & 0.416055088067093 \tabularnewline
67 & 12 & 11.6538609563776 & 0.346139043622418 \tabularnewline
68 & 12 & 11.6846796147537 & 0.31532038524631 \tabularnewline
69 & 12 & 11.6519821703911 & 0.348017829608894 \tabularnewline
70 & 12 & 11.5957330224683 & 0.404266977531734 \tabularnewline
71 & 12 & 11.6054770288449 & 0.394522971155111 \tabularnewline
72 & 12 & 11.5582957620984 & 0.441704237901579 \tabularnewline
73 & 12 & 11.8038061039425 & 0.196193896057462 \tabularnewline
74 & 12 & 11.7505563894524 & 0.249443610547602 \tabularnewline
75 & 12 & 11.6779224719959 & 0.322077528004083 \tabularnewline
76 & 12 & 11.8796339023925 & 0.120366097607466 \tabularnewline
77 & 12 & 11.6285046297642 & 0.371495370235822 \tabularnewline
78 & 12 & 11.7531573076817 & 0.24684269231826 \tabularnewline
79 & 12 & 11.8317647611291 & 0.168235238870933 \tabularnewline
80 & 12 & 11.6899730568101 & 0.310026943189946 \tabularnewline
81 & 12 & 11.3839858992718 & 0.616014100728164 \tabularnewline
82 & 12 & 11.6444277711804 & 0.355572228819612 \tabularnewline
83 & 12 & 11.6801820045573 & 0.319817995442694 \tabularnewline
84 & 12 & 11.5641089214798 & 0.435891078520236 \tabularnewline
85 & 12 & 11.7023907312777 & 0.297609268722331 \tabularnewline
86 & 12 & 11.5736741216013 & 0.426325878398684 \tabularnewline
87 & 12 & 11.6230188014227 & 0.376981198577294 \tabularnewline
88 & 12 & 11.799382209547 & 0.200617790453011 \tabularnewline
89 & 12 & 11.6232424180962 & 0.376757581903812 \tabularnewline
90 & 12 & 11.7692754823912 & 0.23072451760883 \tabularnewline
91 & 12 & 11.6101645667114 & 0.389835433288648 \tabularnewline
92 & 12 & 11.5765467912349 & 0.423453208765053 \tabularnewline
93 & 12 & 11.6912163862243 & 0.308783613775727 \tabularnewline
94 & 12 & 11.5138753938823 & 0.486124606117673 \tabularnewline
95 & 12 & 11.5027333341056 & 0.497266665894427 \tabularnewline
96 & 12 & 11.6405210908049 & 0.359478909195107 \tabularnewline
97 & 12 & 11.6775639847617 & 0.322436015238348 \tabularnewline
98 & 12 & 11.639160033417 & 0.360839966582953 \tabularnewline
99 & 12 & 11.6165787295142 & 0.383421270485832 \tabularnewline
100 & 12 & 12.0053518924987 & -0.00535189249868441 \tabularnewline
101 & 12 & 11.7325200029008 & 0.267479997099158 \tabularnewline
102 & 12 & 11.646656668393 & 0.353343331606999 \tabularnewline
103 & 12 & 11.636024284087 & 0.363975715913002 \tabularnewline
104 & 12 & 11.3666791434878 & 0.633320856512167 \tabularnewline
105 & 12 & 11.8010840355434 & 0.198915964456621 \tabularnewline
106 & 12 & 11.8915638166822 & 0.10843618331776 \tabularnewline
107 & 12 & 11.6972489538349 & 0.302751046165091 \tabularnewline
108 & 12 & 11.7766191264794 & 0.223380873520632 \tabularnewline
109 & 12 & 11.5431554146628 & 0.456844585337178 \tabularnewline
110 & 12 & 11.5523797539386 & 0.44762024606141 \tabularnewline
111 & 12 & 11.7113206917283 & 0.288679308271701 \tabularnewline
112 & 12 & 11.8003363528452 & 0.199663647154804 \tabularnewline
113 & 12 & 11.5014318283138 & 0.498568171686205 \tabularnewline
114 & 12 & 11.7983650478 & 0.201634952200035 \tabularnewline
115 & 12 & 11.8067604944806 & 0.193239505519375 \tabularnewline
116 & 12 & 11.6243934696248 & 0.375606530375168 \tabularnewline
117 & 12 & 11.7005663673363 & 0.299433632663725 \tabularnewline
118 & 12 & 11.8039285198203 & 0.196071480179729 \tabularnewline
119 & 12 & 11.6686581016683 & 0.331341898331741 \tabularnewline
120 & 12 & 11.6535099426839 & 0.346490057316136 \tabularnewline
121 & 12 & 11.6601430532571 & 0.339856946742911 \tabularnewline
122 & 12 & 11.6574015470792 & 0.342598452920814 \tabularnewline
123 & 12 & 11.4243798603933 & 0.575620139606695 \tabularnewline
124 & 12 & 11.6624101257428 & 0.337589874257212 \tabularnewline
125 & 12 & 11.6388419282823 & 0.361158071717663 \tabularnewline
126 & 12 & 11.5484939592677 & 0.451506040732283 \tabularnewline
127 & 12 & 11.7322292382167 & 0.26777076178333 \tabularnewline
128 & 12 & 11.7291757058036 & 0.270824294196361 \tabularnewline
129 & 12 & 11.8108652387319 & 0.189134761268069 \tabularnewline
130 & 12 & 11.5816057557488 & 0.418394244251179 \tabularnewline
131 & 12 & 11.5820998402599 & 0.417900159740081 \tabularnewline
132 & 12 & 11.4990804644139 & 0.500919535586079 \tabularnewline
133 & 12 & 11.8695440418436 & 0.130455958156374 \tabularnewline
134 & 12 & 11.6524654303937 & 0.347534569606321 \tabularnewline
135 & 12 & 11.7237484178208 & 0.276251582179226 \tabularnewline
136 & 12 & 11.521973408977 & 0.478026591023023 \tabularnewline
137 & 12 & 11.7362841911175 & 0.263715808882484 \tabularnewline
138 & 12 & 11.7671683456779 & 0.232831654322061 \tabularnewline
139 & 12 & 11.6388419282823 & 0.361158071717663 \tabularnewline
140 & 12 & 11.5983933705904 & 0.401606629409589 \tabularnewline
141 & 12 & 11.6701447776368 & 0.329855222363151 \tabularnewline
142 & 12 & 11.5954965523098 & 0.404503447690176 \tabularnewline
143 & 12 & 11.8025541041815 & 0.197445895818468 \tabularnewline
144 & 12 & 11.6039416433696 & 0.396058356630394 \tabularnewline
145 & 12 & 11.8820083633855 & 0.117991636614544 \tabularnewline
146 & 12 & 11.4881977115151 & 0.51180228848495 \tabularnewline
