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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 26 Dec 2019 08:24:08 +0100

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/2019/Dec/26/t1577345484q1g3wip9fkdgiy5.htm/, Retrieved Sat, 18 May 2024 13:18:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318979, Retrieved Sat, 18 May 2024 13:18:25 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

123

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-       [Multiple Regression] [] [2019-12-26 07:24:08] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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65	60	60	55	60	70	50	9.9
55	60	60	35	50	55	80	8.9
75	55	60	55	60	60	55	8.9
80	50	65	50	65	55	40	7.6
60	75	50	45	55	75	70	7.1
70	65	60	55	65	55	55	7.1
60	55	75	70	45	65	50	7
60	70	60	60	50	70	30	7
65	75	60	60	60	65	45	6.9
65	70	60	60	60	65	55	6.8
65	60	40	60	60	65	70	6.6
60	65	65	55	45	55	70	6.4
55	65	55	65	45	65	75	6.3
45	60	45	65	40	65	45	6.3
65	45	60	55	65	70	70	6.2
60	70	55	60	60	60	50	6
75	70	65	60	50	60	60	5.6
75	60	70	70	55	60	35	5.5
50	60	70	75	40	60	50	5.4
60	60	55	60	45	60	45	5.4
65	40	40	55	70	70	65	5.3
45	50	40	65	50	65	40	5.2
55	65	60	60	55	75	40	5.1
50	45	70	60	30	65	40	5
60	60	55	45	50	55	45	5
75	40	35	50	80	65	45	5
75	70	60	55	50	50	50	5
55	55	70	60	45	45	30	5
50	55	65	60	40	75	40	4.7
55	65	50	45	60	55	45	4.7
50	60	60	55	40	55	35	4.6
55	55	50	60	60	60	50	4.6
55	55	60	45	50	50	50	4.6
45	50	50	60	60	65	25	4.5
55	50	50	60	60	65	35	4.5
50	50	55	40	45	65	40	4.4
50	45	40	45	55	60	45	4.4
65	55	55	60	55	65	55	4.4
60	50	45	45	70	45	55	4.4
45	55	60	40	50	45	55	4.3
45	45	40	55	55	50	60	4.3
60	45	35	40	65	60	60	4.3
45	55	55	45	35	75	50	4.3
45	45	55	65	40	50	50	4.3
55	55	45	35	55	75	45	4.2
60	65	55	50	50	45	35	4.2
70	65	70	50	55	50	35	4.2
65	60	30	55	50	75	55	4.1
65	65	60	40	60	50	35	4.1
55	60	50	35	55	70	50	4.1
60	65	45	60	55	60	65	4.1
55	60	55	55	50	50	60	4.1
50	60	60	65	40	55	60	4
45	50	50	55	50	75	65	4
60	55	45	40	60	60	40	4
45	55	45	65	45	60	50	4
60	60	50	60	55	50	45	4
70	65	45	55	70	65	35	3.9
55	55	40	50	55	65	55	3.8
45	45	45	45	60	40	75	3.8
55	55	50	55	65	70	35	3.8
70	60	60	60	45	70	65	3.8
45	55	40	60	40	55	80	3.7
45	60	65	60	40	50	25	3.7
45	60	65	50	40	70	40	3.7
55	60	35	60	50	75	65	3.7
70	70	70	35	50	50	35	3.7
55	40	30	70	60	75	80	3.7
45	60	60	60	60	55	30	3.7
45	60	50	45	35	80	80	3.6
60	65	65	55	55	65	35	3.6
55	55	50	55	45	50	60	3.6
45	50	55	40	65	50	35	3.6
35	50	75	50	35	60	30	3.5
45	50	65	60	35	45	70	3.5
45	35	70	60	35	50	40	3.4
65	60	40	60	65	65	50	3.4
45	50	50	55	45	50	30	3.3
50	55	60	55	50	60	25	3.3
55	45	45	75	50	25	20	3.3
45	50	60	50	35	50	70	3.3
60	65	35	35	55	50	60	3.2
45	50	60	60	50	45	20	3.2
45	60	55	60	45	60	40	3.2
60	65	35	55	70	45	60	3.2
45	55	50	35	50	55	45	3.2
45	65	50	60	55	55	35	3.2
60	60	60	45	70	45	35	3.1
50	45	30	55	65	50	55	3.1
50	50	50	50	50	50	55	3.1
60	70	55	40	50	65	45	3.1
70	50	65	75	50	45	35	3
45	45	45	60	40	50	35	3
65	60	35	50	55	65	40	3
50	45	40	40	70	80	40	3
70	60	60	50	55	50	45	2.9
50	55	55	55	45	55	40	2.9
50	55	55	50	60	50	30	2.9
65	60	55	50	70	50	50	2.8
45	40	55	45	35	70	35	2.8
45	60	60	55	40	60	50	2.8
45	55	60	60	40	60	45	2.8
40	45	60	65	35	65	40	2.8
45	50	60	50	30	65	45	2.7
45	60	50	40	45	50	40	2.7
80	75	55	50	60	50	45	2.7
55	60	45	60	65	70	40	2.6
55	45	40	55	60	55	35	2.6
50	55	50	50	50	45	70	2.6
45	60	55	60	45	50	35	2.6
45	50	65	35	40	55	45	2.6
45	45	65	65	35	60	50	2.5
45	60	60	60	40	55	45	2.5
65	70	55	40	55	55	55	2.5
45	55	55	55	40	50	40	2.5
45	45	65	55	40	45	40	2.4
40	50	50	60	40	65	65	2.4
50	50	55	60	45	55	40	2.3
60	40	30	50	65	60	50	2.3
70	55	45	60	55	55	40	2.3
45	45	20	55	65	70	35	2.3
45	55	50	40	50	60	45	2.3
55	40	45	55	60	40	60	2.3
60	50	50	65	55	40	20	2.2
60	65	50	45	55	60	50	2.2
40	50	50	50	45	70	60	2.2
50	50	55	35	50	55	65	2.1
60	70	55	55	55	70	35	2.1
45	45	60	50	45	60	45	2.1
55	50	50	50	55	60	35	2.1
50	65	65	55	35	55	45	2.1
50	55	40	55	50	40	70	2.1
50	50	55	50	50	70	30	2
45	50	60	55	40	60	25	2
55	55	50	40	60	60	50	2
55	45	30	50	65	75	80	2
65	60	50	40	65	55	35	2
45	45	70	60	35	45	30	1.9
50	55	55	55	55	50	45	1.9
50	60	40	45	55	55	50	1.9
60	50	55	45	55	60	40	1.9
60	60	45	50	70	55	40	1.9
55	60	55	50	45	50	45	1.9
40	60	55	55	35	65	75	1.9
55	55	60	55	45	50	25	1.9
50	50	50	50	50	65	35	1.9
50	60	65	55	35	75	45	1.7
55	50	60	35	65	65	25	1.6
65	65	45	50	50	75	55	1.6
40	60	60	65	35	45	25	1.6
35	45	80	75	25	60	35	1.6
65	60	60	40	55	40	30	1.6
45	60	50	55	40	60	55	1.6
35	60	65	60	45	60	35	1.6
50	70	60	50	35	60	40	1.5
45	40	55	50	45	55	35	1.4
50	50	45	50	55	50	50	1.4
50	60	60	45	45	75	65	1.4
60	45	45	60	55	55	50	1.4
50	60	30	50	50	65	75	1.3
70	60	45	60	55	60	55	1.3
55	50	55	45	50	65	35	1.3
65	55	60	55	55	55	40	1.3
50	55	45	55	55	55	45	1.2
45	50	65	65	30	65	50	1.1
45	50	60	50	45	55	30	1.1
45	50	55	50	40	55	35	1.1
50	50	45	50	50	40	55	1
55	45	25	45	60	65	45	1
50	55	45	50	55	60	60	0.9
50	45	70	60	35	45	25	0.8
45	65	60	60	45	60	30	0.7
50	55	65	45	40	75	50	0.6
45	60	55	60	45	80	35	0.5
50	35	60	70	35	40	35	0.5
45	60	55	35	50	55	35	0.5
60	60	40	40	55	75	75	0.4
50	55	55	55	45	60	60	0.4
45	55	60	55	40	50	40	0.4
60	60	55	50	45	60	35	0.4
45	50	60	45	35	55	60	0.4
40	35	60	55	45	60	40	0.4
50	50	55	30	50	60	50	0.2
55	50	55	50	55	55	30	0.1
40	60	65	60	40	55	40	-0.1
55	55	30	55	60	45	50	-0.2
50	50	55	55	45	65	35	-0.2
50	45	55	40	45	55	40	-0.8
50	45	45	50	55	35	55	-1.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

3 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318979&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&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	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
WAR[t] = -13.0797 + 0.0311391Contact[t] + 0.0231518GapP[t] + 0.068303Power[t] + 0.0504539Eye[t] + 0.0691848Ks[t] + 0.0279448Def[t] + 0.0390332Speed[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WAR[t] =  -13.0797 +  0.0311391Contact[t] +  0.0231518GapP[t] +  0.068303Power[t] +  0.0504539Eye[t] +  0.0691848Ks[t] +  0.0279448Def[t] +  0.0390332Speed[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WAR[t] =  -13.0797 +  0.0311391Contact[t] +  0.0231518GapP[t] +  0.068303Power[t] +  0.0504539Eye[t] +  0.0691848Ks[t] +  0.0279448Def[t] +  0.0390332Speed[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&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
WAR[t] = -13.0797 + 0.0311391Contact[t] + 0.0231518GapP[t] + 0.068303Power[t] + 0.0504539Eye[t] + 0.0691848Ks[t] + 0.0279448Def[t] + 0.0390332Speed[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)	-13.08	1.873	-6.9850e+00	5.257e-11	2.628e-11
Contact	+0.03114	0.01772	+1.7570e+00	0.0806	0.0403
GapP	+0.02315	0.01567	+1.4780e+00	0.1412	0.07062
Power	+0.0683	0.01504	+4.5410e+00	1.017e-05	5.086e-06
Eye	+0.05045	0.01337	+3.7730e+00	0.0002184	0.0001092
Ks	+0.06918	0.0184	+3.7610e+00	0.0002286	0.0001143
Def	+0.02795	0.01199	+2.3310e+00	0.02084	0.01042
Speed	+0.03903	0.009269	+4.2110e+00	4.003e-05	2.001e-05

\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) & -13.08 &  1.873 & -6.9850e+00 &  5.257e-11 &  2.628e-11 \tabularnewline
Contact & +0.03114 &  0.01772 & +1.7570e+00 &  0.0806 &  0.0403 \tabularnewline
GapP & +0.02315 &  0.01567 & +1.4780e+00 &  0.1412 &  0.07062 \tabularnewline
Power & +0.0683 &  0.01504 & +4.5410e+00 &  1.017e-05 &  5.086e-06 \tabularnewline
Eye & +0.05045 &  0.01337 & +3.7730e+00 &  0.0002184 &  0.0001092 \tabularnewline
Ks & +0.06918 &  0.0184 & +3.7610e+00 &  0.0002286 &  0.0001143 \tabularnewline
Def & +0.02795 &  0.01199 & +2.3310e+00 &  0.02084 &  0.01042 \tabularnewline
Speed & +0.03903 &  0.009269 & +4.2110e+00 &  4.003e-05 &  2.001e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&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]-13.08[/C][C] 1.873[/C][C]-6.9850e+00[/C][C] 5.257e-11[/C][C] 2.628e-11[/C][/ROW]
[ROW][C]Contact[/C][C]+0.03114[/C][C] 0.01772[/C][C]+1.7570e+00[/C][C] 0.0806[/C][C] 0.0403[/C][/ROW]
[ROW][C]GapP[/C][C]+0.02315[/C][C] 0.01567[/C][C]+1.4780e+00[/C][C] 0.1412[/C][C] 0.07062[/C][/ROW]
[ROW][C]Power[/C][C]+0.0683[/C][C] 0.01504[/C][C]+4.5410e+00[/C][C] 1.017e-05[/C][C] 5.086e-06[/C][/ROW]
[ROW][C]Eye[/C][C]+0.05045[/C][C] 0.01337[/C][C]+3.7730e+00[/C][C] 0.0002184[/C][C] 0.0001092[/C][/ROW]
[ROW][C]Ks[/C][C]+0.06918[/C][C] 0.0184[/C][C]+3.7610e+00[/C][C] 0.0002286[/C][C] 0.0001143[/C][/ROW]
[ROW][C]Def[/C][C]+0.02795[/C][C] 0.01199[/C][C]+2.3310e+00[/C][C] 0.02084[/C][C] 0.01042[/C][/ROW]
[ROW][C]Speed[/C][C]+0.03903[/C][C] 0.009269[/C][C]+4.2110e+00[/C][C] 4.003e-05[/C][C] 2.001e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&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)	-13.08	1.873	-6.9850e+00	5.257e-11	2.628e-11
Contact	+0.03114	0.01772	+1.7570e+00	0.0806	0.0403
GapP	+0.02315	0.01567	+1.4780e+00	0.1412	0.07062
Power	+0.0683	0.01504	+4.5410e+00	1.017e-05	5.086e-06
Eye	+0.05045	0.01337	+3.7730e+00	0.0002184	0.0001092
Ks	+0.06918	0.0184	+3.7610e+00	0.0002286	0.0001143
Def	+0.02795	0.01199	+2.3310e+00	0.02084	0.01042
Speed	+0.03903	0.009269	+4.2110e+00	4.003e-05	2.001e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.5789
R-squared	0.3352
Adjusted R-squared	0.3095
F-TEST (value)	13.04
F-TEST (DF numerator)	7
F-TEST (DF denominator)	181
p-value	1.524e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.56
Sum Squared Residuals	440.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5789 \tabularnewline
R-squared &  0.3352 \tabularnewline
Adjusted R-squared &  0.3095 \tabularnewline
F-TEST (value) &  13.04 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 181 \tabularnewline
p-value &  1.524e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.56 \tabularnewline
Sum Squared Residuals &  440.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5789[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3352[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3095[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 13.04[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]181[/C][/ROW]
[ROW][C]p-value[/C][C] 1.524e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.56[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 440.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&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	0.5789
R-squared	0.3352
Adjusted R-squared	0.3095
F-TEST (value)	13.04
F-TEST (DF numerator)	7
F-TEST (DF denominator)	181
p-value	1.524e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.56
Sum Squared Residuals	440.7

