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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 08 Dec 2014 11:51:34 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/08/t1418039524a4ohni6ac5t3mny.htm/, Retrieved Sun, 19 May 2024 11:15:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263947, Retrieved Sun, 19 May 2024 11:15:52 +0000

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Estimated Impact

118

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

-       [Multiple Regression] [paper mr numeracy] [2014-12-08 11:51:34] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]
- R  D    [Multiple Regression] [paper mr numeracy] [2014-12-08 11:55:23] [673773038936aef3a5778d7e6bda5c1e] 
- R  D    [Multiple Regression] [paper mr numeracy] [2014-12-08 11:57:14] [673773038936aef3a5778d7e6bda5c1e] 
- RMPD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [paper motivation ...] [2014-12-08 12:19:20] [673773038936aef3a5778d7e6bda5c1e] 

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Dataseries X:

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1 21 1.00 0.50 0.67 0.67 0.00 0.50
0 22 0.89 0.50 0.83 0.33 0.50 1.00
1 22 0.89 0.40 1.00 0.67 0.00 1.00
0 18 0.89 0.50 0.83 0.00 0.00 0.00
0 23 0.89 0.70 0.67 0.00 1.00 1.00
0 12 0.78 0.30 0.00 0.00 0.50 0.50
1 20 0.89 0.40 0.83 0.67 0.50 0.00
0 22 1.00 0.40 0.50 0.67 1.00 1.00
0 21 0.89 0.70 0.83 0.00 0.50 0.00
0 19 0.78 0.60 0.33 0.67 0.50 0.50
0 22 1.00 0.60 0.50 1.00 0.00 0.50
0 15 0.78 0.20 0.67 0.00 0.50 0.50
0 20 0.89 0.40 1.00 0.00 0.50 0.50
1 19 0.89 0.40 0.50 0.67 0.00 1.00
1 18 0.89 0.50 0.67 0.33 0.00 0.00
1 15 0.89 0.30 0.17 0.67 0.00 0.50
0 20 0.89 0.40 0.83 0.33 0.50 0.50
1 21 0.67 0.70 0.67 0.33 0.50 1.00
0 21 1.00 0.50 0.67 0.33 0.00 1.00
1 15 0.78 0.20 0.67 0.00 0.00 1.00
0 16 0.78 0.30 0.50 0.67 0.00 0.50
0 23 0.89 0.60 1.00 0.33 0.00 1.00
1 21 0.78 0.60 0.83 0.33 0.00 1.00
0 18 0.89 0.20 0.83 0.33 0.00 1.00
0 25 0.89 0.70 1.00 0.67 1.00 0.00
0 9 0.33 0.20 0.67 0.00 0.00 0.00
0 30 1.00 1.00 1.00 0.33 1.00 1.00
1 20 0.89 0.40 0.83 0.67 0.00 0.50
0 23 0.89 0.40 1.00 1.00 0.00 1.00
1 16 0.67 0.20 0.83 0.67 0.00 0.50
1 16 0.56 0.40 0.67 0.33 0.00 1.00
1 19 0.89 0.40 0.67 0.00 0.50 1.00
0 25 0.89 0.70 1.00 0.67 0.50 0.50
0 18 1.00 0.20 0.67 0.67 0.00 0.50
0 23 0.78 0.60 1.00 1.00 0.00 0.50
0 21 0.78 0.30 1.00 1.00 0.50 0.50
1 10 0.33 0.30 0.50 0.33 0.00 0.00
0 14 0.78 0.20 0.67 0.00 0.50 0.00
0 22 0.89 0.50 0.83 0.67 0.50 0.50
1 26 0.89 0.70 1.00 0.67 0.50 1.00
0 23 0.78 0.60 1.00 0.67 0.50 0.50
0 23 0.89 0.40 1.00 0.67 0.50 1.00
0 24 0.89 0.60 1.00 0.33 0.50 1.00
0 24 1.00 0.40 1.00 1.00 0.00 1.00
0 18 0.67 0.30 0.83 0.67 0.00 1.00
1 23 1.00 0.50 0.83 0.67 0.50 0.50
0 15 0.89 0.20 0.50 0.00 0.00 1.00
0 19 0.89 0.30 0.83 0.00 0.50 1.00
1 16 0.89 0.50 0.17 0.00 0.00 1.00
0 25 0.78 0.70 0.83 1.00 0.50 1.00
0 23 0.89 0.40 1.00 0.67 1.00 0.50
0 17 0.78 0.30 1.00 0.00 0.00 0.50
0 19 0.78 0.20 0.67 0.67 1.00 1.00
0 21 1.00 0.50 1.00 0.00 0.00 0.50
0 18 0.78 0.40 1.00 0.00 0.50 0.00
0 27 1.00 0.60 1.00 0.67 1.00 1.00
1 21 0.78 0.40 0.83 1.00 0.00 1.00
0 13 0.67 0.40 0.33 0.00 0.00 0.50
1 8 0.33 0.20 0.33 0.33 0.00 0.00
0 29 1.00 0.90 1.00 0.67 0.50 1.00
0 28 1.00 0.80 1.00 0.67 1.00 0.50
1 23 0.78 0.80 0.83 0.00 0.50 1.00
1 21 0.67 0.30 1.00 1.00 0.50 1.00
0 19 1.00 0.20 0.83 0.67 0.00 0.50
1 19 0.89 0.40 0.67 0.00 0.50 1.00
0 20 0.89 0.20 0.83 1.00 0.00 1.00
1 18 0.78 0.20 0.67 0.67 0.50 1.00
0 19 1.00 0.10 0.83 0.67 0.00 1.00
0 17 0.56 0.40 0.67 1.00 0.50 0.00
1 19 0.67 0.50 1.00 0.00 0.50 0.50
1 25 0.89 0.80 0.83 0.33 0.50 1.00
1 19 0.89 0.40 0.67 0.67 0.00 0.50
1 22 0.89 0.60 0.83 0.33 0.50 0.50
0 23 0.89 0.50 0.83 0.67 0.50 1.00
1 14 0.78 0.30 0.67 0.00 0.00 0.00
0 28 0.89 0.80 1.00 1.00 0.50 1.00
1 16 1.00 0.40 0.33 0.00 0.50 0.00
0 24 1.00 0.60 0.83 0.67 0.50 0.50
1 20 0.89 0.40 1.00 0.33 0.00 0.50
1 12 0.44 0.30 0.83 0.00 0.00 0.00
0 24 0.78 0.80 0.83 0.00 1.00 1.00
1 22 0.89 0.60 0.50 0.33 1.00 1.00
1 12 0.67 0.30 0.50 0.00 0.00 0.00
1 22 0.78 0.50 0.83 0.67 0.50 1.00
0 20 0.78 0.40 1.00 0.33 0.00 1.00
1 10 0.33 0.30 0.33 0.67 0.00 0.00
0 23 0.89 0.70 1.00 0.33 0.00 0.50
0 17 0.89 0.20 0.67 0.33 0.50 0.50
1 22 0.89 0.40 0.83 1.00 0.00 1.00
1 24 0.89 0.60 1.00 0.67 0.50 0.50
1 18 0.56 0.60 0.83 0.00 0.00 1.00
0 21 0.67 0.60 0.83 0.67 0.50 0.50
0 20 0.67 0.40 1.00 0.33 0.50 1.00
0 20 0.78 0.60 0.83 0.00 0.00 1.00
1 22 0.78 0.50 1.00 0.33 0.50 1.00
0 19 0.78 0.50 0.83 0.00 0.00 1.00
1 20 0.89 0.60 0.67 0.00 0.00 1.00
0 26 1.00 0.80 0.83 0.33 0.50 1.00
0 23 0.89 0.50 0.83 0.67 1.00 0.50
0 24 0.89 0.60 0.83 0.67 0.50 1.00
0 21 0.78 0.40 0.83 0.67 0.50 1.00
0 21 1.00 0.30 0.67 0.67 0.50 1.00
1 19 0.78 0.30 0.83 1.00 0.00 0.50
0 8 0.67 0.20 0.00 0.00 0.00 0.00
0 17 0.78 0.40 0.83 0.00 0.00 0.50
0 20 0.89 0.50 1.00 0.00 0.00 0.50
1 11 0.67 0.30 0.17 0.00 0.50 0.00
1 8 0.22 0.40 0.17 0.00 0.50 0.00
1 15 0.44 0.50 0.50 1.00 0.00 0.00
1 18 0.89 0.30 0.50 0.67 0.00 1.00
1 18 0.67 0.50 1.00 0.00 0.00 0.50
1 19 0.89 0.40 0.67 0.67 0.00 0.50
0 19 0.67 0.40 0.83 0.67 0.00 1.00