147 & 12 & 11.6503330142642 & 0.349666985735787 \tabularnewline
148 & 12 & 11.7528131797697 & 0.24718682023034 \tabularnewline
149 & 12 & 11.6601886132237 & 0.339811386776283 \tabularnewline
150 & 12 & 11.5260389617878 & 0.47396103821218 \tabularnewline
151 & 12 & 11.3732650237305 & 0.626734976269543 \tabularnewline
152 & 12 & 11.646656668393 & 0.353343331606999 \tabularnewline
153 & 12 & 11.5389247245709 & 0.461075275429067 \tabularnewline
154 & 12 & 11.7172327981031 & 0.282767201896938 \tabularnewline
155 & 12 & 11.6958108063662 & 0.304189193633809 \tabularnewline
156 & 12 & 11.5554170081351 & 0.444582991864865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&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]11[/C][C]11.6216409906169[/C][C]-0.621640990616871[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.6969904635764[/C][C]-0.696990463576375[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]11.7130891146588[/C][C]-0.71308911465883[/C][/ROW]
[ROW][C]4[/C][C]11[/C][C]11.5406458988621[/C][C]-0.540645898862118[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]11.5172587337156[/C][C]-0.517258733715609[/C][/ROW]
[ROW][C]6[/C][C]11[/C][C]11.7891018592725[/C][C]-0.789101859272514[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]11.6142391413794[/C][C]-0.614239141379449[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]11.5551509399542[/C][C]-0.555150939954179[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]11.5701082353018[/C][C]-0.57010823530183[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]11.7005842422157[/C][C]-0.700584242215719[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11.6532903869398[/C][C]-0.653290386939841[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.686105813481[/C][C]-0.686105813481022[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]11.4849382025013[/C][C]-0.484938202501271[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.5802353608596[/C][C]-0.580235360859618[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.2858803311803[/C][C]-0.285880331180268[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]11.5500513275704[/C][C]-0.550051327570409[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]11.7544646030253[/C][C]-0.754464603025274[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]11.5302952798317[/C][C]-0.53029527983172[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]11.5022741267071[/C][C]-0.502274126707138[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]11.5967442144635[/C][C]-0.596744214463518[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6543347946442[/C][C]-0.654334794644174[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.5824791049439[/C][C]-0.582479104943901[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6159349570931[/C][C]-0.61593495709315[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.3015599795857[/C][C]-0.301559979585743[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.6099450109673[/C][C]-0.609945010967329[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]11.6677388455973[/C][C]-0.667738845597345[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.745060676966[/C][C]-0.745060676966014[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]11.6386182550757[/C][C]-0.638618255075679[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]11.6926097306498[/C][C]-0.692609730649781[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]11.5296860095058[/C][C]-0.529686009505843[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]11.5809999772952[/C][C]-0.580999977295244[/C][/ROW]
[ROW][C]32[/C][C]11[/C][C]11.6046070709313[/C][C]-0.604607070931258[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]11.6620243831214[/C][C]-0.662024383121419[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.3838860707903[/C][C]-0.383886070790334[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]11.6256411317785[/C][C]-0.625641131778526[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]11.6929785458428[/C][C]-0.692978545842758[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]11.5592256629438[/C][C]-0.559225662943812[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]11.5298879775691[/C][C]-0.529887977569143[/C][/ROW]
[ROW][C]39[/C][C]11[/C][C]11.7851051510166[/C][C]-0.785105151016608[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.5937204851491[/C][C]-0.593720485149112[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]11.6670240860153[/C][C]-0.667024086015321[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]11.749987090019[/C][C]-0.749987090019045[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]11.6949260523904[/C][C]-0.694926052390418[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]11.6980798641726[/C][C]-0.698079864172597[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.5078865442939[/C][C]-0.507886544293886[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.5421655494892[/C][C]-0.542165549489209[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]11.7005627769238[/C][C]-0.700562776923764[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