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9.9	5.265	4.635
2	8.9	4.005	4.895
3	8.9	5.377	3.523
4	7.6	5.127	2.473
5	7.1	4.844	2.256
6	7.1	5.659	1.441
7	7	5.598	1.402
8	7	4.121	2.879
9	6.9	5.53	1.37
10	6.8	5.805	0.9953
11	6.6	4.793	1.807
12	6.4	4.891	1.509
13	6.3	5.031	1.269
14	6.3	2.404	3.896
15	6.2	6.045	0.1552
16	6	4.973	1.027
17	5.6	5.821	-0.2212
18	5.5	5.806	-0.3058
19	5.4	4.827	0.5727
20	5.4	3.508	1.892
21	5.3	4.714	0.5863
22	5.2	2.328	2.872
23	5.1	4.726	0.3745
24	5	2.781	2.219
25	5	2.958	2.042
26	5	4.203	0.7972
27	5	4.558	0.4424
28	5	3.257	1.743
29	4.7	3.642	1.058
30	4.7	3.268	1.432
31	4.6	2.41	2.19
32	4.6	4.128	0.4719
33	4.6	3.083	1.517
34	4.5	2.865	1.635
35	4.5	3.567	0.9335
36	4.4	1.901	2.499
37	4.4	1.76	2.64
38	4.4	4.77	-0.37
39	4.4	3.538	0.8625
40	4.3	2.575	1.725
41	4.3	2.415	1.885
42	4.3	2.755	1.545
43	4.3	2.091	2.209
44	4.3	2.516	1.784
45	4.2	2.403	1.797
46	4.2	2.656	1.544
47	4.2	4.477	-0.2773
48	4.1	2.859	1.241
49	4.1	3.48	0.62
50	4.1	2.916	1.184
51	4.1	4.413	-0.3134
52	4.1	3.752	0.3479
53	4	3.89	0.1096
54	4	3.761	0.2385
55	4	2.543	1.457
56	4	2.69	1.31
57	4	3.579	0.421
58	3.9	4.479	-0.579
59	3.8	2.929	0.8705
60	3.8	2.904	0.8962
61	3.8	3.916	-0.1157
62	3.8	5.221	-1.421
63	3.7	2.781	0.9188
64	3.7	2.318	1.382
65	3.7	2.958	0.7421
66	3.7	3.532	0.1679
67	3.7	3.49	0.2096
68	3.7	4.509	-0.8095
69	3.7	3.695	0.00491
70	3.6	3.176	0.4241
71	3.6	4.496	-0.8959
72	3.6	2.949	0.6511
73	3.6	2.514	1.086
74	3.5	2.082	1.418
75	3.5	3.357	0.1427
76	3.4	2.32	1.08
77	3.4	4.358	-0.9578
78	3.3	1.351	1.949
79	3.3	2.735	0.5645
80	3.3	1.471	1.829
81	3.3	2.651	0.649
82	3.2	1.994	1.206
83	3.2	2.102	1.098
84	3.2	2.846	0.3541
85	3.2	3.901	-0.7015
86	3.2	1.529	1.671
87	3.2	2.977	0.2229
88	3.1	4.013	-0.9129
89	3.1	2.384	0.7158
90	3.1	2.576	0.524
91	3.1	3.216	-0.1162
92	3	4.564	-1.564
93	3	0.995	2.005
94	3	2.43	0.5704
95	3	2.909	0.09083
96	2.9	4.069	-1.169
97	2.9	2.494	0.4062
98	2.9	2.749	0.1508
99	2.8	4.805	-2.005
100	2.8	1.018	1.782
101	2.8	2.98	-0.1795
102	2.8	2.921	-0.1209
103	2.8	2.385	0.4155
104	2.7	1.748	0.9516
105	2.7	1.216	1.484
106	2.7	4.732	-2.032
107	2.6	4.137	-1.537
108	2.6	2.236	0.364
109	2.6	3.137	-0.5375
110	2.6	2.371	0.2288
111	2.6	1.746	0.8545
112	2.5	3.132	-0.6324
113	2.5	2.897	-0.3969
114	2.5	3.829	-1.329
115	2.5	1.852	0.6475
116	2.4	2.164	0.2358
117	2.4	2.887	-0.4868
118	2.3	2.63	-0.3303
119	2.3	2.412	-0.1118
120	2.3	3.378	-1.078
121	2.3	1.324	0.9763
122	2.3	1.921	0.3794
123	2.3	3.018	-0.7185
124	2.2	2.344	-0.1445
125	2.2	3.413	-1.213
126	2.2	2.673	-0.4727
127	2.1	2.691	-0.5907
128	2.1	4.068	-1.968
129	2.1	2.531	-0.4307
130	2.1	2.576	-0.4764
131	2.1	2.912	-0.8117
132	2.1	2.567	-0.467
133	2	2.501	-0.5005
134	2	1.772	0.2278
135	2	3.119	-1.119
136	2	3.962	-1.962
137	2	3.167	-1.167
138	1.9	2.022	-0.1218
139	1.9	3.241	-1.341
140	1.9	2.163	-0.2627
141	1.9	3.016	-1.116
142	1.9	3.715	-1.815
143	1.9	2.568	-0.6684
144	1.9	3.252	-1.352
145	1.9	2.266	-0.3658
146	1.9	2.214	-0.3145
147	1.7	3.355	-1.655
148	1.6	2.944	-1.344
149	1.6	3.747	-2.147
150	1.6	1.587	0.01257
151	1.6	3.073	-1.473
152	1.6	2.544	-0.9437
153	1.6	2.492	-0.8916
154	1.6	3.022	-1.422
155	1.5	2.378	-0.8782
156	1.4	1.543	-0.1434
157	1.4	2.385	-0.9852
158	1.4	3.981	-2.581
159	1.4	3.225	-1.825
160	1.3	2.641	-1.341
161	1.3	4.219	-2.919
162	1.3	2.459	-1.159
163	1.3	3.994	-2.694
164	1.2	2.698	-1.498
165	1.1	3.042	-1.942
166	1.1	1.921	-0.8213
167	1.1	1.429	-0.329
168	1	1.955	-0.955
169	1	1.377	-0.3767
170	0.9	3.171	-2.271
171	0.8	1.982	-1.182
172	0.7	2.913	-2.213
173	0.6	3.276	-2.676
174	0.5	3.21	-2.71
175	0.5	1.823	-1.323
176	0.5	1.596	-1.096
177	0.4	3.756	-3.357
178	0.4	3.414	-3.014
179	0.4	2.194	-1.794
180	0.4	2.613	-2.213
181	0.4	2.148	-1.748
182	0.4	2.201	-1.801
183	0.2	1.993	-1.793
184	0.1	2.583	-2.483
185	-0.1	2.888	-2.988
186	-0.2	2.091	-2.291
187	-0.2	2.462	-2.662
188	-0.8	1.505	-2.305
189	-1.1	2.045	-3.145

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9.9 &  5.265 &  4.635 \tabularnewline
2 &  8.9 &  4.005 &  4.895 \tabularnewline
3 &  8.9 &  5.377 &  3.523 \tabularnewline
4 &  7.6 &  5.127 &  2.473 \tabularnewline
5 &  7.1 &  4.844 &  2.256 \tabularnewline
6 &  7.1 &  5.659 &  1.441 \tabularnewline
7 &  7 &  5.598 &  1.402 \tabularnewline
8 &  7 &  4.121 &  2.879 \tabularnewline
9 &  6.9 &  5.53 &  1.37 \tabularnewline
10 &  6.8 &  5.805 &  0.9953 \tabularnewline
11 &  6.6 &  4.793 &  1.807 \tabularnewline
12 &  6.4 &  4.891 &  1.509 \tabularnewline
13 &  6.3 &  5.031 &  1.269 \tabularnewline
14 &  6.3 &  2.404 &  3.896 \tabularnewline
15 &  6.2 &  6.045 &  0.1552 \tabularnewline
16 &  6 &  4.973 &  1.027 \tabularnewline
17 &  5.6 &  5.821 & -0.2212 \tabularnewline
18 &  5.5 &  5.806 & -0.3058 \tabularnewline
19 &  5.4 &  4.827 &  0.5727 \tabularnewline
20 &  5.4 &  3.508 &  1.892 \tabularnewline
21 &  5.3 &  4.714 &  0.5863 \tabularnewline
22 &  5.2 &  2.328 &  2.872 \tabularnewline
23 &  5.1 &  4.726 &  0.3745 \tabularnewline
24 &  5 &  2.781 &  2.219 \tabularnewline
25 &  5 &  2.958 &  2.042 \tabularnewline
26 &  5 &  4.203 &  0.7972 \tabularnewline
27 &  5 &  4.558 &  0.4424 \tabularnewline
28 &  5 &  3.257 &  1.743 \tabularnewline
29 &  4.7 &  3.642 &  1.058 \tabularnewline
30 &  4.7 &  3.268 &  1.432 \tabularnewline
31 &  4.6 &  2.41 &  2.19 \tabularnewline
32 &  4.6 &  4.128 &  0.4719 \tabularnewline
33 &  4.6 &  3.083 &  1.517 \tabularnewline
34 &  4.5 &  2.865 &  1.635 \tabularnewline
35 &  4.5 &  3.567 &  0.9335 \tabularnewline
36 &  4.4 &  1.901 &  2.499 \tabularnewline
37 &  4.4 &  1.76 &  2.64 \tabularnewline
38 &  4.4 &  4.77 & -0.37 \tabularnewline
39 &  4.4 &  3.538 &  0.8625 \tabularnewline
40 &  4.3 &  2.575 &  1.725 \tabularnewline
41 &  4.3 &  2.415 &  1.885 \tabularnewline
42 &  4.3 &  2.755 &  1.545 \tabularnewline
43 &  4.3 &  2.091 &  2.209 \tabularnewline
44 &  4.3 &  2.516 &  1.784 \tabularnewline
45 &  4.2 &  2.403 &  1.797 \tabularnewline
46 &  4.2 &  2.656 &  1.544 \tabularnewline
47 &  4.2 &  4.477 & -0.2773 \tabularnewline
48 &  4.1 &  2.859 &  1.241 \tabularnewline
49 &  4.1 &  3.48 &  0.62 \tabularnewline
50 &  4.1 &  2.916 &  1.184 \tabularnewline
51 &  4.1 &  4.413 & -0.3134 \tabularnewline
52 &  4.1 &  3.752 &  0.3479 \tabularnewline
53 &  4 &  3.89 &  0.1096 \tabularnewline
54 &  4 &  3.761 &  0.2385 \tabularnewline
55 &  4 &  2.543 &  1.457 \tabularnewline
56 &  4 &  2.69 &  1.31 \tabularnewline
57 &  4 &  3.579 &  0.421 \tabularnewline
58 &  3.9 &  4.479 & -0.579 \tabularnewline
59 &  3.8 &  2.929 &  0.8705 \tabularnewline
60 &  3.8 &  2.904 &  0.8962 \tabularnewline
61 &  3.8 &  3.916 & -0.1157 \tabularnewline
62 &  3.8 &  5.221 & -1.421 \tabularnewline
63 &  3.7 &  2.781 &  0.9188 \tabularnewline
64 &  3.7 &  2.318 &  1.382 \tabularnewline
65 &  3.7 &  2.958 &  0.7421 \tabularnewline
66 &  3.7 &  3.532 &  0.1679 \tabularnewline
67 &  3.7 &  3.49 &  0.2096 \tabularnewline
68 &  3.7 &  4.509 & -0.8095 \tabularnewline
69 &  3.7 &  3.695 &  0.00491 \tabularnewline
70 &  3.6 &  3.176 &  0.4241 \tabularnewline
71 &  3.6 &  4.496 & -0.8959 \tabularnewline
72 &  3.6 &  2.949 &  0.6511 \tabularnewline
73 &  3.6 &  2.514 &  1.086 \tabularnewline
74 &  3.5 &  2.082 &  1.418 \tabularnewline
75 &  3.5 &  3.357 &  0.1427 \tabularnewline
76 &  3.4 &  2.32 &  1.08 \tabularnewline
77 &  3.4 &  4.358 & -0.9578 \tabularnewline
78 &  3.3 &  1.351 &  1.949 \tabularnewline
79 &  3.3 &  2.735 &  0.5645 \tabularnewline
80 &  3.3 &  1.471 &  1.829 \tabularnewline
81 &  3.3 &  2.651 &  0.649 \tabularnewline
82 &  3.2 &  1.994 &  1.206 \tabularnewline
83 &  3.2 &  2.102 &  1.098 \tabularnewline
84 &  3.2 &  2.846 &  0.3541 \tabularnewline
85 &  3.2 &  3.901 & -0.7015 \tabularnewline
86 &  3.2 &  1.529 &  1.671 \tabularnewline
87 &  3.2 &  2.977 &  0.2229 \tabularnewline
88 &  3.1 &  4.013 & -0.9129 \tabularnewline
89 &  3.1 &  2.384 &  0.7158 \tabularnewline
90 &  3.1 &  2.576 &  0.524 \tabularnewline
91 &  3.1 &  3.216 & -0.1162 \tabularnewline
92 &  3 &  4.564 & -1.564 \tabularnewline
93 &  3 &  0.995 &  2.005 \tabularnewline
94 &  3 &  2.43 &  0.5704 \tabularnewline
95 &  3 &  2.909 &  0.09083 \tabularnewline
96 &  2.9 &  4.069 & -1.169 \tabularnewline
97 &  2.9 &  2.494 &  0.4062 \tabularnewline
98 &  2.9 &  2.749 &  0.1508 \tabularnewline
99 &  2.8 &  4.805 & -2.005 \tabularnewline
100 &  2.8 &  1.018 &  1.782 \tabularnewline
101 &  2.8 &  2.98 & -0.1795 \tabularnewline
102 &  2.8 &  2.921 & -0.1209 \tabularnewline
103 &  2.8 &  2.385 &  0.4155 \tabularnewline
104 &  2.7 &  1.748 &  0.9516 \tabularnewline
105 &  2.7 &  1.216 &  1.484 \tabularnewline
106 &  2.7 &  4.732 & -2.032 \tabularnewline
107 &  2.6 &  4.137 & -1.537 \tabularnewline
108 &  2.6 &  2.236 &  0.364 \tabularnewline
109 &  2.6 &  3.137 & -0.5375 \tabularnewline
110 &  2.6 &  2.371 &  0.2288 \tabularnewline
111 &  2.6 &  1.746 &  0.8545 \tabularnewline
112 &  2.5 &  3.132 & -0.6324 \tabularnewline
113 &  2.5 &  2.897 & -0.3969 \tabularnewline
114 &  2.5 &  3.829 & -1.329 \tabularnewline
115 &  2.5 &  1.852 &  0.6475 \tabularnewline
116 &  2.4 &  2.164 &  0.2358 \tabularnewline
117 &  2.4 &  2.887 & -0.4868 \tabularnewline
118 &  2.3 &  2.63 & -0.3303 \tabularnewline
119 &  2.3 &  2.412 & -0.1118 \tabularnewline
120 &  2.3 &  3.378 & -1.078 \tabularnewline
121 &  2.3 &  1.324 &  0.9763 \tabularnewline
122 &  2.3 &  1.921 &  0.3794 \tabularnewline
123 &  2.3 &  3.018 & -0.7185 \tabularnewline
124 &  2.2 &  2.344 & -0.1445 \tabularnewline
125 &  2.2 &  3.413 & -1.213 \tabularnewline
126 &  2.2 &  2.673 & -0.4727 \tabularnewline
127 &  2.1 &  2.691 & -0.5907 \tabularnewline
128 &  2.1 &  4.068 & -1.968 \tabularnewline
129 &  2.1 &  2.531 & -0.4307 \tabularnewline
130 &  2.1 &  2.576 & -0.4764 \tabularnewline
131 &  2.1 &  2.912 & -0.8117 \tabularnewline
132 &  2.1 &  2.567 & -0.467 \tabularnewline
133 &  2 &  2.501 & -0.5005 \tabularnewline
134 &  2 &  1.772 &  0.2278 \tabularnewline
135 &  2 &  3.119 & -1.119 \tabularnewline
136 &  2 &  3.962 & -1.962 \tabularnewline
137 &  2 &  3.167 & -1.167 \tabularnewline
138 &  1.9 &  2.022 & -0.1218 \tabularnewline
139 &  1.9 &  3.241 & -1.341 \tabularnewline
140 &  1.9 &  2.163 & -0.2627 \tabularnewline
141 &  1.9 &  3.016 & -1.116 \tabularnewline
142 &  1.9 &  3.715 & -1.815 \tabularnewline
143 &  1.9 &  2.568 & -0.6684 \tabularnewline
144 &  1.9 &  3.252 & -1.352 \tabularnewline
145 &  1.9 &  2.266 & -0.3658 \tabularnewline
146 &  1.9 &  2.214 & -0.3145 \tabularnewline
147 &  1.7 &  3.355 & -1.655 \tabularnewline
148 &  1.6 &  2.944 & -1.344 \tabularnewline
149 &  1.6 &  3.747 & -2.147 \tabularnewline
150 &  1.6 &  1.587 &  0.01257 \tabularnewline
151 &  1.6 &  3.073 & -1.473 \tabularnewline
152 &  1.6 &  2.544 & -0.9437 \tabularnewline
153 &  1.6 &  2.492 & -0.8916 \tabularnewline
154 &  1.6 &  3.022 & -1.422 \tabularnewline
155 &  1.5 &  2.378 & -0.8782 \tabularnewline
156 &  1.4 &  1.543 & -0.1434 \tabularnewline
157 &  1.4 &  2.385 & -0.9852 \tabularnewline
158 &  1.4 &  3.981 & -2.581 \tabularnewline
159 &  1.4 &  3.225 & -1.825 \tabularnewline
160 &  1.3 &  2.641 & -1.341 \tabularnewline
161 &  1.3 &  4.219 & -2.919 \tabularnewline
162 &  1.3 &  2.459 & -1.159 \tabularnewline
163 &  1.3 &  3.994 & -2.694 \tabularnewline
164 &  1.2 &  2.698 & -1.498 \tabularnewline
165 &  1.1 &  3.042 & -1.942 \tabularnewline
166 &  1.1 &  1.921 & -0.8213 \tabularnewline
167 &  1.1 &  1.429 & -0.329 \tabularnewline
168 &  1 &  1.955 & -0.955 \tabularnewline
169 &  1 &  1.377 & -0.3767 \tabularnewline
170 &  0.9 &  3.171 & -2.271 \tabularnewline
171 &  0.8 &  1.982 & -1.182 \tabularnewline
172 &  0.7 &  2.913 & -2.213 \tabularnewline
173 &  0.6 &  3.276 & -2.676 \tabularnewline
174 &  0.5 &  3.21 & -2.71 \tabularnewline
175 &  0.5 &  1.823 & -1.323 \tabularnewline
176 &  0.5 &  1.596 & -1.096 \tabularnewline
177 &  0.4 &  3.756 & -3.357 \tabularnewline
178 &  0.4 &  3.414 & -3.014 \tabularnewline
179 &  0.4 &  2.194 & -1.794 \tabularnewline
180 &  0.4 &  2.613 & -2.213 \tabularnewline
181 &  0.4 &  2.148 & -1.748 \tabularnewline
182 &  0.4 &  2.201 & -1.801 \tabularnewline
183 &  0.2 &  1.993 & -1.793 \tabularnewline
184 &  0.1 &  2.583 & -2.483 \tabularnewline
185 & -0.1 &  2.888 & -2.988 \tabularnewline
186 & -0.2 &  2.091 & -2.291 \tabularnewline
187 & -0.2 &  2.462 & -2.662 \tabularnewline
188 & -0.8 &  1.505 & -2.305 \tabularnewline
189 & -1.1 &  2.045 & -3.145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=5