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
gendercode[t] = + 1.16871 -1.94641NUMERACYTOT_op_32[t] + 16.8055Calculation[t] + 19.8695Algebraic_Reasoning[t] + 11.2617Graphical_Interpretation[t] + 5.88502Proportionality_and_Ratio[t] + 3.5512Probability_and_Sampling[t] + 3.92878Estimation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
gendercode[t] =  +  1.16871 -1.94641NUMERACYTOT_op_32[t] +  16.8055Calculation[t] +  19.8695Algebraic_Reasoning[t] +  11.2617Graphical_Interpretation[t] +  5.88502Proportionality_and_Ratio[t] +  3.5512Probability_and_Sampling[t] +  3.92878Estimation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]gendercode[t] =  +  1.16871 -1.94641NUMERACYTOT_op_32[t] +  16.8055Calculation[t] +  19.8695Algebraic_Reasoning[t] +  11.2617Graphical_Interpretation[t] +  5.88502Proportionality_and_Ratio[t] +  3.5512Probability_and_Sampling[t] +  3.92878Estimation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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
gendercode[t] = + 1.16871 -1.94641NUMERACYTOT_op_32[t] + 16.8055Calculation[t] + 19.8695Algebraic_Reasoning[t] + 11.2617Graphical_Interpretation[t] + 5.88502Proportionality_and_Ratio[t] + 3.5512Probability_and_Sampling[t] + 3.92878Estimation[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)	1.16871	0.245027	4.77	5.97524e-06	2.98762e-06
NUMERACYTOT_op_32	-1.94641	2.0863	-0.9329	0.352987	0.176493
Calculation	16.8055	18.7413	0.8967	0.371924	0.185962
Algebraic_Reasoning	19.8695	20.9267	0.9495	0.344557	0.172278
Graphical_Interpretation	11.2617	12.5379	0.8982	0.371127	0.185564
Proportionality_and_Ratio	5.88502	6.26451	0.9394	0.34967	0.174835
Probability_and_Sampling	3.5512	4.15237	0.8552	0.394376	0.197188
Estimation	3.92878	4.15779	0.9449	0.346869	0.173435

\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) & 1.16871 & 0.245027 & 4.77 & 5.97524e-06 & 2.98762e-06 \tabularnewline
NUMERACYTOT_op_32 & -1.94641 & 2.0863 & -0.9329 & 0.352987 & 0.176493 \tabularnewline
Calculation & 16.8055 & 18.7413 & 0.8967 & 0.371924 & 0.185962 \tabularnewline
Algebraic_Reasoning & 19.8695 & 20.9267 & 0.9495 & 0.344557 & 0.172278 \tabularnewline
Graphical_Interpretation & 11.2617 & 12.5379 & 0.8982 & 0.371127 & 0.185564 \tabularnewline
Proportionality_and_Ratio & 5.88502 & 6.26451 & 0.9394 & 0.34967 & 0.174835 \tabularnewline
Probability_and_Sampling & 3.5512 & 4.15237 & 0.8552 & 0.394376 & 0.197188 \tabularnewline
Estimation & 3.92878 & 4.15779 & 0.9449 & 0.346869 & 0.173435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&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]1.16871[/C][C]0.245027[/C][C]4.77[/C][C]5.97524e-06[/C][C]2.98762e-06[/C][/ROW]
[ROW][C]NUMERACYTOT_op_32[/C][C]-1.94641[/C][C]2.0863[/C][C]-0.9329[/C][C]0.352987[/C][C]0.176493[/C][/ROW]
[ROW][C]Calculation[/C][C]16.8055[/C][C]18.7413[/C][C]0.8967[/C][C]0.371924[/C][C]0.185962[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]19.8695[/C][C]20.9267[/C][C]0.9495[/C][C]0.344557[/C][C]0.172278[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]11.2617[/C][C]12.5379[/C][C]0.8982[/C][C]0.371127[/C][C]0.185564[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]5.88502[/C][C]6.26451[/C][C]0.9394[/C][C]0.34967[/C][C]0.174835[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]3.5512[/C][C]4.15237[/C][C]0.8552[/C][C]0.394376[/C][C]0.197188[/C][/ROW]
[ROW][C]Estimation[/C][C]3.92878[/C][C]4.15779[/C][C]0.9449[/C][C]0.346869[/C][C]0.173435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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)	1.16871	0.245027	4.77	5.97524e-06	2.98762e-06
NUMERACYTOT_op_32	-1.94641	2.0863	-0.9329	0.352987	0.176493
Calculation	16.8055	18.7413	0.8967	0.371924	0.185962
Algebraic_Reasoning	19.8695	20.9267	0.9495	0.344557	0.172278
Graphical_Interpretation	11.2617	12.5379	0.8982	0.371127	0.185564
Proportionality_and_Ratio	5.88502	6.26451	0.9394	0.34967	0.174835
Probability_and_Sampling	3.5512	4.15237	0.8552	0.394376	0.197188
Estimation	3.92878	4.15779	0.9449	0.346869	0.173435

Multiple Linear Regression - Regression Statistics
Multiple R	0.394161
R-squared	0.155363
Adjusted R-squared	0.0990537
F-TEST (value)	2.75911
F-TEST (DF numerator)	7
F-TEST (DF denominator)	105
p-value	0.0113442
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.469918
Sum Squared Residuals	23.1864

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.394161 \tabularnewline
R-squared & 0.155363 \tabularnewline
Adjusted R-squared & 0.0990537 \tabularnewline
F-TEST (value) & 2.75911 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0.0113442 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.469918 \tabularnewline
Sum Squared Residuals & 23.1864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.394161[/C][/ROW]
[ROW][C]R-squared[/C][C]0.155363[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0990537[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.75911[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]105[/C][/ROW]
[ROW][C]p-value[/C][C]0.0113442[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.469918[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23.1864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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	0.394161
R-squared	0.155363
Adjusted R-squared	0.0990537
F-TEST (value)	2.75911
F-TEST (DF numerator)	7
F-TEST (DF denominator)	105
p-value	0.0113442
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.469918
Sum Squared Residuals	23.1864