.5277488506827[/C][C]-0.52774885068272[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.5236583462898[/C][C]-0.523658346289826[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]11.6696917835477[/C][C]-0.669691783547736[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]11.5390283780636[/C][C]-0.539028378063589[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]11.6954157437753[/C][C]-0.695415743775309[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.5550479946179[/C][C]-0.555047994617898[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.5817055842303[/C][C]-0.581705584230323[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]11.594298008358[/C][C]-0.594298008357966[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]11.5418811270109[/C][C]-0.541881127010905[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.6210800319315[/C][C]0.378919968068459[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.7823339577058[/C][C]0.217666042294226[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]11.7461194887685[/C][C]0.253880511231471[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]11.6243601493299[/C][C]0.375639850670102[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.454626150576[/C][C]0.54537384942403[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.890445871206[/C][C]0.109554128793964[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]11.649948779808[/C][C]0.350051220191954[/C][/ROW]
[ROW][C]64[/C][C]12[/C][C]11.8203227998375[/C][C]0.179677200162534[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.5284574304055[/C][C]0.471542569594499[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]11.5839449119329[/C][C]0.416055088067093[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]11.6538609563776[/C][C]0.346139043622418[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]11.6846796147537[/C][C]0.31532038524631[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]11.6519821703911[/C][C]0.348017829608894[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.5957330224683[/C][C]0.404266977531734[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.6054770288449[/C][C]0.394522971155111[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]11.5582957620984[/C][C]0.441704237901579[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]11.8038061039425[/C][C]0.196193896057462[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]11.7505563894524[/C][C]0.249443610547602[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.6779224719959[/C][C]0.322077528004083[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.8796339023925[/C][C]0.120366097607466[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.6285046297642[/C][C]0.371495370235822[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.7531573076817[/C][C]0.24684269231826[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.8317647611291[/C][C]0.168235238870933[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.6899730568101[/C][C]0.310026943189946[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.3839858992718[/C][C]0.616014100728164[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.6444277711804[/C][C]0.355572228819612[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.6801820045573[/C][C]0.319817995442694[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.5641089214798[/C][C]0.435891078520236[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]11.7023907312777[/C][C]0.297609268722331[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.5736741216013[/C][C]0.426325878398684[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.6230188014227[/C][C]0.376981198577294[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.799382209547[/C][C]0.200617790453011[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.6232424180962[/C][C]0.376757581903812[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.7692754823912[/C][C]0.23072451760883[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.6101645667114[/C][C]0.389835433288648[/C][/ROW]
[ROW][C]92[/C][C]12[/C][C]11.5765467912349[/C][C]0.423453208765053[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.6912163862243[/C][C]0.308783613775727[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.5138753938823[/C][C]0.486124606117673[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.5027333341056[/C][C]0.497266665894427[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.6405210908049[/C][C]0.359478909195107[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.6775639847617[/C][C]0.322436015238348[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]11.639160033417[/C][C]0.360839966582953[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.6165787295142[/C][C]0.383421270485832[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]12.0053518924987[/C][C]-0.00535189249868441[/C][/ROW]