[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] 9.9[/C][C] 5.265[/C][C] 4.635[/C][/ROW]
[ROW][C]2[/C][C] 8.9[/C][C] 4.005[/C][C] 4.895[/C][/ROW]
[ROW][C]3[/C][C] 8.9[/C][C] 5.377[/C][C] 3.523[/C][/ROW]
[ROW][C]4[/C][C] 7.6[/C][C] 5.127[/C][C] 2.473[/C][/ROW]
[ROW][C]5[/C][C] 7.1[/C][C] 4.844[/C][C] 2.256[/C][/ROW]
[ROW][C]6[/C][C] 7.1[/C][C] 5.659[/C][C] 1.441[/C][/ROW]
[ROW][C]7[/C][C] 7[/C][C] 5.598[/C][C] 1.402[/C][/ROW]
[ROW][C]8[/C][C] 7[/C][C] 4.121[/C][C] 2.879[/C][/ROW]
[ROW][C]9[/C][C] 6.9[/C][C] 5.53[/C][C] 1.37[/C][/ROW]
[ROW][C]10[/C][C] 6.8[/C][C] 5.805[/C][C] 0.9953[/C][/ROW]
[ROW][C]11[/C][C] 6.6[/C][C] 4.793[/C][C] 1.807[/C][/ROW]
[ROW][C]12[/C][C] 6.4[/C][C] 4.891[/C][C] 1.509[/C][/ROW]
[ROW][C]13[/C][C] 6.3[/C][C] 5.031[/C][C] 1.269[/C][/ROW]
[ROW][C]14[/C][C] 6.3[/C][C] 2.404[/C][C] 3.896[/C][/ROW]
[ROW][C]15[/C][C] 6.2[/C][C] 6.045[/C][C] 0.1552[/C][/ROW]
[ROW][C]16[/C][C] 6[/C][C] 4.973[/C][C] 1.027[/C][/ROW]
[ROW][C]17[/C][C] 5.6[/C][C] 5.821[/C][C]-0.2212[/C][/ROW]
[ROW][C]18[/C][C] 5.5[/C][C] 5.806[/C][C]-0.3058[/C][/ROW]
[ROW][C]19[/C][C] 5.4[/C][C] 4.827[/C][C] 0.5727[/C][/ROW]
[ROW][C]20[/C][C] 5.4[/C][C] 3.508[/C][C] 1.892[/C][/ROW]
[ROW][C]21[/C][C] 5.3[/C][C] 4.714[/C][C] 0.5863[/C][/ROW]
[ROW][C]22[/C][C] 5.2[/C][C] 2.328[/C][C] 2.872[/C][/ROW]
[ROW][C]23[/C][C] 5.1[/C][C] 4.726[/C][C] 0.3745[/C][/ROW]
[ROW][C]24[/C][C] 5[/C][C] 2.781[/C][C] 2.219[/C][/ROW]
[ROW][C]25[/C][C] 5[/C][C] 2.958[/C][C] 2.042[/C][/ROW]
[ROW][C]26[/C][C] 5[/C][C] 4.203[/C][C] 0.7972[/C][/ROW]
[ROW][C]27[/C][C] 5[/C][C] 4.558[/C][C] 0.4424[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 3.257[/C][C] 1.743[/C][/ROW]
[ROW][C]29[/C][C] 4.7[/C][C] 3.642[/C][C] 1.058[/C][/ROW]
[ROW][C]30[/C][C] 4.7[/C][C] 3.268[/C][C] 1.432[/C][/ROW]
[ROW][C]31[/C][C] 4.6[/C][C] 2.41[/C][C] 2.19[/C][/ROW]
[ROW][C]32[/C][C] 4.6[/C][C] 4.128[/C][C] 0.4719[/C][/ROW]
[ROW][C]33[/C][C] 4.6[/C][C] 3.083[/C][C] 1.517[/C][/ROW]
[ROW][C]34[/C][C] 4.5[/C][C] 2.865[/C][C] 1.635[/C][/ROW]
[ROW][C]35[/C][C] 4.5[/C][C] 3.567[/C][C] 0.9335[/C][/ROW]
[ROW][C]36[/C][C] 4.4[/C][C] 1.901[/C][C] 2.499[/C][/ROW]
[ROW][C]37[/C][C] 4.4[/C][C] 1.76[/C][C] 2.64[/C][/ROW]
[ROW][C]38[/C][C] 4.4[/C][C] 4.77[/C][C]-0.37[/C][/ROW]
[ROW][C]39[/C][C] 4.4[/C][C] 3.538[/C][C] 0.8625[/C][/ROW]
[ROW][C]40[/C][C] 4.3[/C][C] 2.575[/C][C] 1.725[/C][/ROW]
[ROW][C]41[/C][C] 4.3[/C][C] 2.415[/C][C] 1.885[/C][/ROW]
[ROW][C]42[/C][C] 4.3[/C][C] 2.755[/C][C] 1.545[/C][/ROW]
[ROW][C]43[/C][C] 4.3[/C][C] 2.091[/C][C] 2.209[/C][/ROW]
[ROW][C]44[/C][C] 4.3[/C][C] 2.516[/C][C] 1.784[/C][/ROW]
[ROW][C]45[/C][C] 4.2[/C][C] 2.403[/C][C] 1.797[/C][/ROW]
[ROW][C]46[/C][C] 4.2[/C][C] 2.656[/C][C] 1.544[/C][/ROW]
[ROW][C]47[/C][C] 4.2[/C][C] 4.477[/C][C]-0.2773[/C][/ROW]
[ROW][C]48[/C][C] 4.1[/C][C] 2.859[/C][C] 1.241[/C][/ROW]
[ROW][C]49[/C][C] 4.1[/C][C] 3.48[/C][C] 0.62[/C][/ROW]
[ROW][C]50[/C][C] 4.1[/C][C] 2.916[/C][C] 1.184[/C][/ROW]
[ROW][C]51[/C][C] 4.1[/C][C] 4.413[/C][C]-0.3134[/C][/ROW]
[ROW][C]52[/C][C] 4.1[/C][C] 3.752[/C][C] 0.3479[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 3.89[/C][C] 0.1096[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.761[/C][C] 0.2385[/C][/ROW]
[ROW][C]55[/C][C] 4[/C][C] 2.543[/C][C] 1.457[/C][/ROW]
[ROW][C]56[/C][C] 4[/C][C] 2.69[/C][C] 1.31[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 3.579[/C][C] 0.421[/C][/ROW]
[ROW][C]58[/C][C] 3.9[/C][C] 4.479[/C][C]-0.579[/C][/ROW]
[ROW][C]59[/C][C] 3.8[/C][C] 2.929[/C][C] 0.8705[/C][/ROW]
[ROW][C]60[/C][C] 3.8[/C][C] 2.904[/C][C] 0.8962[/C][/ROW]
[ROW][C]61[/C][C] 3.8[/C][C] 3.916[/C][C]-0.1157[/C][/ROW]
[ROW][C]62[/C][C] 3.8[/C][C] 5.221[/C][C]-1.421[/C][/ROW]
[ROW][C]63[/C][C] 3.7[/C][C] 2.781[/C][C] 0.9188[/C][/ROW]
[ROW][C]64[/C][C] 3.7[/C][C] 2.318[/C][C] 1.382[/C][/ROW]
[ROW][C]65[/C][C] 3.7[/C][C] 2.958[/C][C] 0.7421[/C][/ROW]
[ROW][C]66[/C][C] 3.7[/C][C] 3.532[/C][C] 0.1679[/C][/ROW]
[ROW][C]67[/C][C] 3.7[/C][C] 3.49[/C][C] 0.2096[/C][/ROW]
[ROW][C]68[/C][C] 3.7[/C][C] 4.509[/C][C]-0.8095[/C][/ROW]
[ROW][C]69[/C][C] 3.7[/C][C] 3.695[/C][C] 0.00491[/C][/ROW]
[ROW][C]70[/C][C] 3.6[/C][C] 3.176[/C][C] 0.4241[/C][/ROW]
[ROW][C]71[/C][C] 3.6[/C][C] 4.496[/C][C]-0.8959[/C][/ROW]
[ROW][C]72[/C][C] 3.6[/C][C] 2.949[/C][C] 0.6511[/C][/ROW]
[ROW][C]73[/C][C] 3.6[/C][C] 2.514[/C][C] 1.086[/C][/ROW]
[ROW][C]74[/C][C] 3.5[/C][C] 2.082[/C][C] 1.418[/C][/ROW]
[ROW][C]75[/C][C] 3.5[/C][C] 3.357[/C][C] 0.1427[/C][/ROW]
[ROW][C]76[/C][C] 3.4[/C][C] 2.32[/C][C] 1.08[/C][/ROW]
[ROW][C]77[/C][C] 3.4[/C][C] 4.358[/C][C]-0.9578[/C][/ROW]
[ROW][C]78[/C][C] 3.3[/C][C] 1.351[/C][C] 1.949[/C][/ROW]
[ROW][C]79[/C][C] 3.3[/C][C] 2.735[/C][C] 0.5645[/C][/ROW]
[ROW][C]80[/C][C] 3.3[/C][C] 1.471[/C][C] 1.829[/C][/ROW]
[ROW][C]81[/C][C] 3.3[/C][C] 2.651[/C][C] 0.649[/C][/ROW]
[ROW][C]82[/C][C] 3.2[/C][C] 1.994[/C][C] 1.206[/C][/ROW]
[ROW][C]83[/C][C] 3.2[/C][C] 2.102[/C][C] 1.098[/C][/ROW]
[ROW][C]84[/C][C] 3.2[/C][C] 2.846[/C][C] 0.3541[/C][/ROW]
[ROW][C]85[/C][C] 3.2[/C][C] 3.901[/C][C]-0.7015[/C][/ROW]
[ROW][C]86[/C][C] 3.2[/C][C] 1.529[/C][C] 1.671[/C][/ROW]
[ROW][C]87[/C][C] 3.2[/C][C] 2.977[/C][C] 0.2229[/C][/ROW]
[ROW][C]88[/C][C] 3.1[/C][C] 4.013[/C][C]-0.9129[/C][/ROW]
[ROW][C]89[/C][C] 3.1[/C][C] 2.384[/C][C] 0.7158[/C][/ROW]
[ROW][C]90[/C][C] 3.1[/C][C] 2.576[/C][C] 0.524[/C][/ROW]
[ROW][C]91[/C][C] 3.1[/C][C] 3.216[/C][C]-0.1162[/C][/ROW]
[ROW][C]92[/C][C] 3[/C][C] 4.564[/C][C]-1.564[/C][/ROW]
[ROW][C]93[/C][C] 3[/C][C] 0.995[/C][C] 2.005[/C][/ROW]
[ROW][C]94[/C][C] 3[/C][C] 2.43[/C][C] 0.5704[/C][/ROW]
[ROW][C]95[/C][C] 3[/C][C] 2.909[/C][C] 0.09083[/C][/ROW]
[ROW][C]96[/C][C] 2.9[/C][C] 4.069[/C][C]-1.169[/C][/ROW]
[ROW][C]97[/C][C] 2.9[/C][C] 2.494[/C][C] 0.4062[/C][/ROW]
[ROW][C]98[/C][C] 2.9[/C][C] 2.749[/C][C] 0.1508[/C][/ROW]
[ROW][C]99[/C][C] 2.8[/C][C] 4.805[/C][C]-2.005[/C][/ROW]
[ROW][C]100[/C][C] 2.8[/C][C] 1.018[/C][C] 1.782[/C][/ROW]
[ROW][C]101[/C][C] 2.8[/C][C] 2.98[/C][C]-0.1795[/C][/ROW]
[ROW][C]102[/C][C] 2.8[/C][C] 2.921[/C][C]-0.1209[/C][/ROW]
[ROW][C]103[/C][C] 2.8[/C][C] 2.385[/C][C] 0.4155[/C][/ROW]
[ROW][C]104[/C][C] 2.7[/C][C] 1.748[/C][C] 0.9516[/C][/ROW]
[ROW][C]105[/C][C] 2.7[/C][C] 1.216[/C][C] 1.484[/C][/ROW]
[ROW][C]106[/C][C] 2.7[/C][C] 4.732[/C][C]-2.032[/C][/ROW]
[ROW][C]107[/C][C] 2.6[/C][C] 4.137[/C][C]-1.537[/C][/ROW]
[ROW][C]108[/C][C] 2.6[/C][C] 2.236[/C][C] 0.364[/C][/ROW]
[ROW][C]109[/C][C] 2.6[/C][C] 3.137[/C][C]-0.5375[/C][/ROW]
[ROW][C]110[/C][C] 2.6[/C][C] 2.371[/C][C] 0.2288[/C][/ROW]
[ROW][C]111[/C][C] 2.6[/C][C] 1.746[/C][C] 0.8545[/C][/ROW]
[ROW][C]112[/C][C] 2.5[/C][C] 3.132[/C][C]-0.6324[/C][/ROW]
[ROW][C]113[/C][C] 2.5[/C][C] 2.897[/C][C]-0.3969[/C][/ROW]
[ROW][C]114[/C][C] 2.5[/C][C] 3.829[/C][C]-1.329[/C][/ROW]
[ROW][C]115[/C][C] 2.5[/C][C] 1.852[/C][C] 0.6475[/C][/ROW]
[ROW][C]116[/C][C] 2.4[/C][C] 2.164[/C][C] 0.2358[/C][/ROW]
[ROW][C]117[/C][C] 2.4[/C][C] 2.887[/C][C]-0.4868[/C][/ROW]
[ROW][C]118[/C][C] 2.3[/C][C] 2.63[/C][C]-0.3303[/C][/ROW]
[ROW][C]119[/C][C] 2.3[/C][C] 2.412[/C][C]-0.1118[/C][/ROW]
[ROW][C]120[/C][C] 2.3[/C][C] 3.378[/C][C]-1.078[/C][/ROW]
[ROW][C]121[/C][C] 2.3[/C][C] 1.324[/C][C] 0.9763[/C][/ROW]
[ROW][C]122[/C][C] 2.3[/C][C] 1.921[/C][C] 0.3794[/C][/ROW]
[ROW][C]123[/C][C] 2.3[/C][C] 3.018[/C][C]-0.7185[/C][/ROW]
[ROW][C]124[/C][C] 2.2[/C][C] 2.344[/C][C]-0.1445[/C][/ROW]
[ROW][C]125[/C][C] 2.2[/C][C] 3.413[/C][C]-1.213[/C][/ROW]
[ROW][C]126[/C][C] 2.2[/C][C] 2.673[/C][C]-0.4727[/C][/ROW]
[ROW][C]127[/C][C] 2.1[/C][C] 2.691[/C][C]-0.5907[/C][/ROW]
[ROW][C]128[/C][C] 2.1[/C][C] 4.068[/C][C]-1.968[/C][/ROW]
[ROW][C]129[/C][C] 2.1[/C][C] 2.531[/C][C]-0.4307[/C][/ROW]
[ROW][C]130[/C][C] 2.1[/C][C] 2.576[/C][C]-0.4764[/C][/ROW]
[ROW][C]131[/C][C] 2.1[/C][C] 2.912[/C][C]-0.8117[/C][/ROW]
[ROW][C]132[/C][C] 2.1[/C][C] 2.567[/C][C]-0.467[/C][/ROW]
[ROW][C]133[/C][C] 2[/C][C] 2.501[/C][C]-0.5005[/C][/ROW]
[ROW][C]134[/C][C] 2[/C][C] 1.772[/C][C] 0.2278[/C][/ROW]
[ROW][C]135[/C][C] 2[/C][C] 3.119[/C][C]-1.119[/C][/ROW]
[ROW][C]136[/C][C] 2[/C][C] 3.962[/C][C]-1.962[/C][/ROW]
[ROW][C]137[/C][C] 2[/C][C] 3.167[/C][C]-1.167[/C][/ROW]
[ROW][C]138[/C][C] 1.9[/C][C] 2.022[/C][C]-0.1218[/C][/ROW]
[ROW][C]139[/C][C] 1.9[/C][C] 3.241[/C][C]-1.341[/C][/ROW]
[ROW][C]140[/C][C] 1.9[/C][C] 2.163[/C][C]-0.2627[/C][/ROW]
[ROW][C]141[/C][C] 1.9[/C][C] 3.016[/C][C]-1.116[/C][/ROW]
[ROW][C]142[/C][C] 1.9[/C][C] 3.715[/C][C]-1.815[/C][/ROW]
[ROW][C]143[/C][C] 1.9[/C][C] 2.568[/C][C]-0.6684[/C][/ROW]
[ROW][C]144[/C][C] 1.9[/C][C] 3.252[/C][C]-1.352[/C][/ROW]
[ROW][C]145[/C][C] 1.9[/C][C] 2.266[/C][C]-0.3658[/C][/ROW]
[ROW][C]146[/C][C] 1.9[/C][C] 2.214[/C][C]-0.3145[/C][/ROW]
[ROW][C]147[/C][C] 1.7[/C][C] 3.355[/C][C]-1.655[/C][/ROW]
[ROW][C]148[/C][C] 1.6[/C][C] 2.944[/C][C]-1.344[/C][/ROW]
[ROW][C]149[/C][C] 1.6[/C][C] 3.747[/C][C]-2.147[/C][/ROW]
[ROW][C]150[/C][C] 1.6[/C][C] 1.587[/C][C] 0.01257[/C][/ROW]
[ROW][C]151[/C][C] 1.6[/C][C] 3.073[/C][C]-1.473[/C][/ROW]
[ROW][C]152[/C][C] 1.6[/C][C] 2.544[/C][C]-0.9437[/C][/ROW]
[ROW][C]153[/C][C] 1.6[/C][C] 2.492[/C][C]-0.8916[/C][/ROW]
[ROW][C]154[/C][C] 1.6[/C][C] 3.022[/C][C]-1.422[/C][/ROW]
[ROW][C]155[/C][C] 1.5[/C][C] 2.378[/C][C]-0.8782[/C][/ROW]
[ROW][C]156[/C][C] 1.4[/C][C] 1.543[/C][C]-0.1434[/C][/ROW]
[ROW][C]157[/C][C] 1.4[/C][C] 2.385[/C][C]-0.9852[/C][/ROW]
[ROW][C]158[/C][C] 1.4[/C][C] 3.981[/C][C]-2.581[/C][/ROW]
[ROW][C]159[/C][C] 1.4[/C][C] 3.225[/C][C]-1.825[/C][/ROW]
[ROW][C]160[/C][C] 1.3[/C][C] 2.641[/C][C]-1.341[/C][/ROW]
[ROW][C]161[/C][C] 1.3[/C][C] 4.219[/C][C]-2.919[/C][/ROW]
[ROW][C]162[/C][C] 1.3[/C][C] 2.459[/C][C]-1.159[/C][/ROW]
[ROW][C]163[/C][C] 1.3[/C][C] 3.994[/C][C]-2.694[/C][/ROW]
[ROW][C]164[/C][C] 1.2[/C][C] 2.698[/C][C]-1.498[/C][/ROW]
[ROW][C]165[/C][C] 1.1[/C][C] 3.042[/C][C]-1.942[/C][/ROW]
[ROW][C]166[/C][C] 1.1[/C][C] 1.921[/C][C]-0.8213[/C][/ROW]
[ROW][C]167[/C][C] 1.1[/C][C] 1.429[/C][C]-0.329[/C][/ROW]
[ROW][C]168[/C][C] 1[/C][C] 1.955[/C][C]-0.955[/C][/ROW]
[ROW][C]169[/C][C] 1[/C][C] 1.377[/C][C]-0.3767[/C][/ROW]
[ROW][C]170[/C][C] 0.9[/C][C] 3.171[/C][C]-2.271[/C][/ROW]
[ROW][C]171[/C][C] 0.8[/C][C] 1.982[/C][C]-1.182[/C][/ROW]
[ROW][C]172[/C][C] 0.7[/C][C] 2.913[/C][C]-2.213[/C][/ROW]
[ROW][C]173[/C][C] 0.6[/C][C] 3.276[/C][C]-2.676[/C][/ROW]
[ROW][C]174[/C][C] 0.5[/C][C] 3.21[/C][C]-2.71[/C][/ROW]
[ROW][C]175[/C][C] 0.5[/C][C] 1.823[/C][C]-1.323[/C][/ROW]
[ROW][C]176[/C][C] 0.5[/C][C] 1.596[/C][C]-1.096[/C][/ROW]
[ROW][C]177[/C][C] 0.4[/C][C] 3.756[/C][C]-3.357[/C][/ROW]
[ROW][C]178[/C][C] 0.4[/C][C] 3.414[/C][C]-3.014[/C][/ROW]
[ROW][C]179[/C][C] 0.4[/C][C] 2.194[/C][C]-1.794[/C][/ROW]
[ROW][C]180[/C][C] 0.4[/C][C] 2.613[/C][C]-2.213[/C][/ROW]
[ROW][C]181[/C][C] 0.4[/C][C] 2.148[/C][C]-1.748[/C][/ROW]
[ROW][C]182[/C][C] 0.4[/C][C] 2.201[/C][C]-1.801[/C][/ROW]
[ROW][C]183[/C][C] 0.2[/C][C] 1.993[/C][C]-1.793[/C][/ROW]
[ROW][C]184[/C][C] 0.1[/C][C] 2.583[/C][C]-2.483[/C][/ROW]
[ROW][C]185[/C][C]-0.1[/C][C] 2.888[/C][C]-2.988[/C][/ROW]
[ROW][C]186[/C][C]-0.2[/C][C] 2.091[/C][C]-2.291[/C][/ROW]
[ROW][C]187[/C][C]-0.2[/C][C] 2.462[/C][C]-2.662[/C][/ROW]
[ROW][C]188[/C][C]-0.8[/C][C] 1.505[/C][C]-2.305[/C][/ROW]
[ROW][C]189[/C][C]-1.1[/C][C] 2.045[/C][C]-3.145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=5