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1	0.487033	0.512967
2	0	0.232968	-0.232968
3	1	0.385814	0.614186
4	0	0.372172	-0.372172
5	0	0.292127	-0.292127
6	0	0.620927	-0.620927
7	1	0.210964	0.789036
8	0	0.154778	-0.154778
9	0	0.282434	-0.282434
10	0	0.616214	-0.616214
11	0	0.55514	-0.55514
12	0	0.340074	-0.340074
13	0	0.146879	-0.146879
14	1	0.594205	0.405795
15	1	0.512359	0.487641
16	1	0.712152	0.287848
17	0	0.174449	-0.174449
18	1	0.65419	0.34581
19	0	0.450518	-0.450518
20	1	0.528867	0.471133
21	0	0.633489	-0.633489
22	0	0.412392	-0.412392
23	1	0.54212	0.45788
24	0	0.282168	-0.282168
25	0	0.129839	-0.129839
26	0	0.716062	-0.716062
27	0	0.135115	-0.135115
28	1	0.399757	0.600243
29	0	0.381459	-0.381459
30	1	0.514289	0.485711
31	1	0.801194	0.198806
32	1	0.341327	0.658673
33	0	0.318632	-0.318632
34	0	0.365421	-0.365421
35	0	0.542356	-0.542356
36	0	0.249932	-0.249932
37	1	0.78417	0.21583
38	0	0.322093	-0.322093
39	0	0.269482	-0.269482
40	1	0.336614	0.663386
41	0	0.375898	-0.375898
42	0	0.215002	-0.215002
43	0	0.241581	-0.241581
44	0	0.283656	-0.283656
45	0	0.572807	-0.572807
46	1	0.171679	0.828321
47	0	0.462989	-0.462989
48	0	0.156248	-0.156248
49	1	0.761067	0.238933
50	0	0.461987	-0.461987
51	0	0.0262087	-0.0262087
52	0	0.374957	-0.374957
53	0	0.237385	-0.237385
54	0	0.260425	-0.260425
55	0	0.226701	-0.226701
56	0	0.0274613	-0.0274613
57	1	0.511187	0.488813
58	0	0.753613	-0.753613
59	1	0.775557	0.224443
60	0	0.319885	-0.319885
61	0	0.0905546	-0.0905546
62	1	0.456737	0.543263
63	1	0.365717	0.634283
64	0	0.220879	-0.220879
65	1	0.341327	0.658673
66	0	0.332309	-0.332309
67	1	0.408197	0.591803
68	0	0.198323	-0.198323
69	0	0.644561	-0.644561
70	1	0.383023	0.616977
71	1	0.35458	0.64542
72	1	0.544299	0.455701
73	1	0.255524	0.744476
74	0	0.287464	-0.287464
75	1	0.533442	0.466558
76	0	0.372796	-0.372796
77	1	0.27141	0.72859
78	0	0.212217	-0.212217
79	1	0.313337	0.686663
80	1	0.514255	0.485745
81	0	0.285926	-0.285926
82	1	0.27916	0.72084
83	1	0.66317	0.33683
84	1	0.385267	0.614733
85	0	0.429121	-0.429121
86	1	0.87059	0.12941
87	0	0.434948	-0.434948
88	0	0.237916	-0.237916
89	1	0.413384	0.586616
90	1	0.278095	0.721905
91	1	0.742081	0.257919
92	0	0.505626	-0.505626
93	0	0.356112	-0.356112
94	0	0.546475	-0.546475
95	1	0.298846	0.701154
96	0	0.505937	-0.505937
97	1	0.593213	0.406787
98	0	0.256777	-0.256777
99	0	0.0986707	-0.0986707
100	0	0.328001	-0.328001
101	0	0.34473	-0.34473
102	0	0.253128	-0.253128
103	1	0.452669	0.547331
104	0	0.831023	-0.831023
105	0	0.447419	-0.447419
106	0	0.358228	-0.358228
107	1	0.668824	0.331176
108	1	0.932519	0.067481
109	1	0.817583	0.182417
110	1	0.553667	0.446333
111	1	0.553835	0.446165
112	1	0.544299	0.455701
113	0	0.613345	-0.613345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.487033 & 0.512967 \tabularnewline
2 & 0 & 0.232968 & -0.232968 \tabularnewline
3 & 1 & 0.385814 & 0.614186 \tabularnewline
4 & 0 & 0.372172 & -0.372172 \tabularnewline
5 & 0 & 0.292127 & -0.292127 \tabularnewline
6 & 0 & 0.620927 & -0.620927 \tabularnewline
7 & 1 & 0.210964 & 0.789036 \tabularnewline
8 & 0 & 0.154778 & -0.154778 \tabularnewline
9 & 0 & 0.282434 & -0.282434 \tabularnewline
10 & 0 & 0.616214 & -0.616214 \tabularnewline
11 & 0 & 0.55514 & -0.55514 \tabularnewline
12 & 0 & 0.340074 & -0.340074 \tabularnewline
13 & 0 & 0.146879 & -0.146879 \tabularnewline
14 & 1 & 0.594205 & 0.405795 \tabularnewline
15 & 1 & 0.512359 & 0.487641 \tabularnewline
16 & 1 & 0.712152 & 0.287848 \tabularnewline
17 & 0 & 0.174449 & -0.174449 \tabularnewline
18 & 1 & 0.65419 & 0.34581 \tabularnewline
19 & 0 & 0.450518 & -0.450518 \tabularnewline
20 & 1 & 0.528867 & 0.471133 \tabularnewline
21 & 0 & 0.633489 & -0.633489 \tabularnewline
22 & 0 & 0.412392 & -0.412392 \tabularnewline
23 & 1 & 0.54212 & 0.45788 \tabularnewline
24 & 0 & 0.282168 & -0.282168 \tabularnewline
25 & 0 & 0.129839 & -0.129839 \tabularnewline
26 & 0 & 0.716062 & -0.716062 \tabularnewline
27 & 0 & 0.135115 & -0.135115 \tabularnewline
28 & 1 & 0.399757 & 0.600243 \tabularnewline
29 & 0 & 0.381459 & -0.381459 \tabularnewline
30 & 1 & 0.514289 & 0.485711 \tabularnewline
31 & 1 & 0.801194 & 0.198806 \tabularnewline
32 & 1 & 0.341327 & 0.658673 \tabularnewline
33 & 0 & 0.318632 & -0.318632 \tabularnewline
34 & 0 & 0.365421 & -0.365421 \tabularnewline
35 & 0 & 0.542356 & -0.542356 \tabularnewline
36 & 0 & 0.249932 & -0.249932 \tabularnewline
37 & 1 & 0.78417 & 0.21583 \tabularnewline
38 & 0 & 0.322093 & -0.322093 \tabularnewline
39 & 0 & 0.269482 & -0.269482 \tabularnewline
40 & 1 & 0.336614 & 0.663386 \tabularnewline
41 & 0 & 0.375898 & -0.375898 \tabularnewline
42 & 0 & 0.215002 & -0.215002 \tabularnewline
43 & 0 & 0.241581 & -0.241581 \tabularnewline
44 & 0 & 0.283656 & -0.283656 \tabularnewline
45 & 0 & 0.572807 & -0.572807 \tabularnewline
46 & 1 & 0.171679 & 0.828321 \tabularnewline
47 & 0 & 0.462989 & -0.462989 \tabularnewline
48 & 0 & 0.156248 & -0.156248 \tabularnewline
49 & 1 & 0.761067 & 0.238933 \tabularnewline
50 & 0 & 0.461987 & -0.461987 \tabularnewline
51 & 0 & 0.0262087 & -0.0262087 \tabularnewline
52 & 0 & 0.374957 & -0.374957 \tabularnewline
53 & 0 & 0.237385 & -0.237385 \tabularnewline
54 & 0 & 0.260425 & -0.260425 \tabularnewline
55 & 0 & 0.226701 & -0.226701 \tabularnewline
56 & 0 & 0.0274613 & -0.0274613 \tabularnewline
57 & 1 & 0.511187 & 0.488813 \tabularnewline
58 & 0 & 0.753613 & -0.753613 \tabularnewline
59 & 1 & 0.775557 & 0.224443 \tabularnewline
60 & 0 & 0.319885 & -0.319885 \tabularnewline
61 & 0 & 0.0905546 & -0.0905546 \tabularnewline
62 & 1 & 0.456737 & 0.543263 \tabularnewline
63 & 1 & 0.365717 & 0.634283 \tabularnewline
64 & 0 & 0.220879 & -0.220879 \tabularnewline
65 & 1 & 0.341327 & 0.658673 \tabularnewline
66 & 0 & 0.332309 & -0.332309 \tabularnewline
67 & 1 & 0.408197 & 0.591803 \tabularnewline
68 & 0 & 0.198323 & -0.198323 \tabularnewline
69 & 0 & 0.644561 & -0.644561 \tabularnewline
70 & 1 & 0.383023 & 0.616977 \tabularnewline
71 & 1 & 0.35458 & 0.64542 \tabularnewline
72 & 1 & 0.544299 & 0.455701 \tabularnewline
73 & 1 & 0.255524 & 0.744476 \tabularnewline
74 & 0 & 0.287464 & -0.287464 \tabularnewline
75 & 1 & 0.533442 & 0.466558 \tabularnewline
76 & 0 & 0.372796 & -0.372796 \tabularnewline
77 & 1 & 0.27141 & 0.72859 \tabularnewline
78 & 0 & 0.212217 & -0.212217 \tabularnewline
79 & 1 & 0.313337 & 0.686663 \tabularnewline
80 & 1 & 0.514255 & 0.485745 \tabularnewline
81 & 0 & 0.285926 & -0.285926 \tabularnewline
82 & 1 & 0.27916 & 0.72084 \tabularnewline
83 & 1 & 0.66317 & 0.33683 \tabularnewline
84 & 1 & 0.385267 & 0.614733 \tabularnewline
85 & 0 & 0.429121 & -0.429121 \tabularnewline
86 & 1 & 0.87059 & 0.12941 \tabularnewline
87 & 0 & 0.434948 & -0.434948 \tabularnewline
88 & 0 & 0.237916 & -0.237916 \tabularnewline
89 & 1 & 0.413384 & 0.586616 \tabularnewline
90 & 1 & 0.278095 & 0.721905 \tabularnewline
91 & 1 & 0.742081 & 0.257919 \tabularnewline
92 & 0 & 0.505626 & -0.505626 \tabularnewline
93 & 0 & 0.356112 & -0.356112 \tabularnewline
94 & 0 & 0.546475 & -0.546475 \tabularnewline
95 & 1 & 0.298846 & 0.701154 \tabularnewline
96 & 0 & 0.505937 & -0.505937 \tabularnewline
97 & 1 & 0.593213 & 0.406787 \tabularnewline
98 & 0 & 0.256777 & -0.256777 \tabularnewline
99 & 0 & 0.0986707 & -0.0986707 \tabularnewline
100 & 0 & 0.328001 & -0.328001 \tabularnewline
101 & 0 & 0.34473 & -0.34473 \tabularnewline
102 & 0 & 0.253128 & -0.253128 \tabularnewline
103 & 1 & 0.452669 & 0.547331 \tabularnewline
104 & 0 & 0.831023 & -0.831023 \tabularnewline
105 & 0 & 0.447419 & -0.447419 \tabularnewline
106 & 0 & 0.358228 & -0.358228 \tabularnewline
107 & 1 & 0.668824 & 0.331176 \tabularnewline
108 & 1 & 0.932519 & 0.067481 \tabularnewline
109 & 1 & 0.817583 & 0.182417 \tabularnewline
110 & 1 & 0.553667 & 0.446333 \tabularnewline
111 & 1 & 0.553835 & 0.446165 \tabularnewline
112 & 1 & 0.544299 & 0.455701 \tabularnewline
113 & 0 & 0.613345 & -0.613345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&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]1[/C][C]0.487033[/C][C]0.512967[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.232968[/C][C]-0.232968[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.385814[/C][C]0.614186[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.372172[/C][C]-0.372172[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.292127[/C][C]-0.292127[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.620927[/C][C]-0.620927[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.210964[/C][C]0.789036[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.154778[/C][C]-0.154778[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.282434[/C][C]-0.282434[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.616214[/C][C]-0.616214[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.55514[/C][C]-0.55514[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.340074[/C][C]-0.340074[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.146879[/C][C]-0.146879[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.594205[/C][C]0.405795[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.512359[/C][C]0.487641[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.712152[/C][C]0.287848[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.174449[/C][C]-0.174449[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.65419[/C][C]0.34581[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.450518[/C][C]-0.450518[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.528867[/C][C]0.471133[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.633489[/C][C]-0.633489[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.412392[/C][C]-0.412392[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