[ROW][C]101[/C][C]12[/C][C]11.7325200029008[/C][C]0.267479997099158[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.646656668393[/C][C]0.353343331606999[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]11.636024284087[/C][C]0.363975715913002[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]11.3666791434878[/C][C]0.633320856512167[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]11.8010840355434[/C][C]0.198915964456621[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]11.8915638166822[/C][C]0.10843618331776[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.6972489538349[/C][C]0.302751046165091[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.7766191264794[/C][C]0.223380873520632[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.5431554146628[/C][C]0.456844585337178[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]11.5523797539386[/C][C]0.44762024606141[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.7113206917283[/C][C]0.288679308271701[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.8003363528452[/C][C]0.199663647154804[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]11.5014318283138[/C][C]0.498568171686205[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]11.7983650478[/C][C]0.201634952200035[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.8067604944806[/C][C]0.193239505519375[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.6243934696248[/C][C]0.375606530375168[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]11.7005663673363[/C][C]0.299433632663725[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]11.8039285198203[/C][C]0.196071480179729[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]11.6686581016683[/C][C]0.331341898331741[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.6535099426839[/C][C]0.346490057316136[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.6601430532571[/C][C]0.339856946742911[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]11.6574015470792[/C][C]0.342598452920814[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]11.4243798603933[/C][C]0.575620139606695[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]11.6624101257428[/C][C]0.337589874257212[/C][/ROW]
[ROW][C]125[/C][C]12[/C][C]11.6388419282823[/C][C]0.361158071717663[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.5484939592677[/C][C]0.451506040732283[/C][/ROW]
[ROW][C]127[/C][C]12[/C][C]11.7322292382167[/C][C]0.26777076178333[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.7291757058036[/C][C]0.270824294196361[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]11.8108652387319[/C][C]0.189134761268069[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]11.5816057557488[/C][C]0.418394244251179[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]11.5820998402599[/C][C]0.417900159740081[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.4990804644139[/C][C]0.500919535586079[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]11.8695440418436[/C][C]0.130455958156374[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]11.6524654303937[/C][C]0.347534569606321[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.7237484178208[/C][C]0.276251582179226[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.521973408977[/C][C]0.478026591023023[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.7362841911175[/C][C]0.263715808882484[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]11.7671683456779[/C][C]0.232831654322061[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]11.6388419282823[/C][C]0.361158071717663[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.5983933705904[/C][C]0.401606629409589[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.6701447776368[/C][C]0.329855222363151[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.5954965523098[/C][C]0.404503447690176[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.8025541041815[/C][C]0.197445895818468[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.6039416433696[/C][C]0.396058356630394[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.8820083633855[/C][C]0.117991636614544[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.4881977115151[/C][C]0.51180228848495[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.6503330142642[/C][C]0.349666985735787[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]11.7528131797697[/C][C]0.24718682023034[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.6601886132237[/C][C]0.339811386776283[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.5260389617878[/C][C]0.47396103821218[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.3732650237305[/C][C]0.626734976269543[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]11.646656668393[/C][C]0.353343331606999[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.5389247245709[/C][C]0.461075275429067[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]11.7172327981031[/C][C]0.282767201896938[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]11.6958108063662[/C][C]0.304189193633809[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]11.5554170081351[/C][C]0.444582991864865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.6216409906169	-0.621640990616871
2	11	11.6969904635764	-0.696990463576375
3	11	11.7130891146588	-0.71308911465883
4	11	11.5406458988621	-0.540645898862118