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	9.9	5.265	4.635
2	8.9	4.005	4.895
3	8.9	5.377	3.523
4	7.6	5.127	2.473
5	7.1	4.844	2.256
6	7.1	5.659	1.441
7	7	5.598	1.402
8	7	4.121	2.879
9	6.9	5.53	1.37
10	6.8	5.805	0.9953
11	6.6	4.793	1.807
12	6.4	4.891	1.509
13	6.3	5.031	1.269
14	6.3	2.404	3.896
15	6.2	6.045	0.1552
16	6	4.973	1.027
17	5.6	5.821	-0.2212
18	5.5	5.806	-0.3058
19	5.4	4.827	0.5727
20	5.4	3.508	1.892
21	5.3	4.714	0.5863
22	5.2	2.328	2.872
23	5.1	4.726	0.3745
24	5	2.781	2.219
25	5	2.958	2.042
26	5	4.203	0.7972
27	5	4.558	0.4424
28	5	3.257	1.743
29	4.7	3.642	1.058
30	4.7	3.268	1.432
31	4.6	2.41	2.19
32	4.6	4.128	0.4719
33	4.6	3.083	1.517
34	4.5	2.865	1.635
35	4.5	3.567	0.9335
36	4.4	1.901	2.499
37	4.4	1.76	2.64
38	4.4	4.77	-0.37
39	4.4	3.538	0.8625
40	4.3	2.575	1.725
41	4.3	2.415	1.885
42	4.3	2.755	1.545
43	4.3	2.091	2.209
44	4.3	2.516	1.784
45	4.2	2.403	1.797
46	4.2	2.656	1.544
47	4.2	4.477	-0.2773
48	4.1	2.859	1.241
49	4.1	3.48	0.62
50	4.1	2.916	1.184
51	4.1	4.413	-0.3134
52	4.1	3.752	0.3479
53	4	3.89	0.1096
54	4	3.761	0.2385
55	4	2.543	1.457
56	4	2.69	1.31
57	4	3.579	0.421
58	3.9	4.479	-0.579
59	3.8	2.929	0.8705
60	3.8	2.904	0.8962
61	3.8	3.916	-0.1157
62	3.8	5.221	-1.421
63	3.7	2.781	0.9188
64	3.7	2.318	1.382
65	3.7	2.958	0.7421
66	3.7	3.532	0.1679
67	3.7	3.49	0.2096
68	3.7	4.509	-0.8095
69	3.7	3.695	0.00491
70	3.6	3.176	0.4241
71	3.6	4.496	-0.8959
72	3.6	2.949	0.6511
73	3.6	2.514	1.086
74	3.5	2.082	1.418
75	3.5	3.357	0.1427
76	3.4	2.32	1.08
77	3.4	4.358	-0.9578
78	3.3	1.351	1.949
79	3.3	2.735	0.5645
80	3.3	1.471	1.829
81	3.3	2.651	0.649
82	3.2	1.994	1.206
83	3.2	2.102	1.098
84	3.2	2.846	0.3541
85	3.2	3.901	-0.7015
86	3.2	1.529	1.671
87	3.2	2.977	0.2229
88	3.1	4.013	-0.9129
89	3.1	2.384	0.7158
90	3.1	2.576	0.524
91	3.1	3.216	-0.1162
92	3	4.564	-1.564
93	3	0.995	2.005
94	3	2.43	0.5704
95	3	2.909	0.09083
96	2.9	4.069	-1.169
97	2.9	2.494	0.4062
98	2.9	2.749	0.1508
99	2.8	4.805	-2.005
100	2.8	1.018	1.782
101	2.8	2.98	-0.1795
102	2.8	2.921	-0.1209
103	2.8	2.385	0.4155
104	2.7	1.748	0.9516
105	2.7	1.216	1.484
106	2.7	4.732	-2.032
107	2.6	4.137	-1.537
108	2.6	2.236	0.364
109	2.6	3.137	-0.5375
110	2.6	2.371	0.2288
111	2.6	1.746	0.8545
112	2.5	3.132	-0.6324
113	2.5	2.897	-0.3969
114	2.5	3.829	-1.329
115	2.5	1.852	0.6475
116	2.4	2.164	0.2358
117	2.4	2.887	-0.4868
118	2.3	2.63	-0.3303
119	2.3	2.412	-0.1118
120	2.3	3.378	-1.078
121	2.3	1.324	0.9763
122	2.3	1.921	0.3794
123	2.3	3.018	-0.7185
124	2.2	2.344	-0.1445
125	2.2	3.413	-1.213
126	2.2	2.673	-0.4727
127	2.1	2.691	-0.5907
128	2.1	4.068	-1.968
129	2.1	2.531	-0.4307
130	2.1	2.576	-0.4764
131	2.1	2.912	-0.8117
132	2.1	2.567	-0.467
133	2	2.501	-0.5005
134	2	1.772	0.2278
135	2	3.119	-1.119
136	2	3.962	-1.962
137	2	3.167	-1.167
138	1.9	2.022	-0.1218
139	1.9	3.241	-1.341
140	1.9	2.163	-0.2627
141	1.9	3.016	-1.116
142	1.9	3.715	-1.815
143	1.9	2.568	-0.6684
144	1.9	3.252	-1.352
145	1.9	2.266	-0.3658
146	1.9	2.214	-0.3145
147	1.7	3.355	-1.655
148	1.6	2.944	-1.344
149	1.6	3.747	-2.147
150	1.6	1.587	0.01257
151	1.6	3.073	-1.473
152	1.6	2.544	-0.9437
153	1.6	2.492	-0.8916
154	1.6	3.022	-1.422
155	1.5	2.378	-0.8782
156	1.4	1.543	-0.1434
157	1.4	2.385	-0.9852
158	1.4	3.981	-2.581
159	1.4	3.225	-1.825
160	1.3	2.641	-1.341
161	1.3	4.219	-2.919
162	1.3	2.459	-1.159
163	1.3	3.994	-2.694
164	1.2	2.698	-1.498
165	1.1	3.042	-1.942
166	1.1	1.921	-0.8213
167	1.1	1.429	-0.329
168	1	1.955	-0.955
169	1	1.377	-0.3767
170	0.9	3.171	-2.271
171	0.8	1.982	-1.182
172	0.7	2.913	-2.213
173	0.6	3.276	-2.676
174	0.5	3.21	-2.71
175	0.5	1.823	-1.323
176	0.5	1.596	-1.096
177	0.4	3.756	-3.357
178	0.4	3.414	-3.014
179	0.4	2.194	-1.794
180	0.4	2.613	-2.213
181	0.4	2.148	-1.748
182	0.4	2.201	-1.801
183	0.2	1.993	-1.793
184	0.1	2.583	-2.483
185	-0.1	2.888	-2.988
186	-0.2	2.091	-2.291
187	-0.2	2.462	-2.662
188	-0.8	1.505	-2.305
189	-1.1	2.045	-3.145