.54212[/C][C]0.45788[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.282168[/C][C]-0.282168[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.129839[/C][C]-0.129839[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.716062[/C][C]-0.716062[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.135115[/C][C]-0.135115[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.399757[/C][C]0.600243[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.381459[/C][C]-0.381459[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.514289[/C][C]0.485711[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.801194[/C][C]0.198806[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.341327[/C][C]0.658673[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.318632[/C][C]-0.318632[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.365421[/C][C]-0.365421[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.542356[/C][C]-0.542356[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.249932[/C][C]-0.249932[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.78417[/C][C]0.21583[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.322093[/C][C]-0.322093[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.269482[/C][C]-0.269482[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.336614[/C][C]0.663386[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.375898[/C][C]-0.375898[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.215002[/C][C]-0.215002[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.241581[/C][C]-0.241581[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.283656[/C][C]-0.283656[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.572807[/C][C]-0.572807[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.171679[/C][C]0.828321[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.462989[/C][C]-0.462989[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.156248[/C][C]-0.156248[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.761067[/C][C]0.238933[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.461987[/C][C]-0.461987[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.0262087[/C][C]-0.0262087[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.374957[/C][C]-0.374957[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.237385[/C][C]-0.237385[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.260425[/C][C]-0.260425[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.226701[/C][C]-0.226701[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.0274613[/C][C]-0.0274613[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.511187[/C][C]0.488813[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.753613[/C][C]-0.753613[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.775557[/C][C]0.224443[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.319885[/C][C]-0.319885[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0905546[/C][C]-0.0905546[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.456737[/C][C]0.543263[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.365717[/C][C]0.634283[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.220879[/C][C]-0.220879[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.341327[/C][C]0.658673[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.332309[/C][C]-0.332309[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.408197[/C][C]0.591803[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.198323[/C][C]-0.198323[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.644561[/C][C]-0.644561[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.383023[/C][C]0.616977[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.35458[/C][C]0.64542[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.544299[/C][C]0.455701[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.255524[/C][C]0.744476[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.287464[/C][C]-0.287464[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.533442[/C][C]0.466558[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.372796[/C][C]-0.372796[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.27141[/C][C]0.72859[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.212217[/C][C]-0.212217[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.313337[/C][C]0.686663[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.514255[/C][C]0.485745[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.285926[/C][C]-0.285926[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.27916[/C][C]0.72084[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.66317[/C][C]0.33683[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.385267[/C][C]0.614733[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.429121[/C][C]-0.429121[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.87059[/C][C]0.12941[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.434948[/C][C]-0.434948[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.237916[/C][C]-0.237916[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.413384[/C][C]0.586616[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.278095[/C][C]0.721905[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.742081[/C][C]0.257919[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.505626[/C][C]-0.505626[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.356112[/C][C]-0.356112[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.546475[/C][C]-0.546475[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.298846[/C][C]0.701154[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.505937[/C][C]-0.505937[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.593213[/C][C]0.406787[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.256777[/C][C]-0.256777[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0986707[/C][C]-0.0986707[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.328001[/C][C]-0.328001[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.34473[/C][C]-0.34473[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.253128[/C][C]-0.253128[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0.452669[/C][C]0.547331[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.831023[/C][C]-0.831023[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.447419[/C][C]-0.447419[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.358228[/C][C]-0.358228[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0.668824[/C][C]0.331176[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0.932519[/C][C]0.067481[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0.817583[/C][C]0.182417[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.553667[/C][C]0.446333[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.553835[/C][C]0.446165[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0.544299[/C][C]0.455701[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.613345[/C][C]-0.613345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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	1	0.487033	0.512967
2	0	0.232968	-0.232968
3	1	0.385814	0.614186
4	0	0.372172	-0.372172
5	0	0.292127	-0.292127
6	0	0.620927	-0.620927
7	1	0.210964	0.789036
8	0	0.154778	-0.154778
9	0	0.282434	-0.282434
10	0	0.616214	-0.616214
11	0	0.55514	-0.55514
12	0	0.340074	-0.340074
13	0	0.146879	-0.146879
14	1	0.594205	0.405795
15	1	0.512359	0.487641
16	1	0.712152	0.287848
17	0	0.174449	-0.174449
18	1	0.65419	0.34581
19	0	0.450518	-0.450518
20	1	0.528867	0.471133
21	0	0.633489	-0.633489
22	0	0.412392	-0.412392
23	1	0.54212	0.45788
24	0	0.282168	-0.282168
25	0	0.129839	-0.129839
26	0	0.716062	-0.716062
27	0	0.135115	-0.135115
28	1	0.399757	0.600243
29	0	0.381459	-0.381459
30	1	0.514289	0.485711
31	1	0.801194	0.198806
32	1	0.341327	0.658673
33	0	0.318632	-0.318632
34	0	0.365421	-0.365421
35	0	0.542356	-0.542356
36	0	0.249932	-0.249932
37	1	0.78417	0.21583
38	0	0.322093	-0.322093
39	0	0.269482	-0.269482
40	1	0.336614	0.663386
41	0	0.375898	-0.375898
42	0	0.215002	-0.215002
43	0	0.241581	-0.241581
44	0	0.283656	-0.283656
45	0	0.572807	-0.572807
46	1	0.171679	0.828321
47	0	0.462989	-0.462989
48	0	0.156248	-0.156248
49	1	0.761067	0.238933
50	0	0.461987	-0.461987
51	0	0.0262087	-0.0262087
52	0	0.374957	-0.374957
53	0	0.237385	-0.237385
54	0	0.260425	-0.260425
55	0	0.226701	-0.226701
56	0	0.0274613	-0.0274613
57	1	0.511187	0.488813
58	0	0.753613	-0.753613
59	1	0.775557	0.224443
60	0	0.319885	-0.319885
61	0	0.0905546	-0.0905546
62	1	0.456737	0.543263
63	1	0.365717	0.634283
64	0	0.220879	-0.220879
65	1	0.341327	0.658673
66	0	0.332309	-0.332309
67	1	0.408197	0.591803
68	0	0.198323	-0.198323
69	0	0.644561	-0.644561
70	1	0.383023	0.616977
71	1	0.35458	0.64542
72	1	0.544299	0.455701
73	1	0.255524	0.744476
74	0	0.287464	-0.287464
75	1	0.533442	0.466558
76	0	0.372796	-0.372796
77	1	0.27141	0.72859
78	0	0.212217	-0.212217
79	1	0.313337	0.686663
80	1	0.514255	0.485745
81	0	0.285926	-0.285926
82	1	0.27916	0.72084
83	1	0.66317	0.33683
84	1	0.385267	0.614733
85	0	0.429121	-0.429121
86	1	0.87059	0.12941
87	0	0.434948	-0.434948
88	0	0.237916	-0.237916
89	1	0.413384	0.586616
90	1	0.278095	0.721905
91	1	0.742081	0.257919
92	0	0.505626	-0.505626
93	0	0.356112	-0.356112
94	0	0.546475	-0.546475
95	1	0.298846	0.701154
96	0	0.505937	-0.505937
97	1	0.593213	0.406787
98	0	0.256777	-0.256777
99	0	0.0986707	-0.0986707
100	0	0.328001	-0.328001
101	0	0.34473	-0.34473
102	0	0.253128	-0.253128
103	1	0.452669	0.547331
104	0	0.831023	-0.831023
105	0	0.447419	-0.447419
106	0	0.358228	-0.358228
107	1	0.668824	0.331176
108	1	0.932519	0.067481
109	1	0.817583	0.182417
110	1	0.553667	0.446333
111	1	0.553835	0.446165
112	1	0.544299	0.455701
113	0	0.613345	-0.613345