5	11	11.5172587337156	-0.517258733715609
6	11	11.7891018592725	-0.789101859272514
7	11	11.6142391413794	-0.614239141379449
8	11	11.5551509399542	-0.555150939954179
9	11	11.5701082353018	-0.57010823530183
10	11	11.7005842422157	-0.700584242215719
11	11	11.6532903869398	-0.653290386939841
12	11	11.686105813481	-0.686105813481022
13	11	11.4849382025013	-0.484938202501271
14	11	11.5802353608596	-0.580235360859618
15	11	11.2858803311803	-0.285880331180268
16	11	11.5500513275704	-0.550051327570409
17	11	11.7544646030253	-0.754464603025274
18	11	11.5302952798317	-0.53029527983172
19	11	11.5022741267071	-0.502274126707138
20	11	11.5967442144635	-0.596744214463518
21	11	11.6543347946442	-0.654334794644174
22	11	11.5824791049439	-0.582479104943901
23	11	11.6159349570931	-0.61593495709315
24	11	11.3015599795857	-0.301559979585743
25	11	11.6099450109673	-0.609945010967329
26	11	11.6677388455973	-0.667738845597345
27	11	11.745060676966	-0.745060676966014
28	11	11.6386182550757	-0.638618255075679
29	11	11.6926097306498	-0.692609730649781
30	11	11.5296860095058	-0.529686009505843
31	11	11.5809999772952	-0.580999977295244
32	11	11.6046070709313	-0.604607070931258
33	11	11.6620243831214	-0.662024383121419
34	11	11.3838860707903	-0.383886070790334
35	11	11.6256411317785	-0.625641131778526
36	11	11.6929785458428	-0.692978545842758
37	11	11.5592256629438	-0.559225662943812
38	11	11.5298879775691	-0.529887977569143
39	11	11.7851051510166	-0.785105151016608
40	11	11.5937204851491	-0.593720485149112
41	11	11.6670240860153	-0.667024086015321
42	11	11.749987090019	-0.749987090019045
43	11	11.6949260523904	-0.694926052390418
44	11	11.6980798641726	-0.698079864172597
45	11	11.5078865442939	-0.507886544293886
46	11	11.5421655494892	-0.542165549489209
47	11	11.7005627769238	-0.700562776923764
48	11	11.5277488506827	-0.52774885068272
49	11	11.5236583462898	-0.523658346289826
50	11	11.6696917835477	-0.669691783547736
51	11	11.5390283780636	-0.539028378063589
52	11	11.6954157437753	-0.695415743775309
53	11	11.5550479946179	-0.555047994617898
54	11	11.5817055842303	-0.581705584230323
55	11	11.594298008358	-0.594298008357966
56	11	11.5418811270109	-0.541881127010905
57	12	11.6210800319315	0.378919968068459
58	12	11.7823339577058	0.217666042294226
59	12	11.7461194887685	0.253880511231471
60	12	11.6243601493299	0.375639850670102
61	12	11.454626150576	0.54537384942403
62	12	11.890445871206	0.109554128793964
63	12	11.649948779808	0.350051220191954
64	12	11.8203227998375	0.179677200162534
65	12	11.5284574304055	0.471542569594499
66	12	11.5839449119329	0.416055088067093
67	12	11.6538609563776	0.346139043622418
68	12	11.6846796147537	0.31532038524631
69	12	11.6519821703911	0.348017829608894
70	12	11.5957330224683	0.404266977531734
71	12	11.6054770288449	0.394522971155111
72	12	11.5582957620984	0.441704237901579
73	12	11.8038061039425	0.196193896057462
74	12	11.7505563894524	0.249443610547602
75	12	11.6779224719959	0.322077528004083
76	12	11.8796339023925	0.120366097607466
77	12	11.6285046297642	0.371495370235822
78	12	11.7531573076817	0.24684269231826
79	12	11.8317647611291	0.168235238870933
80	12	11.6899730568101	0.310026943189946
81	12	11.3839858992718	0.616014100728164
82	12	11.6444277711804	0.355572228819612
83	12	11.6801820045573	0.319817995442694
84	12	11.5641089214798	0.435891078520236
85	12	11.7023907312777	0.297609268722331
86	12	11.5736741216013	0.426325878398684
87	12	11.6230188014227	0.376981198577294
88	12	11.799382209547	0.200617790453011
89	12	11.6232424180962	0.376757581903812
90	12	11.7692754823912	0.23072451760883
91	12	11.6101645667114	0.389835433288648
92	12	11.5765467912349	0.423453208765053
93	12	11.6912163862243	0.308783613775727
94	12	11.5138753938823	0.486124606117673
95	12	11.5027333341056	0.497266665894427
96	12	11.6405210908049	0.359478909195107
97	12	11.6775639847617	0.322436015238348
98	12	11.639160033417	0.360839966582953
99	12	11.6165787295142	0.383421270485832
100	12	12.0053518924987	-0.00535189249868441
101	12	11.7325200029008	0.267479997099158
102	12	11.646656668393	0.353343331606999
103	12	11.636024284087	0.363975715913002
104	12	11.3666791434878	0.633320856512167
105	12	11.8010840355434	0.198915964456621
106	12	11.8915638166822	0.10843618331776
107	12	11.6972489538349	0.302751046165091
108	12	11.7766191264794	0.223380873520632
109	12	11.5431554146628	0.456844585337178
110	12	11.5523797539386	0.44762024606141
111	12	11.7113206917283	0.288679308271701
112	12	11.8003363528452	0.199663647154804
113	12	11.5014318283138	0.498568171686205
114	12	11.7983650478	0.201634952200035
115	12	11.8067604944806	0.193239505519375
116	12	11.6243934696248	0.375606530375168
117	12	11.7005663673363	0.299433632663725
118	12	11.8039285198203	0.196071480179729
119	12	11.6686581016683	0.331341898331741
120	12	11.6535099426839	0.346490057316136
121	12	11.6601430532571	0.339856946742911
122	12	11.6574015470792	0.342598452920814
123	12	11.4243798603933	0.575620139606695
124	12	11.6624101257428	0.337589874257212
125	12	11.6388419282823	0.361158071717663
126	12	11.5484939592677	0.451506040732283
127	12	11.7322292382167	0.26777076178333