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.4678	0.9356	0.5322
12	0.3333	0.6667	0.6667
13	0.2113	0.4226	0.7887
14	0.1343	0.2687	0.8657
15	0.4249	0.8498	0.5751
16	0.3361	0.6722	0.6639
17	0.3391	0.6782	0.6609
18	0.2944	0.5888	0.7056
19	0.224	0.4479	0.776
20	0.2414	0.4828	0.7586
21	0.3026	0.6051	0.6974
22	0.2578	0.5157	0.7422
23	0.3457	0.6914	0.6543
24	0.3719	0.7438	0.6281
25	0.4941	0.9881	0.5059
26	0.4877	0.9754	0.5123
27	0.4935	0.9871	0.5065
28	0.4627	0.9254	0.5373
29	0.4915	0.983	0.5085
30	0.5305	0.9389	0.4695
31	0.5041	0.9919	0.4959
32	0.4866	0.9731	0.5134
33	0.4927	0.9854	0.5073
34	0.4508	0.9017	0.5492
35	0.4197	0.8393	0.5803
36	0.4285	0.857	0.5715
37	0.4051	0.8103	0.5949
38	0.4606	0.9212	0.5394
39	0.4373	0.8747	0.5627
40	0.4254	0.8509	0.5746
41	0.3987	0.7974	0.6013
42	0.3958	0.7916	0.6042
43	0.4248	0.8495	0.5752
44	0.4094	0.8187	0.5906
45	0.435	0.87	0.565
46	0.4151	0.8302	0.5849
47	0.4642	0.9285	0.5358
48	0.4655	0.931	0.5345
49	0.4713	0.9427	0.5287
50	0.5035	0.9931	0.4965
51	0.5503	0.8994	0.4497
52	0.5607	0.8786	0.4393
53	0.5802	0.8395	0.4198
54	0.6298	0.7404	0.3702
55	0.6318	0.7365	0.3682
56	0.6123	0.7753	0.3877
57	0.5979	0.8042	0.4021
58	0.6175	0.765	0.3825
59	0.6175	0.765	0.3825
60	0.6043	0.7915	0.3957
61	0.6162	0.7677	0.3838
62	0.7569	0.4862	0.2431
63	0.7479	0.5041	0.2521
64	0.7359	0.5282	0.2641
65	0.7638	0.4725	0.2362
66	0.7701	0.4599	0.2299
67	0.81	0.3801	0.19
68	0.8273	0.3453	0.1727
69	0.8259	0.3481	0.1741
70	0.8649	0.2701	0.1351
71	0.8965	0.2069	0.1035
72	0.9009	0.1982	0.09911
73	0.8972	0.2055	0.1028
74	0.9038	0.1924	0.09622
75	0.9151	0.1698	0.08489
76	0.9241	0.1519	0.07594
77	0.9339	0.1322	0.06611
78	0.932	0.1359	0.06796
79	0.9327	0.1345	0.06727
80	0.93	0.1399	0.06997
81	0.942	0.1161	0.05804
82	0.9423	0.1154	0.05771
83	0.9379	0.1241	0.06206
84	0.9377	0.1246	0.06229
85	0.9372	0.1256	0.0628
86	0.9423	0.1153	0.05767
87	0.938	0.1239	0.06195
88	0.9474	0.1053	0.05263
89	0.9455	0.109	0.05451
90	0.9507	0.09864	0.04932
91	0.9597	0.08064	0.04032
92	0.9697	0.06057	0.03029
93	0.9723	0.05538	0.02769
94	0.9732	0.05361	0.0268
95	0.9761	0.04779	0.02389
96	0.9828	0.03443	0.01722
97	0.9838	0.0325	0.01625
98	0.9839	0.03219	0.0161
99	0.9889	0.02214	0.01107
100	0.9912	0.01769	0.008845
101	0.9923	0.01532	0.007658
102	0.9932	0.01357	0.006785
103	0.9942	0.01167	0.005833
104	0.9958	0.008422	0.004211
105	0.9965	0.007065	0.003533
106	0.9975	0.004951	0.002475
107	0.9977	0.004531	0.002266
108	0.9977	0.004623	0.002312
109	0.9979	0.004153	0.002077
110	0.9978	0.004317	0.002158
111	0.9986	0.002723	0.001362
112	0.999	0.0021	0.00105
113	0.999	0.001965	0.0009825
114	0.9993	0.001425	0.0007127
115	0.9994	0.001196	0.0005982
116	0.9995	0.0009431	0.0004715
117	0.9996	0.0007633	0.0003816
118	0.9996	0.0007338	0.0003669
119	0.9996	0.0007738	0.0003869
120	0.9996	0.0007616	0.0003808
121	0.9996	0.0008119	0.0004059
122	0.9997	0.0006565	0.0003283
123	0.9997	0.0005994	0.0002997
124	0.9997	0.0006498	0.0003249
125	0.9997	0.000604	0.000302
126	0.9998	0.000469	0.0002345
127	0.9998	0.0003282	0.0001641
128	0.9998	0.0003097	0.0001549
129	0.9999	0.000236	0.000118
130	0.9999	0.0002328	0.0001164
131	0.9999	0.0002322	0.0001161
132	0.9999	0.0001659	8.294e-05
133	0.9999	0.0001796	8.98e-05
134	0.9999	0.0001687	8.436e-05
135	0.9999	0.0001567	7.836e-05
136	1	9.639e-05	4.819e-05
137	0.9999	0.000111	5.548e-05
138	1	9.634e-05	4.817e-05
139	1	9.779e-05	4.89e-05
140	1	9.17e-05	4.585e-05
141	1	8.039e-05	4.019e-05
142	1	8.409e-05	4.204e-05
143	1	8.101e-05	4.05e-05
144	1	5.201e-05	2.601e-05
145	1	5.857e-05	2.929e-05
146	1	5.249e-05	2.624e-05
147	1	6.209e-05	3.104e-05
148	1	5.322e-05	2.661e-05
149	1	6.847e-05	3.424e-05
150	0.9999	0.0001034	5.169e-05
151	0.9999	0.0001199	5.995e-05
152	1	9.373e-05	4.686e-05
153	0.9999	0.0001162	5.808e-05
154	0.9999	0.0001073	5.364e-05
155	0.9999	0.0001242	6.208e-05
156	0.9999	0.0001155	5.774e-05
157	1	9.236e-05	4.618e-05
158	1	9.971e-05	4.986e-05
159	0.9999	0.0001276	6.38e-05
160	0.9999	0.0001706	8.528e-05
161	0.9999	0.0002653	0.0001327
162	0.9998	0.0003472	0.0001736
163	0.9999	0.0002265	0.0001132
164	0.9999	0.0002011	0.0001006
165	0.9998	0.0004038	0.0002019
166	0.9998	0.0004748	0.0002374
167	0.9996	0.0008355	0.0004177
168	0.9997	0.0005766	0.0002883
169	0.9997	0.0005052	0.0002526
170	0.9998	0.0003595	0.0001798
171	0.9996	0.0007842	0.0003921
172	0.9991	0.001883	0.0009413
173	0.9975	0.005025	0.002512
174	0.9936	0.01279	0.006396
175	0.9919	0.01629	0.008143
176	0.9809	0.03812	0.01906
177	0.955	0.08993	0.04496
178	0.892	0.2161	0.108