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.172229	0.344457	0.827771
12	0.568783	0.862433	0.431217
13	0.48119	0.96238	0.51881
14	0.491529	0.983058	0.508471
15	0.460366	0.920733	0.539634
16	0.366411	0.732821	0.633589
17	0.27057	0.541139	0.72943
18	0.225488	0.450976	0.774512
19	0.177151	0.354301	0.822849
20	0.146344	0.292689	0.853656
21	0.411425	0.822849	0.588575
22	0.388021	0.776041	0.611979
23	0.47973	0.959461	0.52027
24	0.409128	0.818255	0.590872
25	0.388308	0.776617	0.611692
26	0.383011	0.766021	0.616989
27	0.317792	0.635585	0.682208
28	0.33029	0.66058	0.66971
29	0.427569	0.855138	0.572431
30	0.405349	0.810697	0.594651
31	0.344618	0.689237	0.655382
32	0.398007	0.796013	0.601993
33	0.400017	0.800034	0.599983
34	0.436915	0.873829	0.563085
35	0.485529	0.971059	0.514471
36	0.438528	0.877056	0.561472
37	0.486117	0.972234	0.513883
38	0.452593	0.905187	0.547407
39	0.404476	0.808952	0.595524
40	0.451271	0.902542	0.548729
41	0.433465	0.866929	0.566535
42	0.388118	0.776236	0.611882
43	0.344014	0.688028	0.655986
44	0.309916	0.619831	0.690084
45	0.333045	0.66609	0.666955
46	0.474115	0.948229	0.525885
47	0.465923	0.931847	0.534077
48	0.414835	0.82967	0.585165
49	0.372224	0.744449	0.627776
50	0.354686	0.709371	0.645314
51	0.305779	0.611559	0.694221
52	0.290527	0.581054	0.709473
53	0.257138	0.514277	0.742862
54	0.228189	0.456379	0.771811
55	0.202674	0.405349	0.797326
56	0.167824	0.335647	0.832176
57	0.1781	0.356199	0.8219
58	0.22307	0.446139	0.77693
59	0.209797	0.419595	0.790203
60	0.184688	0.369376	0.815312
61	0.154937	0.309874	0.845063
62	0.169737	0.339475	0.830263
63	0.196349	0.392697	0.803651
64	0.171977	0.343953	0.828023
65	0.202765	0.40553	0.797235
66	0.183331	0.366662	0.816669
67	0.208278	0.416557	0.791722
68	0.180575	0.36115	0.819425
69	0.220795	0.441589	0.779205
70	0.240459	0.480919	0.759541
71	0.283683	0.567365	0.716317
72	0.269638	0.539275	0.730362
73	0.325705	0.651409	0.674295
74	0.292601	0.585201	0.707399
75	0.274324	0.548647	0.725676
76	0.254844	0.509689	0.745156
77	0.311096	0.622191	0.688904
78	0.276822	0.553644	0.723178
79	0.319942	0.639885	0.680058
80	0.395454	0.790908	0.604546
81	0.352419	0.704838	0.647581
82	0.455728	0.911456	0.544272
83	0.430292	0.860583	0.569708
84	0.454299	0.908598	0.545701
85	0.423655	0.847309	0.576345
86	0.369065	0.738131	0.630935
87	0.381538	0.763076	0.618462
88	0.316064	0.632128	0.683936
89	0.349335	0.698669	0.650665
90	0.35022	0.700441	0.64978
91	0.301584	0.603168	0.698416
92	0.473223	0.946447	0.526777
93	0.421713	0.843427	0.578287
94	0.378724	0.757447	0.621276
95	0.55598	0.88804	0.44402
96	0.467741	0.935482	0.532259
97	0.445124	0.890248	0.554876
98	0.385986	0.771971	0.614014
99	0.287998	0.575996	0.712002
100	0.202344	0.404688	0.797656
101	0.147215	0.294429	0.852785
102	0.162636	0.325272	0.837364