128	12	11.7291757058036	0.270824294196361
129	12	11.8108652387319	0.189134761268069
130	12	11.5816057557488	0.418394244251179
131	12	11.5820998402599	0.417900159740081
132	12	11.4990804644139	0.500919535586079
133	12	11.8695440418436	0.130455958156374
134	12	11.6524654303937	0.347534569606321
135	12	11.7237484178208	0.276251582179226
136	12	11.521973408977	0.478026591023023
137	12	11.7362841911175	0.263715808882484
138	12	11.7671683456779	0.232831654322061
139	12	11.6388419282823	0.361158071717663
140	12	11.5983933705904	0.401606629409589
141	12	11.6701447776368	0.329855222363151
142	12	11.5954965523098	0.404503447690176
143	12	11.8025541041815	0.197445895818468
144	12	11.6039416433696	0.396058356630394
145	12	11.8820083633855	0.117991636614544
146	12	11.4881977115151	0.51180228848495
147	12	11.6503330142642	0.349666985735787
148	12	11.7528131797697	0.24718682023034
149	12	11.6601886132237	0.339811386776283
150	12	11.5260389617878	0.47396103821218
151	12	11.3732650237305	0.626734976269543
152	12	11.646656668393	0.353343331606999
153	12	11.5389247245709	0.461075275429067
154	12	11.7172327981031	0.282767201896938
155	12	11.6958108063662	0.304189193633809
156	12	11.5554170081351	0.444582991864865

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	1.48928703722835e-44	2.97857407445671e-44	1
13	1.05179216207328e-62	2.10358432414657e-62	1
14	4.7883826995958e-72	9.57676539919159e-72	1
15	2.02415758330557e-86	4.04831516661114e-86	1
16	0	0	1
17	4.26725007845233e-116	8.53450015690466e-116	1
18	8.62665224698528e-144	1.72533044939706e-143	1
19	6.32665132153327e-147	1.26533026430665e-146	1
20	8.34701842377719e-167	1.66940368475544e-166	1
21	8.65254325606669e-183	1.73050865121334e-182	1
22	3.67874634613034e-187	7.35749269226067e-187	1
23	5.30604299137937e-207	1.06120859827587e-206	1
24	1.1067279250178e-230	2.21345585003559e-230	1
25	3.14921565100337e-227	6.29843130200673e-227	1
26	2.44615497785553e-246	4.89230995571105e-246	1
27	2.57934074446996e-263	5.15868148893992e-263	1
28	5.25394843441852e-271	1.0507896868837e-270	1
29	6.73830964279213e-297	1.34766192855843e-296	1
30	5.09555034717875e-300	1.01911006943575e-299	1
31	4.81086541324997e-319	9.62173082649994e-319	1
32	0	0	1
33	0	0	1
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	0	0	1
39	0	0	1
40	0	0	1
41	0	0	1
42	0	0	1
43	0	0	1
44	0	0	1
45	0	0	1
46	0	0	1
47	0	0	1
48	0	0	1
49	0	0	1
50	0	0	1
51	0	0	1
52	0	0	1
53	0	0	1
54	0	0	1
55	0	0	1
56	1	8.18662974575767e-16	4.09331487287884e-16
57	1	0	0
58	1	0	0
59	1	0	0
60	1	0	0
61	1	0	0
62	1	0	0
63	1	0	0
64	1	0	0
65	1	0	0
66	1	0	0
67	1	0	0
68	1	0	0
69	1	0	0
70	1	0	0
71	1	0	0
72	1	0	0
73	1	0	0
74	1	0	0
75	1	0	0
76	1	0	0
77	1	0	0
78	1	0	0
79	1	0	0
80	1	0	0
81	1	0	0
82	1	0	0
83	1	0	0
84	1	0	0
85	1	0	0
86	1	0	0
87	1	0	0
88	1	0	0
89	1	0	0
90	1	0	0
91	1	0	0
92	1	0	0
93	1	0	0
94	1	0	0
95	1	0	0
96	1	0	0
97	1	0	0
98	1	0	0
99	1	0	0
100	1	0	0
101	1	0	0
102	1	0	0
103	1	0	0
104	1	0	0
105	1	0	0
106	1	0	0
107	1	0	0
108	1	0	0
109	1	0	0
110	1	0	0
111	1	0	0
112	1	0	0
113	1	0	0
114	1	0	0
115	1	0	0
116	1	0	0
117	1	0	0
118	1	0	0
119	1	0	0
120	1	0	0
121	1	0	0
122	1	0	0
123	1	0	0
124	1	0	0
125	1	4.8356176278036e-315	2.4178088139018e-315
126	1	1.94121890990191e-303	9.70609454950957e-304
127	1	1.78633429683948e-286	8.93167148419742e-287
128	1	1.15975330917604e-273	5.79876654588021e-274
129	1	1.62102466285694e-263	8.10512331428471e-264
130	1	4.85601407599612e-274	2.42800703799806e-274
131	1	3.1831571265729e-227	1.59157856328645e-227
132	1	4.11511447321153e-219	2.05755723660576e-219
133	1	7.58819086383129e-204	3.79409543191565e-204
134	1	9.50572188569446e-200	4.75286094284723e-200
135	1	6.75543085508079e-176	3.3777154275404e-176
136	1	3.20395222563093e-166	1.60197611281546e-166
137	1	1.81178034900209e-147	9.05890174501047e-148
138	1	1.85427033545015e-134	9.27135167725076e-135
139	1	1.13712511753525e-116	5.68562558767623e-117
140	1	0	0
141	1	1.45328568522856e-87	7.26642842614278e-88
142	1	2.10989107329196e-74	1.05494553664598e-74
143	1	1.35534772468937e-59	6.77673862344683e-60
144	1	1.38222665680177e-46	6.91113328400886e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 1.48928703722835e-44 & 2.97857407445671e-44 & 1 \tabularnewline
13 & 1.05179216207328e-62 & 2.10358432414657e-62 & 1 \tabularnewline
14 & 4.7883826995958e-72 & 9.57676539919159e-72 & 1 \tabularnewline
15 & 2.02415758330557e-86 & 4.04831516661114e-86 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 4.26725007845233e-116 & 8.53450015690466e-116 & 1 \tabularnewline
18 & 8.62665224698528e-144 & 1.72533044939706e-143 & 1 \tabularnewline
19 & 6.32665132153327e-147 & 1.26533026430665e-146 & 1 \tabularnewline
20 & 8.34701842377719e-167 & 1.66940368475544e-166 & 1 \tabularnewline
21 & 8.65254325606669e-183 & 1.73050865121334e-182 & 1 \tabularnewline
22 & 3.67874634613034e-187 & 7.35749269226067e-187 & 1 \tabularnewline
23 & 5.30604299137937e-207 & 1.06120859827587e-206 & 1 \tabularnewline
24 & 1.1067279250178e-230 & 2.21345585003559e-230 & 1 \tabularnewline
25 & 3.14921565100337e-227 & 6.29843130200673e-227 & 1 \tabularnewline
26 & 2.44615497785553e-246 & 4.89230995571105e-246 & 1 \tabularnewline