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.4678 &  0.9356 &  0.5322 \tabularnewline
12 &  0.3333 &  0.6667 &  0.6667 \tabularnewline
13 &  0.2113 &  0.4226 &  0.7887 \tabularnewline
14 &  0.1343 &  0.2687 &  0.8657 \tabularnewline
15 &  0.4249 &  0.8498 &  0.5751 \tabularnewline
16 &  0.3361 &  0.6722 &  0.6639 \tabularnewline
17 &  0.3391 &  0.6782 &  0.6609 \tabularnewline
18 &  0.2944 &  0.5888 &  0.7056 \tabularnewline
19 &  0.224 &  0.4479 &  0.776 \tabularnewline
20 &  0.2414 &  0.4828 &  0.7586 \tabularnewline
21 &  0.3026 &  0.6051 &  0.6974 \tabularnewline
22 &  0.2578 &  0.5157 &  0.7422 \tabularnewline
23 &  0.3457 &  0.6914 &  0.6543 \tabularnewline
24 &  0.3719 &  0.7438 &  0.6281 \tabularnewline
25 &  0.4941 &  0.9881 &  0.5059 \tabularnewline
26 &  0.4877 &  0.9754 &  0.5123 \tabularnewline
27 &  0.4935 &  0.9871 &  0.5065 \tabularnewline
28 &  0.4627 &  0.9254 &  0.5373 \tabularnewline
29 &  0.4915 &  0.983 &  0.5085 \tabularnewline
30 &  0.5305 &  0.9389 &  0.4695 \tabularnewline
31 &  0.5041 &  0.9919 &  0.4959 \tabularnewline
32 &  0.4866 &  0.9731 &  0.5134 \tabularnewline
33 &  0.4927 &  0.9854 &  0.5073 \tabularnewline
34 &  0.4508 &  0.9017 &  0.5492 \tabularnewline
35 &  0.4197 &  0.8393 &  0.5803 \tabularnewline
36 &  0.4285 &  0.857 &  0.5715 \tabularnewline
37 &  0.4051 &  0.8103 &  0.5949 \tabularnewline
38 &  0.4606 &  0.9212 &  0.5394 \tabularnewline
39 &  0.4373 &  0.8747 &  0.5627 \tabularnewline
40 &  0.4254 &  0.8509 &  0.5746 \tabularnewline
41 &  0.3987 &  0.7974 &  0.6013 \tabularnewline
42 &  0.3958 &  0.7916 &  0.6042 \tabularnewline
43 &  0.4248 &  0.8495 &  0.5752 \tabularnewline
44 &  0.4094 &  0.8187 &  0.5906 \tabularnewline
45 &  0.435 &  0.87 &  0.565 \tabularnewline
46 &  0.4151 &  0.8302 &  0.5849 \tabularnewline
47 &  0.4642 &  0.9285 &  0.5358 \tabularnewline
48 &  0.4655 &  0.931 &  0.5345 \tabularnewline
49 &  0.4713 &  0.9427 &  0.5287 \tabularnewline
50 &  0.5035 &  0.9931 &  0.4965 \tabularnewline
51 &  0.5503 &  0.8994 &  0.4497 \tabularnewline
52 &  0.5607 &  0.8786 &  0.4393 \tabularnewline
53 &  0.5802 &  0.8395 &  0.4198 \tabularnewline
54 &  0.6298 &  0.7404 &  0.3702 \tabularnewline
55 &  0.6318 &  0.7365 &  0.3682 \tabularnewline
56 &  0.6123 &  0.7753 &  0.3877 \tabularnewline
57 &  0.5979 &  0.8042 &  0.4021 \tabularnewline
58 &  0.6175 &  0.765 &  0.3825 \tabularnewline
59 &  0.6175 &  0.765 &  0.3825 \tabularnewline
60 &  0.6043 &  0.7915 &  0.3957 \tabularnewline
61 &  0.6162 &  0.7677 &  0.3838 \tabularnewline
62 &  0.7569 &  0.4862 &  0.2431 \tabularnewline
63 &  0.7479 &  0.5041 &  0.2521 \tabularnewline
64 &  0.7359 &  0.5282 &  0.2641 \tabularnewline
65 &  0.7638 &  0.4725 &  0.2362 \tabularnewline
66 &  0.7701 &  0.4599 &  0.2299 \tabularnewline
67 &  0.81 &  0.3801 &  0.19 \tabularnewline
68 &  0.8273 &  0.3453 &  0.1727 \tabularnewline
69 &  0.8259 &  0.3481 &  0.1741 \tabularnewline
70 &  0.8649 &  0.2701 &  0.1351 \tabularnewline
71 &  0.8965 &  0.2069 &  0.1035 \tabularnewline
72 &  0.9009 &  0.1982 &  0.09911 \tabularnewline
73 &  0.8972 &  0.2055 &  0.1028 \tabularnewline
74 &  0.9038 &  0.1924 &  0.09622 \tabularnewline
75 &  0.9151 &  0.1698 &  0.08489 \tabularnewline
76 &  0.9241 &  0.1519 &  0.07594 \tabularnewline
77 &  0.9339 &  0.1322 &  0.06611 \tabularnewline
78 &  0.932 &  0.1359 &  0.06796 \tabularnewline
79 &  0.9327 &  0.1345 &  0.06727 \tabularnewline
80 &  0.93 &  0.1399 &  0.06997 \tabularnewline
81 &  0.942 &  0.1161 &  0.05804 \tabularnewline
82 &  0.9423 &  0.1154 &  0.05771 \tabularnewline
83 &  0.9379 &  0.1241 &  0.06206 \tabularnewline
84 &  0.9377 &  0.1246 &  0.06229 \tabularnewline
85 &  0.9372 &  0.1256 &  0.0628 \tabularnewline
86 &  0.9423 &  0.1153 &  0.05767 \tabularnewline
87 &  0.938 &  0.1239 &  0.06195 \tabularnewline
88 &  0.9474 &  0.1053 &  0.05263 \tabularnewline
89 &  0.9455 &  0.109 &  0.05451 \tabularnewline
90 &  0.9507 &  0.09864 &  0.04932 \tabularnewline
91 &  0.9597 &  0.08064 &  0.04032 \tabularnewline
92 &  0.9697 &  0.06057 &  0.03029 \tabularnewline
93 &  0.9723 &  0.05538 &  0.02769 \tabularnewline
94 &  0.9732 &  0.05361 &  0.0268 \tabularnewline
95 &  0.9761 &  0.04779 &  0.02389 \tabularnewline
96 &  0.9828 &  0.03443 &  0.01722 \tabularnewline
97 &  0.9838 &  0.0325 &  0.01625 \tabularnewline
98 &  0.9839 &  0.03219 &  0.0161 \tabularnewline
99 &  0.9889 &  0.02214 &  0.01107 \tabularnewline
100 &  0.9912 &  0.01769 &  0.008845 \tabularnewline
101 &  0.9923 &  0.01532 &  0.007658 \tabularnewline
102 &  0.9932 &  0.01357 &  0.006785 \tabularnewline
103 &  0.9942 &  0.01167 &  0.005833 \tabularnewline
104 &  0.9958 &  0.008422 &  0.004211 \tabularnewline
105 &  0.9965 &  0.007065 &  0.003533 \tabularnewline
106 &  0.9975 &  0.004951 &  0.002475 \tabularnewline
107 &  0.9977 &  0.004531 &  0.002266 \tabularnewline
108 &  0.9977 &  0.004623 &  0.002312 \tabularnewline
109 &  0.9979 &  0.004153 &  0.002077 \tabularnewline
110 &  0.9978 &  0.004317 &  0.002158 \tabularnewline
111 &  0.9986 &  0.002723 &  0.001362 \tabularnewline
112 &  0.999 &  0.0021 &  0.00105 \tabularnewline
113 &  0.999 &  0.001965 &  0.0009825 \tabularnewline
114 &  0.9993 &  0.001425 &  0.0007127 \tabularnewline
115 &  0.9994 &  0.001196 &  0.0005982 \tabularnewline
116 &  0.9995 &  0.0009431 &  0.0004715 \tabularnewline
117 &  0.9996 &  0.0007633 &  0.0003816 \tabularnewline
118 &  0.9996 &  0.0007338 &  0.0003669 \tabularnewline
119 &  0.9996 &  0.0007738 &  0.0003869 \tabularnewline
120 &  0.9996 &  0.0007616 &  0.0003808 \tabularnewline
121 &  0.9996 &  0.0008119 &  0.0004059 \tabularnewline
122 &  0.9997 &  0.0006565 &  0.0003283 \tabularnewline
123 &  0.9997 &  0.0005994 &  0.0002997 \tabularnewline
124 &  0.9997 &  0.0006498 &  0.0003249 \tabularnewline
125 &  0.9997 &  0.000604 &  0.000302 \tabularnewline
126 &  0.9998 &  0.000469 &  0.0002345 \tabularnewline
127 &  0.9998 &  0.0003282 &  0.0001641 \tabularnewline
128 &  0.9998 &  0.0003097 &  0.0001549 \tabularnewline
129 &  0.9999 &  0.000236 &  0.000118 \tabularnewline
130 &  0.9999 &  0.0002328 &  0.0001164 \tabularnewline
131 &  0.9999 &  0.0002322 &  0.0001161 \tabularnewline
132 &  0.9999 &  0.0001659 &  8.294e-05 \tabularnewline
133 &  0.9999 &  0.0001796 &  8.98e-05 \tabularnewline
134 &  0.9999 &  0.0001687 &  8.436e-05 \tabularnewline
135 &  0.9999 &  0.0001567 &  7.836e-05 \tabularnewline
136 &  1 &  9.639e-05 &  4.819e-05 \tabularnewline
137 &  0.9999 &  0.000111 &  5.548e-05 \tabularnewline
138 &  1 &  9.634e-05 &  4.817e-05 \tabularnewline
139 &  1 &  9.779e-05 &  4.89e-05 \tabularnewline
140 &  1 &  9.17e-05 &  4.585e-05 \tabularnewline
141 &  1 &  8.039e-05 &  4.019e-05 \tabularnewline
142 &  1 &  8.409e-05 &  4.204e-05 \tabularnewline
143 &  1 &  8.101e-05 &  4.05e-05 \tabularnewline
144 &  1 &  5.201e-05 &  2.601e-05 \tabularnewline
145 &  1 &  5.857e-05 &  2.929e-05 \tabularnewline
146 &  1 &  5.249e-05 &  2.624e-05 \tabularnewline
147 &  1 &  6.209e-05 &  3.104e-05 \tabularnewline
148 &  1 &  5.322e-05 &  2.661e-05 \tabularnewline
149 &  1 &  6.847e-05 &  3.424e-05 \tabularnewline
150 &  0.9999 &  0.0001034 &  5.169e-05 \tabularnewline
151 &  0.9999 &  0.0001199 &  5.995e-05 \tabularnewline
152 &  1 &  9.373e-05 &  4.686e-05 \tabularnewline
153 &  0.9999 &  0.0001162 &  5.808e-05 \tabularnewline
154 &  0.9999 &  0.0001073 &  5.364e-05 \tabularnewline
155 &  0.9999 &  0.0001242 &  6.208e-05 \tabularnewline
156 &  0.9999 &  0.0001155 &  5.774e-05 \tabularnewline
157 &  1 &  9.236e-05 &  4.618e-05 \tabularnewline
158 &  1 &  9.971e-05 &  4.986e-05 \tabularnewline
159 &  0.9999 &  0.0001276 &  6.38e-05 \tabularnewline
160 &  0.9999 &  0.0001706 &  8.528e-05 \tabularnewline
161 &  0.9999 &  0.0002653 &  0.0001327 \tabularnewline
162 &  0.9998 &  0.0003472 &  0.0001736 \tabularnewline
163 &  0.9999 &  0.0002265 &  0.0001132 \tabularnewline
164 &  0.9999 &  0.0002011 &  0.0001006 \tabularnewline
165 &  0.9998 &  0.0004038 &  0.0002019 \tabularnewline
166 &  0.9998 &  0.0004748 &  0.0002374 \tabularnewline
167 &  0.9996 &  0.0008355 &  0.0004177 \tabularnewline
168 &  0.9997 &  0.0005766 &  0.0002883 \tabularnewline
169 &  0.9997 &  0.0005052 &  0.0002526 \tabularnewline
170 &  0.9998 &  0.0003595 &  0.0001798 \tabularnewline
171 &  0.9996 &  0.0007842 &  0.0003921 \tabularnewline
172 &  0.9991 &  0.001883 &  0.0009413 \tabularnewline
173 &  0.9975 &  0.005025 &  0.002512 \tabularnewline
174 &  0.9936 &  0.01279 &  0.006396 \tabularnewline
175 &  0.9919 &  0.01629 &  0.008143 \tabularnewline
176 &  0.9809 &  0.03812 &  0.01906 \tabularnewline
177 &  0.955 &  0.08993 &  0.04496 \tabularnewline
178 &  0.892 &  0.2161 &  0.108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=6