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.172229 & 0.344457 & 0.827771 \tabularnewline
12 & 0.568783 & 0.862433 & 0.431217 \tabularnewline
13 & 0.48119 & 0.96238 & 0.51881 \tabularnewline
14 & 0.491529 & 0.983058 & 0.508471 \tabularnewline
15 & 0.460366 & 0.920733 & 0.539634 \tabularnewline
16 & 0.366411 & 0.732821 & 0.633589 \tabularnewline
17 & 0.27057 & 0.541139 & 0.72943 \tabularnewline
18 & 0.225488 & 0.450976 & 0.774512 \tabularnewline
19 & 0.177151 & 0.354301 & 0.822849 \tabularnewline
20 & 0.146344 & 0.292689 & 0.853656 \tabularnewline
21 & 0.411425 & 0.822849 & 0.588575 \tabularnewline
22 & 0.388021 & 0.776041 & 0.611979 \tabularnewline
23 & 0.47973 & 0.959461 & 0.52027 \tabularnewline
24 & 0.409128 & 0.818255 & 0.590872 \tabularnewline
25 & 0.388308 & 0.776617 & 0.611692 \tabularnewline
26 & 0.383011 & 0.766021 & 0.616989 \tabularnewline
27 & 0.317792 & 0.635585 & 0.682208 \tabularnewline
28 & 0.33029 & 0.66058 & 0.66971 \tabularnewline
29 & 0.427569 & 0.855138 & 0.572431 \tabularnewline
30 & 0.405349 & 0.810697 & 0.594651 \tabularnewline
31 & 0.344618 & 0.689237 & 0.655382 \tabularnewline
32 & 0.398007 & 0.796013 & 0.601993 \tabularnewline
33 & 0.400017 & 0.800034 & 0.599983 \tabularnewline
34 & 0.436915 & 0.873829 & 0.563085 \tabularnewline
35 & 0.485529 & 0.971059 & 0.514471 \tabularnewline
36 & 0.438528 & 0.877056 & 0.561472 \tabularnewline
37 & 0.486117 & 0.972234 & 0.513883 \tabularnewline
38 & 0.452593 & 0.905187 & 0.547407 \tabularnewline
39 & 0.404476 & 0.808952 & 0.595524 \tabularnewline
40 & 0.451271 & 0.902542 & 0.548729 \tabularnewline
41 & 0.433465 & 0.866929 & 0.566535 \tabularnewline
42 & 0.388118 & 0.776236 & 0.611882 \tabularnewline
43 & 0.344014 & 0.688028 & 0.655986 \tabularnewline
44 & 0.309916 & 0.619831 & 0.690084 \tabularnewline
45 & 0.333045 & 0.66609 & 0.666955 \tabularnewline
46 & 0.474115 & 0.948229 & 0.525885 \tabularnewline
47 & 0.465923 & 0.931847 & 0.534077 \tabularnewline
48 & 0.414835 & 0.82967 & 0.585165 \tabularnewline
49 & 0.372224 & 0.744449 & 0.627776 \tabularnewline
50 & 0.354686 & 0.709371 & 0.645314 \tabularnewline
51 & 0.305779 & 0.611559 & 0.694221 \tabularnewline
52 & 0.290527 & 0.581054 & 0.709473 \tabularnewline
53 & 0.257138 & 0.514277 & 0.742862 \tabularnewline
54 & 0.228189 & 0.456379 & 0.771811 \tabularnewline
55 & 0.202674 & 0.405349 & 0.797326 \tabularnewline
56 & 0.167824 & 0.335647 & 0.832176 \tabularnewline
57 & 0.1781 & 0.356199 & 0.8219 \tabularnewline
58 & 0.22307 & 0.446139 & 0.77693 \tabularnewline
59 & 0.209797 & 0.419595 & 0.790203 \tabularnewline
60 & 0.184688 & 0.369376 & 0.815312 \tabularnewline
61 & 0.154937 & 0.309874 & 0.845063 \tabularnewline
62 & 0.169737 & 0.339475 & 0.830263 \tabularnewline
63 & 0.196349 & 0.392697 & 0.803651 \tabularnewline
64 & 0.171977 & 0.343953 & 0.828023 \tabularnewline
65 & 0.202765 & 0.40553 & 0.797235 \tabularnewline
66 & 0.183331 & 0.366662 & 0.816669 \tabularnewline
67 & 0.208278 & 0.416557 & 0.791722 \tabularnewline
68 & 0.180575 & 0.36115 & 0.819425 \tabularnewline
69 & 0.220795 & 0.441589 & 0.779205 \tabularnewline
70 & 0.240459 & 0.480919 & 0.759541 \tabularnewline
71 & 0.283683 & 0.567365 & 0.716317 \tabularnewline
72 & 0.269638 & 0.539275 & 0.730362 \tabularnewline
73 & 0.325705 & 0.651409 & 0.674295 \tabularnewline
74 & 0.292601 & 0.585201 & 0.707399 \tabularnewline
75 & 0.274324 & 0.548647 & 0.725676 \tabularnewline
76 & 0.254844 & 0.509689 & 0.745156 \tabularnewline
77 & 0.311096 & 0.622191 & 0.688904 \tabularnewline
78 & 0.276822 & 0.553644 & 0.723178 \tabularnewline
79 & 0.319942 & 0.639885 & 0.680058 \tabularnewline
80 & 0.395454 & 0.790908 & 0.604546 \tabularnewline
81 & 0.352419 & 0.704838 & 0.647581 \tabularnewline
82 & 0.455728 & 0.911456 & 0.544272 \tabularnewline
83 & 0.430292 & 0.860583 & 0.569708 \tabularnewline
84 & 0.454299 & 0.908598 & 0.545701 \tabularnewline
85 & 0.423655 & 0.847309 & 0.576345 \tabularnewline
86 & 0.369065 & 0.738131 & 0.630935 \tabularnewline
87 & 0.381538 & 0.763076 & 0.618462 \tabularnewline
88 & 0.316064 & 0.632128 & 0.683936 \tabularnewline
89 & 0.349335 & 0.698669 & 0.650665 \tabularnewline
90 & 0.35022 & 0.700441 & 0.64978 \tabularnewline
91 & 0.301584 & 0.603168 & 0.698416 \tabularnewline
92 & 0.473223 & 0.946447 & 0.526777 \tabularnewline
93 & 0.421713 & 0.843427 & 0.578287 \tabularnewline
94 & 0.378724 & 0.757447 & 0.621276 \tabularnewline
95 & 0.55598 & 0.88804 & 0.44402 \tabularnewline
96 & 0.467741 & 0.935482 & 0.532259 \tabularnewline
97 & 0.445124 & 0.890248 & 0.554876 \tabularnewline
98 & 0.385986 & 0.771971 & 0.614014 \tabularnewline
99 & 0.287998 & 0.575996 & 0.712002 \tabularnewline
100 & 0.202344 & 0.404688 & 0.797656 \tabularnewline
101 & 0.147215 & 0.294429 & 0.852785 \tabularnewline