27 & 2.57934074446996e-263 & 5.15868148893992e-263 & 1 \tabularnewline
28 & 5.25394843441852e-271 & 1.0507896868837e-270 & 1 \tabularnewline
29 & 6.73830964279213e-297 & 1.34766192855843e-296 & 1 \tabularnewline
30 & 5.09555034717875e-300 & 1.01911006943575e-299 & 1 \tabularnewline
31 & 4.81086541324997e-319 & 9.62173082649994e-319 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 1 & 8.18662974575767e-16 & 4.09331487287884e-16 \tabularnewline
57 & 1 & 0 & 0 \tabularnewline
58 & 1 & 0 & 0 \tabularnewline
59 & 1 & 0 & 0 \tabularnewline
60 & 1 & 0 & 0 \tabularnewline
61 & 1 & 0 & 0 \tabularnewline
62 & 1 & 0 & 0 \tabularnewline
63 & 1 & 0 & 0 \tabularnewline
64 & 1 & 0 & 0 \tabularnewline
65 & 1 & 0 & 0 \tabularnewline
66 & 1 & 0 & 0 \tabularnewline
67 & 1 & 0 & 0 \tabularnewline
68 & 1 & 0 & 0 \tabularnewline
69 & 1 & 0 & 0 \tabularnewline
70 & 1 & 0 & 0 \tabularnewline
71 & 1 & 0 & 0 \tabularnewline
72 & 1 & 0 & 0 \tabularnewline
73 & 1 & 0 & 0 \tabularnewline
74 & 1 & 0 & 0 \tabularnewline
75 & 1 & 0 & 0 \tabularnewline
76 & 1 & 0 & 0 \tabularnewline
77 & 1 & 0 & 0 \tabularnewline
78 & 1 & 0 & 0 \tabularnewline
79 & 1 & 0 & 0 \tabularnewline
80 & 1 & 0 & 0 \tabularnewline
81 & 1 & 0 & 0 \tabularnewline
82 & 1 & 0 & 0 \tabularnewline
83 & 1 & 0 & 0 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 4.8356176278036e-315 & 2.4178088139018e-315 \tabularnewline
126 & 1 & 1.94121890990191e-303 & 9.70609454950957e-304 \tabularnewline
127 & 1 & 1.78633429683948e-286 & 8.93167148419742e-287 \tabularnewline
128 & 1 & 1.15975330917604e-273 & 5.79876654588021e-274 \tabularnewline
129 & 1 & 1.62102466285694e-263 & 8.10512331428471e-264 \tabularnewline
130 & 1 & 4.85601407599612e-274 & 2.42800703799806e-274 \tabularnewline
131 & 1 & 3.1831571265729e-227 & 1.59157856328645e-227 \tabularnewline
132 & 1 & 4.11511447321153e-219 & 2.05755723660576e-219 \tabularnewline
133 & 1 & 7.58819086383129e-204 & 3.79409543191565e-204 \tabularnewline
134 & 1 & 9.50572188569446e-200 & 4.75286094284723e-200 \tabularnewline
135 & 1 & 6.75543085508079e-176 & 3.3777154275404e-176 \tabularnewline
136 & 1 & 3.20395222563093e-166 & 1.60197611281546e-166 \tabularnewline
137 & 1 & 1.81178034900209e-147 & 9.05890174501047e-148 \tabularnewline
138 & 1 & 1.85427033545015e-134 & 9.27135167725076e-135 \tabularnewline
139 & 1 & 1.13712511753525e-116 & 5.68562558767623e-117 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 1.45328568522856e-87 & 7.26642842614278e-88 \tabularnewline
142 & 1 & 2.10989107329196e-74 & 1.05494553664598e-74 \tabularnewline
143 & 1 & 1.35534772468937e-59 & 6.77673862344683e-60 \tabularnewline
144 & 1 & 1.38222665680177e-46 & 6.91113328400886e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]1.48928703722835e-44[/C][C]2.97857407445671e-44[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.05179216207328e-62[/C][C]2.10358432414657e-62[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]4.7883826995958e-72[/C][C]9.57676539919159e-72[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]2.02415758330557e-86[/C][C]4.04831516661114e-86[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]4.26725007845233e-116[/C][C]8.53450015690466e-116[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]8.62665224698528e-144[/C][C]1.72533044939706e-143[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]6.32665132153327e-147[/C][C]1.26533026430665e-146[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]8.34701842377719e-167[/C][C]1.66940368475544e-166[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]8.65254325606669e-183[/C][C]1.73050865121334e-182[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]3.67874634613034e-187[/C][C]7.35749269226067e-187[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]5.30604299137937e-207[/C][C]1.06120859827587e-206[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]1.1067279250178e-230[/C][C]2.21345585003559e-230[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]3.14921565100337e-227[/C][C]6.29843130200673e-227[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]2.44615497785553e-246[/C][C]4.89230995571105e-246[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]2.57934074446996e-263[/C][C]5.15868148893992e-263[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.25394843441852e-271[/C][C]1.0507896868837e-270[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]6.73830964279213e-297[/C][C]1.34766192855843e-296[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]5.09555034717875e-300[/C][C]1.01911006943575e-299[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]4.81086541324997e-319[/C][C]9.62173082649994e-319[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]8.18662974575767e-16[/C][C]4.09331487287884e-16[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]4.8356176278036e-315[/C][C]2.4178088139018e-315[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.94121890990191e-303[/C][C]9.70609454950957e-304[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.78633429683948e-286[/C][C]8.93167148419742e-287[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.15975330917604e-273[/C][