[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] 0.4678[/C][C] 0.9356[/C][C] 0.5322[/C][/ROW]
[ROW][C]12[/C][C] 0.3333[/C][C] 0.6667[/C][C] 0.6667[/C][/ROW]
[ROW][C]13[/C][C] 0.2113[/C][C] 0.4226[/C][C] 0.7887[/C][/ROW]
[ROW][C]14[/C][C] 0.1343[/C][C] 0.2687[/C][C] 0.8657[/C][/ROW]
[ROW][C]15[/C][C] 0.4249[/C][C] 0.8498[/C][C] 0.5751[/C][/ROW]
[ROW][C]16[/C][C] 0.3361[/C][C] 0.6722[/C][C] 0.6639[/C][/ROW]
[ROW][C]17[/C][C] 0.3391[/C][C] 0.6782[/C][C] 0.6609[/C][/ROW]
[ROW][C]18[/C][C] 0.2944[/C][C] 0.5888[/C][C] 0.7056[/C][/ROW]
[ROW][C]19[/C][C] 0.224[/C][C] 0.4479[/C][C] 0.776[/C][/ROW]
[ROW][C]20[/C][C] 0.2414[/C][C] 0.4828[/C][C] 0.7586[/C][/ROW]
[ROW][C]21[/C][C] 0.3026[/C][C] 0.6051[/C][C] 0.6974[/C][/ROW]
[ROW][C]22[/C][C] 0.2578[/C][C] 0.5157[/C][C] 0.7422[/C][/ROW]
[ROW][C]23[/C][C] 0.3457[/C][C] 0.6914[/C][C] 0.6543[/C][/ROW]
[ROW][C]24[/C][C] 0.3719[/C][C] 0.7438[/C][C] 0.6281[/C][/ROW]
[ROW][C]25[/C][C] 0.4941[/C][C] 0.9881[/C][C] 0.5059[/C][/ROW]
[ROW][C]26[/C][C] 0.4877[/C][C] 0.9754[/C][C] 0.5123[/C][/ROW]
[ROW][C]27[/C][C] 0.4935[/C][C] 0.9871[/C][C] 0.5065[/C][/ROW]
[ROW][C]28[/C][C] 0.4627[/C][C] 0.9254[/C][C] 0.5373[/C][/ROW]
[ROW][C]29[/C][C] 0.4915[/C][C] 0.983[/C][C] 0.5085[/C][/ROW]
[ROW][C]30[/C][C] 0.5305[/C][C] 0.9389[/C][C] 0.4695[/C][/ROW]
[ROW][C]31[/C][C] 0.5041[/C][C] 0.9919[/C][C] 0.4959[/C][/ROW]
[ROW][C]32[/C][C] 0.4866[/C][C] 0.9731[/C][C] 0.5134[/C][/ROW]
[ROW][C]33[/C][C] 0.4927[/C][C] 0.9854[/C][C] 0.5073[/C][/ROW]
[ROW][C]34[/C][C] 0.4508[/C][C] 0.9017[/C][C] 0.5492[/C][/ROW]
[ROW][C]35[/C][C] 0.4197[/C][C] 0.8393[/C][C] 0.5803[/C][/ROW]
[ROW][C]36[/C][C] 0.4285[/C][C] 0.857[/C][C] 0.5715[/C][/ROW]
[ROW][C]37[/C][C] 0.4051[/C][C] 0.8103[/C][C] 0.5949[/C][/ROW]
[ROW][C]38[/C][C] 0.4606[/C][C] 0.9212[/C][C] 0.5394[/C][/ROW]
[ROW][C]39[/C][C] 0.4373[/C][C] 0.8747[/C][C] 0.5627[/C][/ROW]
[ROW][C]40[/C][C] 0.4254[/C][C] 0.8509[/C][C] 0.5746[/C][/ROW]
[ROW][C]41[/C][C] 0.3987[/C][C] 0.7974[/C][C] 0.6013[/C][/ROW]
[ROW][C]42[/C][C] 0.3958[/C][C] 0.7916[/C][C] 0.6042[/C][/ROW]
[ROW][C]43[/C][C] 0.4248[/C][C] 0.8495[/C][C] 0.5752[/C][/ROW]
[ROW][C]44[/C][C] 0.4094[/C][C] 0.8187[/C][C] 0.5906[/C][/ROW]
[ROW][C]45[/C][C] 0.435[/C][C] 0.87[/C][C] 0.565[/C][/ROW]
[ROW][C]46[/C][C] 0.4151[/C][C] 0.8302[/C][C] 0.5849[/C][/ROW]
[ROW][C]47[/C][C] 0.4642[/C][C] 0.9285[/C][C] 0.5358[/C][/ROW]
[ROW][C]48[/C][C] 0.4655[/C][C] 0.931[/C][C] 0.5345[/C][/ROW]
[ROW][C]49[/C][C] 0.4713[/C][C] 0.9427[/C][C] 0.5287[/C][/ROW]
[ROW][C]50[/C][C] 0.5035[/C][C] 0.9931[/C][C] 0.4965[/C][/ROW]
[ROW][C]51[/C][C] 0.5503[/C][C] 0.8994[/C][C] 0.4497[/C][/ROW]
[ROW][C]52[/C][C] 0.5607[/C][C] 0.8786[/C][C] 0.4393[/C][/ROW]
[ROW][C]53[/C][C] 0.5802[/C][C] 0.8395[/C][C] 0.4198[/C][/ROW]
[ROW][C]54[/C][C] 0.6298[/C][C] 0.7404[/C][C] 0.3702[/C][/ROW]
[ROW][C]55[/C][C] 0.6318[/C][C] 0.7365[/C][C] 0.3682[/C][/ROW]
[ROW][C]56[/C][C] 0.6123[/C][C] 0.7753[/C][C] 0.3877[/C][/ROW]
[ROW][C]57[/C][C] 0.5979[/C][C] 0.8042[/C][C] 0.4021[/C][/ROW]
[ROW][C]58[/C][C] 0.6175[/C][C] 0.765[/C][C] 0.3825[/C][/ROW]
[ROW][C]59[/C][C] 0.6175[/C][C] 0.765[/C][C] 0.3825[/C][/ROW]
[ROW][C]60[/C][C] 0.6043[/C][C] 0.7915[/C][C] 0.3957[/C][/ROW]
[ROW][C]61[/C][C] 0.6162[/C][C] 0.7677[/C][C] 0.3838[/C][/ROW]
[ROW][C]62[/C][C] 0.7569[/C][C] 0.4862[/C][C] 0.2431[/C][/ROW]
[ROW][C]63[/C][C] 0.7479[/C][C] 0.5041[/C][C] 0.2521[/C][/ROW]
[ROW][C]64[/C][C] 0.7359[/C][C] 0.5282[/C][C] 0.2641[/C][/ROW]
[ROW][C]65[/C][C] 0.7638[/C][C] 0.4725[/C][C] 0.2362[/C][/ROW]
[ROW][C]66[/C][C] 0.7701[/C][C] 0.4599[/C][C] 0.2299[/C][/ROW]
[ROW][C]67[/C][C] 0.81[/C][C] 0.3801[/C][C] 0.19[/C][/ROW]
[ROW][C]68[/C][C] 0.8273[/C][C] 0.3453[/C][C] 0.1727[/C][/ROW]
[ROW][C]69[/C][C] 0.8259[/C][C] 0.3481[/C][C] 0.1741[/C][/ROW]
[ROW][C]70[/C][C] 0.8649[/C][C] 0.2701[/C][C] 0.1351[/C][/ROW]
[ROW][C]71[/C][C] 0.8965[/C][C] 0.2069[/C][C] 0.1035[/C][/ROW]
[ROW][C]72[/C][C] 0.9009[/C][C] 0.1982[/C][C] 0.09911[/C][/ROW]
[ROW][C]73[/C][C] 0.8972[/C][C] 0.2055[/C][C] 0.1028[/C][/ROW]
[ROW][C]74[/C][C] 0.9038[/C][C] 0.1924[/C][C] 0.09622[/C][/ROW]
[ROW][C]75[/C][C] 0.9151[/C][C] 0.1698[/C][C] 0.08489[/C][/ROW]
[ROW][C]76[/C][C] 0.9241[/C][C] 0.1519[/C][C] 0.07594[/C][/ROW]
[ROW][C]77[/C][C] 0.9339[/C][C] 0.1322[/C][C] 0.06611[/C][/ROW]
[ROW][C]78[/C][C] 0.932[/C][C] 0.1359[/C][C] 0.06796[/C][/ROW]
[ROW][C]79[/C][C] 0.9327[/C][C] 0.1345[/C][C] 0.06727[/C][/ROW]
[ROW][C]80[/C][C] 0.93[/C][C] 0.1399[/C][C] 0.06997[/C][/ROW]
[ROW][C]81[/C][C] 0.942[/C][C] 0.1161[/C][C] 0.05804[/C][/ROW]
[ROW][C]82[/C][C] 0.9423[/C][C] 0.1154[/C][C] 0.05771[/C][/ROW]
[ROW][C]83[/C][C] 0.9379[/C][C] 0.1241[/C][C] 0.06206[/C][/ROW]
[ROW][C]84[/C][C] 0.9377[/C][C] 0.1246[/C][C] 0.06229[/C][/ROW]
[ROW][C]85[/C][C] 0.9372[/C][C] 0.1256[/C][C] 0.0628[/C][/ROW]
[ROW][C]86[/C][C] 0.9423[/C][C] 0.1153[/C][C] 0.05767[/C][/ROW]
[ROW][C]87[/C][C] 0.938[/C][C] 0.1239[/C][C] 0.06195[/C][/ROW]
[ROW][C]88[/C][C] 0.9474[/C][C] 0.1053[/C][C] 0.05263[/C][/ROW]
[ROW][C]89[/C][C] 0.9455[/C][C] 0.109[/C][C] 0.05451[/C][/ROW]
[ROW][C]90[/C][C] 0.9507[/C][C] 0.09864[/C][C] 0.04932[/C][/ROW]
[ROW][C]91[/C][C] 0.9597[/C][C] 0.08064[/C][C] 0.04032[/C][/ROW]
[ROW][C]92[/C][C] 0.9697[/C][C] 0.06057[/C][C] 0.03029[/C][/ROW]
[ROW][C]93[/C][C] 0.9723[/C][C] 0.05538[/C][C] 0.02769[/C][/ROW]
[ROW][C]94[/C][C] 0.9732[/C][C] 0.05361[/C][C] 0.0268[/C][/ROW]
[ROW][C]95[/C][C] 0.9761[/C][C] 0.04779[/C][C] 0.02389[/C][/ROW]
[ROW][C]96[/C][C] 0.9828[/C][C] 0.03443[/C][C] 0.01722[/C][/ROW]
[ROW][C]97[/C][C] 0.9838[/C][C] 0.0325[/C][C] 0.01625[/C][/ROW]
[ROW][C]98[/C][C] 0.9839[/C][C] 0.03219[/C][C] 0.0161[/C][/ROW]
[ROW][C]99[/C][C] 0.9889[/C][C] 0.02214[/C][C] 0.01107[/C][/ROW]
[ROW][C]100[/C][C] 0.9912[/C][C] 0.01769[/C][C] 0.008845[/C][/ROW]
[ROW][C]101[/C][C] 0.9923[/C][C] 0.01532[/C][C] 0.007658[/C][/ROW]
[ROW][C]102[/C][C] 0.9932[/C][C] 0.01357[/C][C] 0.006785[/C][/ROW]
[ROW][C]103[/C][C] 0.9942[/C][C] 0.01167[/C][C] 0.005833[/C][/ROW]
[ROW][C]104[/C][C] 0.9958[/C][C] 0.008422[/C][C] 0.004211[/C][/ROW]
[ROW][C]105[/C][C] 0.9965[/C][C] 0.007065[/C][C] 0.003533[/C][/ROW]
[ROW][C]106[/C][C] 0.9975[/C][C] 0.004951[/C][C] 0.002475[/C][/ROW]
[ROW][C]107[/C][C] 0.9977[/C][C] 0.004531[/C][C] 0.002266[/C][/ROW]
[ROW][C]108[/C][C] 0.9977[/C][C] 0.004623[/C][C] 0.002312[/C][/ROW]
[ROW][C]109[/C][C] 0.9979[/C][C] 0.004153[/C][C] 0.002077[/C][/ROW]
[ROW][C]110[/C][C] 0.9978[/C][C] 0.004317[/C][C] 0.002158[/C][/ROW]
[ROW][C]111[/C][C] 0.9986[/C][C] 0.002723[/C][C] 0.001362[/C][/ROW]
[ROW][C]112[/C][C] 0.999[/C][C] 0.0021[/C][C] 0.00105[/C][/ROW]
[ROW][C]113[/C][C] 0.999[/C][C] 0.001965[/C][C] 0.0009825[/C][/ROW]
[ROW][C]114[/C][C] 0.9993[/C][C] 0.001425[/C][C] 0.0007127[/C][/ROW]
[ROW][C]115[/C][C] 0.9994[/C][C] 0.001196[/C][C] 0.0005982[/C][/ROW]
[ROW][C]116[/C][C] 0.9995[/C][C] 0.0009431[/C][C] 0.0004715[/C][/ROW]
[ROW][C]117[/C][C] 0.9996[/C][C] 0.0007633[/C][C] 0.0003816[/C][/ROW]
[ROW][C]118[/C][C] 0.9996[/C][C] 0.0007338[/C][C] 0.0003669[/C][/ROW]
[ROW][C]119[/C][C] 0.9996[/C][C] 0.0007738[/C][C] 0.0003869[/C][/ROW]
[ROW][C]120[/C][C] 0.9996[/C][C] 0.0007616[/C][C] 0.0003808[/C][/ROW]
[ROW][C]121[/C][C] 0.9996[/C][C] 0.0008119[/C][C] 0.0004059[/C][/ROW]
[ROW][C]122[/C][C] 0.9997[/C][C] 0.0006565[/C][C] 0.0003283[/C][/ROW]
[ROW][C]123[/C][C] 0.9997[/C][C] 0.0005994[/C][C] 0.0002997[/C][/ROW]
[ROW][C]124[/C][C] 0.9997[/C][C] 0.0006498[/C][C] 0.0003249[/C][/ROW]
[ROW][C]125[/C][C] 0.9997[/C][C] 0.000604[/C][C] 0.000302[/C][/ROW]
[ROW][C]126[/C][C] 0.9998[/C][C] 0.000469[/C][C] 0.0002345[/C][/ROW]
[ROW][C]127[/C][C] 0.9998[/C][C] 0.0003282[/C][C] 0.0001641[/C][/ROW]
[ROW][C]128[/C][C] 0.9998[/C][C] 0.0003097[/C][C] 0.0001549[/C][/ROW]
[ROW][C]129[/C][C] 0.9999[/C][C] 0.000236[/C][C] 0.000118[/C][/ROW]
[ROW][C]130[/C][C] 0.9999[/C][C] 0.0002328[/C][C] 0.0001164[/C][/ROW]
[ROW][C]131[/C][C] 0.9999[/C][C] 0.0002322[/C][C] 0.0001161[/C][/ROW]
[ROW][C]132[/C][C] 0.9999[/C][C] 0.0001659[/C][C] 8.294e-05[/C][/ROW]
[ROW][C]133[/C][C] 0.9999[/C][C] 0.0001796[/C][C] 8.98e-05[/C][/ROW]
[ROW][C]134[/C][C] 0.9999[/C][C] 0.0001687[/C][C] 8.436e-05[/C][/ROW]
[ROW][C]135[/C][C] 0.9999[/C][C] 0.0001567[/C][C] 7.836e-05[/C][/ROW]
[ROW][C]136[/C][C] 1[/C][C] 9.639e-05[/C][C] 4.819e-05[/C][/ROW]
[ROW][C]137[/C][C] 0.9999[/C][C] 0.000111[/C][C] 5.548e-05[/C][/ROW]
[ROW][C]138[/C][C] 1[/C][C] 9.634e-05[/C][C] 4.817e-05[/C][/ROW]
[ROW][C]139[/C][C] 1[/C][C] 9.779e-05[/C][C] 4.89e-05[/C][/ROW]
[ROW][C]140[/C][C] 1[/C][C] 9.17e-05[/C][C] 4.585e-05[/C][/ROW]
[ROW][C]141[/C][C] 1[/C][C] 8.039e-05[/C][C] 4.019e-05[/C][/ROW]
[ROW][C]142[/C][C] 1[/C][C] 8.409e-05[/C][C] 4.204e-05[/C][/ROW]
[ROW][C]143[/C][C] 1[/C][C] 8.101e-05[/C][C] 4.05e-05[/C][/ROW]
[ROW][C]144[/C][C] 1[/C][C] 5.201e-05[/C][C] 2.601e-05[/C][/ROW]
[ROW][C]145[/C][C] 1[/C][C] 5.857e-05[/C][C] 2.929e-05[/C][/ROW]
[ROW][C]146[/C][C] 1[/C][C] 5.249e-05[/C][C] 2.624e-05[/C][/ROW]
[ROW][C]147[/C][C] 1[/C][C] 6.209e-05[/C][C] 3.104e-05[/C][/ROW]
[ROW][C]148[/C][C] 1[/C][C] 5.322e-05[/C][C] 2.661e-05[/C][/ROW]
[ROW][C]149[/C][C] 1[/C][C] 6.847e-05[/C][C] 3.424e-05[/C][/ROW]
[ROW][C]150[/C][C] 0.9999[/C][C] 0.0001034[/C][C] 5.169e-05[/C][/ROW]
[ROW][C]151[/C][C] 0.9999[/C][C] 0.0001199[/C][C] 5.995e-05[/C][/ROW]
[ROW][C]152[/C][C] 1[/C][C] 9.373e-05[/C][C] 4.686e-05[/C][/ROW]
[ROW][C]153[/C][C] 0.9999[/C][C] 0.0001162[/C][C] 5.808e-05[/C][/ROW]
[ROW][C]154[/C][C] 0.9999[/C][C] 0.0001073[/C][C] 5.364e-05[/C][/ROW]
[ROW][C]155[/C][C] 0.9999[/C][C] 0.0001242[/C][C] 6.208e-05[/C][/ROW]
[ROW][C]156[/C][C] 0.9999[/C][C] 0.0001155[/C][C] 5.774e-05[/C][/ROW]
[ROW][C]157[/C][C] 1[/C][C] 9.236e-05[/C][C] 4.618e-05[/C][/ROW]
[ROW][C]158[/C][C] 1[/C][C] 9.971e-05[/C][C] 4.986e-05[/C][/ROW]
[ROW][C]159[/C][C] 0.9999[/C][C] 0.0001276[/C][C] 6.38e-05[/C][/ROW]
[ROW][C]160[/C][C] 0.9999[/C][C] 0.0001706[/C][C] 8.528e-05[/C][/ROW]
[ROW][C]161[/C][C] 0.9999[/C][C] 0.0002653[/C][C] 0.0001327[/C][/ROW]
[ROW][C]162[/C][C] 0.9998[/C][C] 0.0003472[/C][C] 0.0001736[/C][/ROW]
[ROW][C]163[/C][C] 0.9999[/C][C] 0.0002265[/C][C] 0.0001132[/C][/ROW]
[ROW][C]164[/C][C] 0.9999[/C][C] 0.0002011[/C][C] 0.0001006[/C][/ROW]
[ROW][C]165[/C][C] 0.9998[/C][C] 0.0004038[/C][C] 0.0002019[/C][/ROW]
[ROW][C]166[/C][C] 0.9998[/C][C] 0.0004748[/C][C] 0.0002374[/C][/ROW]
[ROW][C]167[/C][C] 0.9996[/C][C] 0.0008355[/C][C] 0.0004177[/C][/ROW]
[ROW][C]168[/C][C] 0.9997[/C][C] 0.0005766[/C][C] 0.0002883[/C][/ROW]
[ROW][C]169[/C][C] 0.9997[/C][C] 0.0005052[/C][C] 0.0002526[/C][/ROW]
[ROW][C]170[/C][C] 0.9998[/C][C] 0.0003595[/C][C] 0.0001798[/C][/ROW]
[ROW][C]171[/C][C] 0.9996[/C][C] 0.0007842[/C][C] 0.0003921[/C][/ROW]
[ROW][C]172[/C][C] 0.9991[/C][C] 0.001883[/C][C] 0.0009413[/C][/ROW]
[ROW][C]173[/C][C] 0.9975[/C][C] 0.005025[/C][C] 0.002512[/C][/ROW]
[ROW][C]174[/C][C] 0.9936[/C][C] 0.01279[/C][C] 0.006396[/C][/ROW]
[ROW][C]175[/C][C] 0.9919[/C][C] 0.01629[/C][C] 0.008143[/C][/ROW]
[ROW][C]176[/C][C] 0.9809[/C][C] 0.03812[/C][C] 0.01906[/C][/ROW]
[ROW][C]177[/C][C] 0.955[/C][C] 0.08993[/C][C] 0.04496[/C][/ROW]
[ROW][C]178[/C][C] 0.892[/C][C] 0.2161[/C][C] 0.108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=6