102 & 0.162636 & 0.325272 & 0.837364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.172229[/C][C]0.344457[/C][C]0.827771[/C][/ROW]
[ROW][C]12[/C][C]0.568783[/C][C]0.862433[/C][C]0.431217[/C][/ROW]
[ROW][C]13[/C][C]0.48119[/C][C]0.96238[/C][C]0.51881[/C][/ROW]
[ROW][C]14[/C][C]0.491529[/C][C]0.983058[/C][C]0.508471[/C][/ROW]
[ROW][C]15[/C][C]0.460366[/C][C]0.920733[/C][C]0.539634[/C][/ROW]
[ROW][C]16[/C][C]0.366411[/C][C]0.732821[/C][C]0.633589[/C][/ROW]
[ROW][C]17[/C][C]0.27057[/C][C]0.541139[/C][C]0.72943[/C][/ROW]
[ROW][C]18[/C][C]0.225488[/C][C]0.450976[/C][C]0.774512[/C][/ROW]
[ROW][C]19[/C][C]0.177151[/C][C]0.354301[/C][C]0.822849[/C][/ROW]
[ROW][C]20[/C][C]0.146344[/C][C]0.292689[/C][C]0.853656[/C][/ROW]
[ROW][C]21[/C][C]0.411425[/C][C]0.822849[/C][C]0.588575[/C][/ROW]
[ROW][C]22[/C][C]0.388021[/C][C]0.776041[/C][C]0.611979[/C][/ROW]
[ROW][C]23[/C][C]0.47973[/C][C]0.959461[/C][C]0.52027[/C][/ROW]
[ROW][C]24[/C][C]0.409128[/C][C]0.818255[/C][C]0.590872[/C][/ROW]
[ROW][C]25[/C][C]0.388308[/C][C]0.776617[/C][C]0.611692[/C][/ROW]
[ROW][C]26[/C][C]0.383011[/C][C]0.766021[/C][C]0.616989[/C][/ROW]
[ROW][C]27[/C][C]0.317792[/C][C]0.635585[/C][C]0.682208[/C][/ROW]
[ROW][C]28[/C][C]0.33029[/C][C]0.66058[/C][C]0.66971[/C][/ROW]
[ROW][C]29[/C][C]0.427569[/C][C]0.855138[/C][C]0.572431[/C][/ROW]
[ROW][C]30[/C][C]0.405349[/C][C]0.810697[/C][C]0.594651[/C][/ROW]
[ROW][C]31[/C][C]0.344618[/C][C]0.689237[/C][C]0.655382[/C][/ROW]
[ROW][C]32[/C][C]0.398007[/C][C]0.796013[/C][C]0.601993[/C][/ROW]
[ROW][C]33[/C][C]0.400017[/C][C]0.800034[/C][C]0.599983[/C][/ROW]
[ROW][C]34[/C][C]0.436915[/C][C]0.873829[/C][C]0.563085[/C][/ROW]
[ROW][C]35[/C][C]0.485529[/C][C]0.971059[/C][C]0.514471[/C][/ROW]
[ROW][C]36[/C][C]0.438528[/C][C]0.877056[/C][C]0.561472[/C][/ROW]
[ROW][C]37[/C][C]0.486117[/C][C]0.972234[/C][C]0.513883[/C][/ROW]
[ROW][C]38[/C][C]0.452593[/C][C]0.905187[/C][C]0.547407[/C][/ROW]
[ROW][C]39[/C][C]0.404476[/C][C]0.808952[/C][C]0.595524[/C][/ROW]
[ROW][C]40[/C][C]0.451271[/C][C]0.902542[/C][C]0.548729[/C][/ROW]
[ROW][C]41[/C][C]0.433465[/C][C]0.866929[/C][C]0.566535[/C][/ROW]
[ROW][C]42[/C][C]0.388118[/C][C]0.776236[/C][C]0.611882[/C][/ROW]
[ROW][C]43[/C][C]0.344014[/C][C]0.688028[/C][C]0.655986[/C][/ROW]
[ROW][C]44[/C][C]0.309916[/C][C]0.619831[/C][C]0.690084[/C][/ROW]
[ROW][C]45[/C][C]0.333045[/C][C]0.66609[/C][C]0.666955[/C][/ROW]
[ROW][C]46[/C][C]0.474115[/C][C]0.948229[/C][C]0.525885[/C][/ROW]
[ROW][C]47[/C][C]0.465923[/C][C]0.931847[/C][C]0.534077[/C][/ROW]
[ROW][C]48[/C][C]0.414835[/C][C]0.82967[/C][C]0.585165[/C][/ROW]
[ROW][C]49[/C][C]0.372224[/C][C]0.744449[/C][C]0.627776[/C][/ROW]
[ROW][C]50[/C][C]0.354686[/C][C]0.709371[/C][C]0.645314[/C][/ROW]
[ROW][C]51[/C][C]0.305779[/C][C]0.611559[/C][C]0.694221[/C][/ROW]
[ROW][C]52[/C][C]0.290527[/C][C]0.581054[/C][C]0.709473[/C][/ROW]
[ROW][C]53[/C][C]0.257138[/C][C]0.514277[/C][C]0.742862[/C][/ROW]
[ROW][C]54[/C][C]0.228189[/C][C]0.456379[/C][C]0.771811[/C][/ROW]
[ROW][C]55[/C][C]0.202674[/C][C]0.405349[/C][C]0.797326[/C][/ROW]
[ROW][C]56[/C][C]0.167824[/C][C]0.335647[/C][C]0.832176[/C][/ROW]
[ROW][C]57[/C][C]0.1781[/C][C]0.356199[/C][C]0.8219[/C][/ROW]
[ROW][C]58[/C][C]0.22307[/C][C]0.446139[/C][C]0.77693[/C][/ROW]
[ROW][C]59[/C][C]0.209797[/C][C]0.419595[/C][C]0.790203[/C][/ROW]
[ROW][C]60[/C][C]0.184688[/C][C]0.369376[/C][C]0.815312[/C][/ROW]
[ROW][C]61[/C][C]0.154937[/C][C]0.309874[/C][C]0.845063[/C][/ROW]
[ROW][C]62[/C][C]0.169737[/C][C]0.339475[/C][C]0.830263[/C][/ROW]
[ROW][C]63[/C][C]0.196349[/C][C]0.392697[/C][C]0.803651[/C][/ROW]
[ROW][C]64[/C][C]0.171977[/C][C]0.343953[/C][C]0.828023[/C][/ROW]
[ROW][C]65[/C][C]0.202765[/C][C]0.40553[/C][C]0.797235[/C][/ROW]
[ROW][C]66[/C][C]0.183331[/C][C]0.366662[/C][C]0.816669[/C][/ROW]
[ROW][C]67[/C][C]0.208278[/C][C]0.416557[/C][C]0.791722[/C][/ROW]
[ROW][C]68[/C][C]0.180575[/C][C]0.36115[/C][C]0.819425[/C][/ROW]
[ROW][C]69[/C][C]0.220795[/C][C]0.441589[/C][C]0.779205[/C][/ROW]
[ROW][C]70[/C][C]0.240459[/C][C]0.480919[/C][C]0.759541[/C][/ROW]
[ROW][C]71[/C][C]0.283683[/C][C]0.567365[/C][C]0.716317[/C][/ROW]
[ROW][C]72[/C][C]0.269638[/C][C]0.539275[/C][C]0.730362[/C][/ROW]
[ROW][C]73[/C][C]0.325705[/C][C]0.651409[/C][C]0.674295[/C][/ROW]
[ROW][C]74[/C][C]0.292601[/C][C]0.585201[/C][C]0.707399[/C][/ROW]
[ROW][C]75[/C][C]0.274324[/C][C]0.548647[/C][C]0.725676[/C][/ROW]
[ROW][C]76[/C][C]0.254844[/C][C]0.509689[/C][C]0.745156[/C][/ROW]
[ROW][C]77[/C][C]0.311096[/C][C]0.622191[/C][C]0.688904[/C][/ROW]
[ROW][C]78[/C][C]0.276822[/C][C]0.553644[/C][C]0.723178[/C][/ROW]
[ROW][C]79[/C][C]0.319942[/C][C]0.639885[/C][C]0.680058[/C][/ROW]
[ROW][C]80[/C][C]0.395454[/C][C]0.790908[/C][C]0.604546[/C][/ROW]
[ROW][C]81[/C][C]0.352419[/C][C]0.704838[/C][C]0.647581[/C][/ROW]
[ROW][C]82[/C][C]0.455728[/C][C]0.911456[/C][C]0.544272[/C][/ROW]