C]5.79876654588021e-274[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.62102466285694e-263[/C][C]8.10512331428471e-264[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]4.85601407599612e-274[/C][C]2.42800703799806e-274[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]3.1831571265729e-227[/C][C]1.59157856328645e-227[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.11511447321153e-219[/C][C]2.05755723660576e-219[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]7.58819086383129e-204[/C][C]3.79409543191565e-204[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]9.50572188569446e-200[/C][C]4.75286094284723e-200[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]6.75543085508079e-176[/C][C]3.3777154275404e-176[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]3.20395222563093e-166[/C][C]1.60197611281546e-166[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.81178034900209e-147[/C][C]9.05890174501047e-148[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.85427033545015e-134[/C][C]9.27135167725076e-135[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.13712511753525e-116[/C][C]5.68562558767623e-117[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.45328568522856e-87[/C][C]7.26642842614278e-88[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]2.10989107329196e-74[/C][C]1.05494553664598e-74[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.35534772468937e-59[/C][C]6.77673862344683e-60[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.38222665680177e-46[/C][C]6.91113328400886e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	1.48928703722835e-44	2.97857407445671e-44	1
13	1.05179216207328e-62	2.10358432414657e-62	1
14	4.7883826995958e-72	9.57676539919159e-72	1
15	2.02415758330557e-86	4.04831516661114e-86	1
16	0	0	1
17	4.26725007845233e-116	8.53450015690466e-116	1
18	8.62665224698528e-144	1.72533044939706e-143	1
19	6.32665132153327e-147	1.26533026430665e-146	1
20	8.34701842377719e-167	1.66940368475544e-166	1
21	8.65254325606669e-183	1.73050865121334e-182	1
22	3.67874634613034e-187	7.35749269226067e-187	1
23	5.30604299137937e-207	1.06120859827587e-206	1
24	1.1067279250178e-230	2.21345585003559e-230	1
25	3.14921565100337e-227	6.29843130200673e-227	1
26	2.44615497785553e-246	4.89230995571105e-246	1
27	2.57934074446996e-263	5.15868148893992e-263	1
28	5.25394843441852e-271	1.0507896868837e-270	1
29	6.73830964279213e-297	1.34766192855843e-296	1
30	5.09555034717875e-300	1.01911006943575e-299	1
31	4.81086541324997e-319	9.62173082649994e-319	1
32	0	0	1
33	0	0	1
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	0	0	1
39	0	0	1
40	0	0	1
41	0	0	1
42	0	0	1
43	0	0	1
44	0	0	1
45	0	0	1
46	0	0	1
47	0	0	1
48	0	0	1
49	0	0	1
50	0	0	1
51	0	0	1
52	0	0	1
53	0	0	1
54	0	0	1
55	0	0	1
56	1	8.18662974575767e-16	4.09331487287884e-16
57	1	0	0
58	1	0	0
59	1	0	0
60	1	0	0
61	1	0	0
62	1	0	0
63	1	0	0
64	1	0	0
65	1	0	0
66	1	0	0
67	1	0	0
68	1	0	0
69	1	0	0
70	1	0	0
71	1	0	0
72	1	0	0
73	1	0	0
74	1	0	0
75	1	0	0
76	1	0	0
77	1	0	0
78	1	0	0
79	1	0	0
80	1	0	0
81	1	0	0
82	1	0	0
83	1	0	0
84	1	0	0
85	1	0	0
86	1	0	0
87	1	0	0
88	1	0	0
89	1	0	0
90	1	0	0
91	1	0	0
92	1	0	0
93	1	0	0
94	1	0	0
95	1	0	0
96	1	0	0
97	1	0	0
98	1	0	0
99	1	0	0
100	1	0	0
101	1	0	0
102	1	0	0
103	1	0	0
104	1	0	0
105	1	0	0
106	1	0	0
107	1	0	0
108	1	0	0
109	1	0	0
110	1	0	0
111	1	0	0
112	1	0	0
113	1	0	0
114	1	0	0
115	1	0	0
116	1	0	0
117	1	0	0
118	1	0	0
119	1	0	0
120	1	0	0
121	1	0	0
122	1	0	0
123	1	0	0
124	1	0	0
125	1	4.8356176278036e-315	2.4178088139018e-315
126	1	1.94121890990191e-303	9.70609454950957e-304
127	1	1.78633429683948e-286	8.93167148419742e-287
128	1	1.15975330917604e-273	5.79876654588021e-274
129	1	1.62102466285694e-263	8.10512331428471e-264
130	1	4.85601407599612e-274	2.42800703799806e-274
131	1	3.1831571265729e-227	1.59157856328645e-227
132	1	4.11511447321153e-219	2.05755723660576e-219
133	1	7.58819086383129e-204	3.79409543191565e-204
134	1	9.50572188569446e-200	4.75286094284723e-200
135	1	6.75543085508079e-176	3.3777154275404e-176
136	1	3.20395222563093e-166	1.60197611281546e-166
137	1	1.81178034900209e-147	9.05890174501047e-148
138	1	1.85427033545015e-134	9.27135167725076e-135
139	1	1.13712511753525e-116	5.68562558767623e-117
140	1	0	0
141	1	1.45328568522856e-87	7.26642842614278e-88
142	1	2.10989107329196e-74	1.05494553664598e-74
143	1	1.35534772468937e-59	6.77673862344683e-60
144	1	1.38222665680177e-46	6.91113328400886e-47

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	133	1	NOK
5% type I error level	133	1	NOK
10% type I error level	133	1	NOK

\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 & 133 & 1 & NOK \tabularnewline
5% type I error level & 133 & 1 & NOK \tabularnewline
10% type I error level & 133 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146322&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]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146322&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146322&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	133	1	NOK
5% type I error level	133	1	NOK
10% type I error level	133	1	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code