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	0.4678	0.9356	0.5322
12	0.3333	0.6667	0.6667
13	0.2113	0.4226	0.7887
14	0.1343	0.2687	0.8657
15	0.4249	0.8498	0.5751
16	0.3361	0.6722	0.6639
17	0.3391	0.6782	0.6609
18	0.2944	0.5888	0.7056
19	0.224	0.4479	0.776
20	0.2414	0.4828	0.7586
21	0.3026	0.6051	0.6974
22	0.2578	0.5157	0.7422
23	0.3457	0.6914	0.6543
24	0.3719	0.7438	0.6281
25	0.4941	0.9881	0.5059
26	0.4877	0.9754	0.5123
27	0.4935	0.9871	0.5065
28	0.4627	0.9254	0.5373
29	0.4915	0.983	0.5085
30	0.5305	0.9389	0.4695
31	0.5041	0.9919	0.4959
32	0.4866	0.9731	0.5134
33	0.4927	0.9854	0.5073
34	0.4508	0.9017	0.5492
35	0.4197	0.8393	0.5803
36	0.4285	0.857	0.5715
37	0.4051	0.8103	0.5949
38	0.4606	0.9212	0.5394
39	0.4373	0.8747	0.5627
40	0.4254	0.8509	0.5746
41	0.3987	0.7974	0.6013
42	0.3958	0.7916	0.6042
43	0.4248	0.8495	0.5752
44	0.4094	0.8187	0.5906
45	0.435	0.87	0.565
46	0.4151	0.8302	0.5849
47	0.4642	0.9285	0.5358
48	0.4655	0.931	0.5345
49	0.4713	0.9427	0.5287
50	0.5035	0.9931	0.4965
51	0.5503	0.8994	0.4497
52	0.5607	0.8786	0.4393
53	0.5802	0.8395	0.4198
54	0.6298	0.7404	0.3702
55	0.6318	0.7365	0.3682
56	0.6123	0.7753	0.3877
57	0.5979	0.8042	0.4021
58	0.6175	0.765	0.3825
59	0.6175	0.765	0.3825
60	0.6043	0.7915	0.3957
61	0.6162	0.7677	0.3838
62	0.7569	0.4862	0.2431
63	0.7479	0.5041	0.2521
64	0.7359	0.5282	0.2641
65	0.7638	0.4725	0.2362
66	0.7701	0.4599	0.2299
67	0.81	0.3801	0.19
68	0.8273	0.3453	0.1727
69	0.8259	0.3481	0.1741
70	0.8649	0.2701	0.1351
71	0.8965	0.2069	0.1035
72	0.9009	0.1982	0.09911
73	0.8972	0.2055	0.1028
74	0.9038	0.1924	0.09622
75	0.9151	0.1698	0.08489
76	0.9241	0.1519	0.07594
77	0.9339	0.1322	0.06611
78	0.932	0.1359	0.06796
79	0.9327	0.1345	0.06727
80	0.93	0.1399	0.06997
81	0.942	0.1161	0.05804
82	0.9423	0.1154	0.05771
83	0.9379	0.1241	0.06206
84	0.9377	0.1246	0.06229
85	0.9372	0.1256	0.0628
86	0.9423	0.1153	0.05767
87	0.938	0.1239	0.06195
88	0.9474	0.1053	0.05263
89	0.9455	0.109	0.05451
90	0.9507	0.09864	0.04932
91	0.9597	0.08064	0.04032
92	0.9697	0.06057	0.03029
93	0.9723	0.05538	0.02769
94	0.9732	0.05361	0.0268
95	0.9761	0.04779	0.02389
96	0.9828	0.03443	0.01722
97	0.9838	0.0325	0.01625
98	0.9839	0.03219	0.0161
99	0.9889	0.02214	0.01107
100	0.9912	0.01769	0.008845
101	0.9923	0.01532	0.007658
102	0.9932	0.01357	0.006785
103	0.9942	0.01167	0.005833
104	0.9958	0.008422	0.004211
105	0.9965	0.007065	0.003533
106	0.9975	0.004951	0.002475
107	0.9977	0.004531	0.002266
108	0.9977	0.004623	0.002312
109	0.9979	0.004153	0.002077
110	0.9978	0.004317	0.002158
111	0.9986	0.002723	0.001362
112	0.999	0.0021	0.00105
113	0.999	0.001965	0.0009825
114	0.9993	0.001425	0.0007127
115	0.9994	0.001196	0.0005982
116	0.9995	0.0009431	0.0004715
117	0.9996	0.0007633	0.0003816
118	0.9996	0.0007338	0.0003669
119	0.9996	0.0007738	0.0003869
120	0.9996	0.0007616	0.0003808
121	0.9996	0.0008119	0.0004059
122	0.9997	0.0006565	0.0003283
123	0.9997	0.0005994	0.0002997
124	0.9997	0.0006498	0.0003249
125	0.9997	0.000604	0.000302
126	0.9998	0.000469	0.0002345
127	0.9998	0.0003282	0.0001641
128	0.9998	0.0003097	0.0001549
129	0.9999	0.000236	0.000118
130	0.9999	0.0002328	0.0001164
131	0.9999	0.0002322	0.0001161
132	0.9999	0.0001659	8.294e-05
133	0.9999	0.0001796	8.98e-05
134	0.9999	0.0001687	8.436e-05
135	0.9999	0.0001567	7.836e-05
136	1	9.639e-05	4.819e-05
137	0.9999	0.000111	5.548e-05
138	1	9.634e-05	4.817e-05
139	1	9.779e-05	4.89e-05
140	1	9.17e-05	4.585e-05
141	1	8.039e-05	4.019e-05
142	1	8.409e-05	4.204e-05
143	1	8.101e-05	4.05e-05
144	1	5.201e-05	2.601e-05
145	1	5.857e-05	2.929e-05
146	1	5.249e-05	2.624e-05
147	1	6.209e-05	3.104e-05
148	1	5.322e-05	2.661e-05
149	1	6.847e-05	3.424e-05
150	0.9999	0.0001034	5.169e-05
151	0.9999	0.0001199	5.995e-05
152	1	9.373e-05	4.686e-05
153	0.9999	0.0001162	5.808e-05
154	0.9999	0.0001073	5.364e-05
155	0.9999	0.0001242	6.208e-05
156	0.9999	0.0001155	5.774e-05
157	1	9.236e-05	4.618e-05
158	1	9.971e-05	4.986e-05
159	0.9999	0.0001276	6.38e-05
160	0.9999	0.0001706	8.528e-05
161	0.9999	0.0002653	0.0001327
162	0.9998	0.0003472	0.0001736
163	0.9999	0.0002265	0.0001132
164	0.9999	0.0002011	0.0001006
165	0.9998	0.0004038	0.0002019
166	0.9998	0.0004748	0.0002374
167	0.9996	0.0008355	0.0004177
168	0.9997	0.0005766	0.0002883
169	0.9997	0.0005052	0.0002526
170	0.9998	0.0003595	0.0001798
171	0.9996	0.0007842	0.0003921
172	0.9991	0.001883	0.0009413
173	0.9975	0.005025	0.002512
174	0.9936	0.01279	0.006396
175	0.9919	0.01629	0.008143
176	0.9809	0.03812	0.01906
177	0.955	0.08993	0.04496
178	0.892	0.2161	0.108

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	70	0.4167	NOK
5% type I error level	82	0.488095	NOK
10% type I error level	88	0.52381	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 & 70 &  0.4167 & NOK \tabularnewline
5% type I error level & 82 & 0.488095 & NOK \tabularnewline
10% type I error level & 88 & 0.52381 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=7

[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]70[/C][C] 0.4167[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]82[/C][C]0.488095[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]88[/C][C]0.52381[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=7

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	70	0.4167	NOK
5% type I error level	82	0.488095	NOK
10% type I error level	88	0.52381	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 7.1362, df1 = 2, df2 = 179, p-value = 0.001043
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.57774, df1 = 14, df2 = 167, p-value = 0.8798
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.5509, df1 = 2, df2 = 179, p-value = 0.5774

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.1362, df1 = 2, df2 = 179, p-value = 0.001043
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.57774, df1 = 14, df2 = 167, p-value = 0.8798
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.5509, df1 = 2, df2 = 179, p-value = 0.5774
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.1362, df1 = 2, df2 = 179, p-value = 0.001043
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.57774, df1 = 14, df2 = 167, p-value = 0.8798
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.5509, df1 = 2, df2 = 179, p-value = 0.5774
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 7.1362, df1 = 2, df2 = 179, p-value = 0.001043
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.57774, df1 = 14, df2 = 167, p-value = 0.8798
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.5509, df1 = 2, df2 = 179, p-value = 0.5774

Variance Inflation Factors (Multicollinearity)
> vif Contact GapP Power Eye Ks Def Speed 2.073801 1.280111 1.900775 1.103652 2.676418 1.070157 1.247060

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 Contact     GapP    Power      Eye       Ks      Def    Speed 
2.073801 1.280111 1.900775 1.103652 2.676418 1.070157 1.247060 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318979&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 Contact     GapP    Power      Eye       Ks      Def    Speed 
2.073801 1.280111 1.900775 1.103652 2.676418 1.070157 1.247060 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318979&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318979&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Contact GapP Power Eye Ks Def Speed 2.073801 1.280111 1.900775 1.103652 2.676418 1.070157 1.247060

Figure 1

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Figure 8

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Figure 10

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Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '7'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code