[ROW][C]83[/C][C]0.430292[/C][C]0.860583[/C][C]0.569708[/C][/ROW]
[ROW][C]84[/C][C]0.454299[/C][C]0.908598[/C][C]0.545701[/C][/ROW]
[ROW][C]85[/C][C]0.423655[/C][C]0.847309[/C][C]0.576345[/C][/ROW]
[ROW][C]86[/C][C]0.369065[/C][C]0.738131[/C][C]0.630935[/C][/ROW]
[ROW][C]87[/C][C]0.381538[/C][C]0.763076[/C][C]0.618462[/C][/ROW]
[ROW][C]88[/C][C]0.316064[/C][C]0.632128[/C][C]0.683936[/C][/ROW]
[ROW][C]89[/C][C]0.349335[/C][C]0.698669[/C][C]0.650665[/C][/ROW]
[ROW][C]90[/C][C]0.35022[/C][C]0.700441[/C][C]0.64978[/C][/ROW]
[ROW][C]91[/C][C]0.301584[/C][C]0.603168[/C][C]0.698416[/C][/ROW]
[ROW][C]92[/C][C]0.473223[/C][C]0.946447[/C][C]0.526777[/C][/ROW]
[ROW][C]93[/C][C]0.421713[/C][C]0.843427[/C][C]0.578287[/C][/ROW]
[ROW][C]94[/C][C]0.378724[/C][C]0.757447[/C][C]0.621276[/C][/ROW]
[ROW][C]95[/C][C]0.55598[/C][C]0.88804[/C][C]0.44402[/C][/ROW]
[ROW][C]96[/C][C]0.467741[/C][C]0.935482[/C][C]0.532259[/C][/ROW]
[ROW][C]97[/C][C]0.445124[/C][C]0.890248[/C][C]0.554876[/C][/ROW]
[ROW][C]98[/C][C]0.385986[/C][C]0.771971[/C][C]0.614014[/C][/ROW]
[ROW][C]99[/C][C]0.287998[/C][C]0.575996[/C][C]0.712002[/C][/ROW]
[ROW][C]100[/C][C]0.202344[/C][C]0.404688[/C][C]0.797656[/C][/ROW]
[ROW][C]101[/C][C]0.147215[/C][C]0.294429[/C][C]0.852785[/C][/ROW]
[ROW][C]102[/C][C]0.162636[/C][C]0.325272[/C][C]0.837364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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
11	0.172229	0.344457	0.827771
12	0.568783	0.862433	0.431217
13	0.48119	0.96238	0.51881
14	0.491529	0.983058	0.508471
15	0.460366	0.920733	0.539634
16	0.366411	0.732821	0.633589
17	0.27057	0.541139	0.72943
18	0.225488	0.450976	0.774512
19	0.177151	0.354301	0.822849
20	0.146344	0.292689	0.853656
21	0.411425	0.822849	0.588575
22	0.388021	0.776041	0.611979
23	0.47973	0.959461	0.52027
24	0.409128	0.818255	0.590872
25	0.388308	0.776617	0.611692
26	0.383011	0.766021	0.616989
27	0.317792	0.635585	0.682208
28	0.33029	0.66058	0.66971
29	0.427569	0.855138	0.572431
30	0.405349	0.810697	0.594651
31	0.344618	0.689237	0.655382
32	0.398007	0.796013	0.601993
33	0.400017	0.800034	0.599983
34	0.436915	0.873829	0.563085
35	0.485529	0.971059	0.514471
36	0.438528	0.877056	0.561472
37	0.486117	0.972234	0.513883
38	0.452593	0.905187	0.547407
39	0.404476	0.808952	0.595524
40	0.451271	0.902542	0.548729
41	0.433465	0.866929	0.566535
42	0.388118	0.776236	0.611882
43	0.344014	0.688028	0.655986
44	0.309916	0.619831	0.690084
45	0.333045	0.66609	0.666955
46	0.474115	0.948229	0.525885
47	0.465923	0.931847	0.534077
48	0.414835	0.82967	0.585165
49	0.372224	0.744449	0.627776
50	0.354686	0.709371	0.645314
51	0.305779	0.611559	0.694221
52	0.290527	0.581054	0.709473
53	0.257138	0.514277	0.742862
54	0.228189	0.456379	0.771811
55	0.202674	0.405349	0.797326
56	0.167824	0.335647	0.832176
57	0.1781	0.356199	0.8219
58	0.22307	0.446139	0.77693
59	0.209797	0.419595	0.790203
60	0.184688	0.369376	0.815312
61	0.154937	0.309874	0.845063
62	0.169737	0.339475	0.830263
63	0.196349	0.392697	0.803651
64	0.171977	0.343953	0.828023
65	0.202765	0.40553	0.797235
66	0.183331	0.366662	0.816669
67	0.208278	0.416557	0.791722
68	0.180575	0.36115	0.819425
69	0.220795	0.441589	0.779205
70	0.240459	0.480919	0.759541
71	0.283683	0.567365	0.716317
72	0.269638	0.539275	0.730362
73	0.325705	0.651409	0.674295
74	0.292601	0.585201	0.707399
75	0.274324	0.548647	0.725676
76	0.254844	0.509689	0.745156
77	0.311096	0.622191	0.688904
78	0.276822	0.553644	0.723178
79	0.319942	0.639885	0.680058
80	0.395454	0.790908	0.604546
81	0.352419	0.704838	0.647581
82	0.455728	0.911456	0.544272
83	0.430292	0.860583	0.569708
84	0.454299	0.908598	0.545701
85	0.423655	0.847309	0.576345
86	0.369065	0.738131	0.630935
87	0.381538	0.763076	0.618462
88	0.316064	0.632128	0.683936
89	0.349335	0.698669	0.650665
90	0.35022	0.700441	0.64978
91	0.301584	0.603168	0.698416
92	0.473223	0.946447	0.526777
93	0.421713	0.843427	0.578287
94	0.378724	0.757447	0.621276
95	0.55598	0.88804	0.44402
96	0.467741	0.935482	0.532259
97	0.445124	0.890248	0.554876
98	0.385986	0.771971	0.614014
99	0.287998	0.575996	0.712002
100	0.202344	0.404688	0.797656
101	0.147215	0.294429	0.852785
102	0.162636	0.325272	0.837364

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263947&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263947&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263947&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

PNG link

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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

PNG link

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PDF link

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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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