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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Jan 2020 10:12:05 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Jan/28/t1580202986kgqvl3r8biug6wf.htm/, Retrieved Wed, 21 Apr 2021 07:31:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319050, Retrieved Wed, 21 Apr 2021 07:31:09 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact46
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [vraag 3] [2020-01-28 09:12:05] [43eb2330ebca6ad52336dea971844457] [Current]
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Dataseries X:
10 10 10 10 21 36 1 0
8 8 9 15 22 32 1 1
8 6 12 14 17 33 1 1
9 10 14 14 21 39 1 1
5 8 6 8 19 34 1 0
10 10 13 19 23 39 1 1
8 7 12 17 21 36 1 1
9 10 13 18 22 33 1 1
8 6 6 10 11 30 1 0
7 7 12 15 20 39 1 0
10 9 10 16 18 37 1 0
10 6 9 12 16 37 1 0
9 7 12 13 18 35 1 1
4 6 7 10 13 32 1 0
4 4 10 14 17 36 1 1
8 6 11 15 20 36 1 1
9 8 15 20 20 41 1 1
10 9 10 9 15 36 1 1
8 8 12 12 18 37 1 0
5 6 10 13 15 29 1 0
10 6 12 16 19 39 1 1
8 10 11 12 19 37 1 0
7 8 11 14 19 32 1 1
8 8 12 15 20 36 1 1
8 7 15 19 20 43 1 1
9 4 12 16 16 30 1 0
8 9 11 16 18 33 1 0
6 8 9 14 17 28 1 1
8 10 11 14 18 30 1 1
8 8 11 14 13 28 1 0
5 6 9 13 20 39 0 1
9 7 15 18 21 34 1 1
8 8 12 15 17 34 1 0
8 5 9 15 19 29 1 0
8 10 12 15 20 32 1 0
6 2 12 13 15 33 1 0
6 6 9 14 15 27 1 0
9 7 9 15 19 35 1 1
8 5 11 14 18 38 1 1
9 8 12 19 22 40 1 1
10 7 12 16 20 34 1 1
8 7 12 16 18 34 0 0
8 10 12 12 14 26 1 0
7 7 6 10 15 39 1 0
7 6 11 11 17 34 1 1
10 10 12 13 16 39 1 1
8 6 9 14 17 26 1 1
7 5 11 11 15 30 1 1
10 8 9 11 17 34 1 1
7 8 10 16 18 34 1 1
7 5 10 9 16 29 1 0
9 8 9 16 18 41 1 0
9 10 12 19 22 43 1 0
8 7 11 13 16 31 1 0
6 7 9 15 16 33 1 0
8 7 9 14 20 34 1 0
9 7 12 15 18 30 1 1
2 2 6 11 16 23 0 0
6 4 10 14 16 29 1 0
8 6 12 15 20 35 1 1
8 7 11 17 21 40 0 1
7 9 14 16 18 27 0 0
8 9 8 13 15 30 1 0
6 4 9 15 18 27 1 0
10 9 10 14 18 29 1 0
10 9 10 15 20 33 1 0
10 8 10 14 18 32 1 0
8 7 11 12 16 33 1 0
8 9 10 12 19 36 1 1
7 7 12 15 20 34 1 1
10 6 14 17 22 45 1 1
5 7 10 13 18 30 0 0
3 2 8 5 8 22 0 1
2 3 8 7 13 24 0 1
3 4 7 10 13 25 0 1
4 5 11 15 18 26 0 1
2 2 6 9 12 27 0 0
6 6 9 9 16 27 0 0
8 8 12 15 21 35 1 0
8 5 12 14 20 36 1 0
5 4 12 11 18 32 0 0
10 10 9 18 22 35 1 1
9 10 15 20 23 35 1 1
8 10 15 20 23 36 1 1
9 9 13 16 21 37 1 1
8 5 9 15 16 33 1 1
5 5 12 14 14 25 1 0
7 7 9 13 18 35 1 1
9 10 15 18 22 37 1 1
8 9 11 14 20 36 1 0
4 8 11 12 18 35 1 1
7 8 6 9 12 29 1 1
8 8 14 19 17 35 1 1
7 8 11 13 15 31 1 0
7 8 8 12 18 30 1 1
9 7 10 14 18 37 1 0
6 6 10 6 15 36 1 1
7 8 9 14 16 35 1 0
4 2 8 11 15 32 1 0
6 5 9 11 16 34 1 1
10 4 10 14 19 37 1 0
9 9 11 12 19 36 1 1
10 10 14 19 23 39 1 1
8 6 12 13 20 37 1 0
4 4 9 14 18 31 0 0
8 10 13 17 21 40 1 1
5 6 8 12 19 38 1 0
8 7 12 16 18 35 0 1
9 7 14 15 19 38 0 1
8 8 9 15 17 32 1 0
4 6 10 15 21 41 1 1
8 5 12 16 19 28 1 0
10 6 12 15 24 40 1 1
6 7 9 12 12 25 1 0
7 6 9 13 15 28 1 0
10 9 12 14 18 37 1 1
9 9 15 17 19 37 1 1
8 7 12 14 22 40 1 1
3 6 11 14 19 26 0 0
8 7 8 14 16 30 1 0
7 7 11 15 19 32 1 0
7 8 11 11 18 31 1 0
8 7 10 11 18 28 1 0
8 8 12 16 19 34 1 1
7 7 9 12 21 39 1 0
7 4 11 12 19 33 0 1
9 10 15 19 22 43 1 0
9 8 14 18 23 37 0 1
9 8 6 16 17 31 1 0
4 2 9 16 18 31 0 1
6 6 9 13 19 34 1 0
6 4 8 11 15 32 1 1
6 4 7 10 14 27 0 0
8 9 10 14 18 34 1 0
3 2 6 14 17 28 0 0
8 6 9 14 19 32 0 0
8 7 9 16 16 39 0 1
6 4 7 10 14 28 0 1
10 10 11 16 20 39 1 0
2 3 9 7 16 32 0 0
9 7 12 16 18 36 0 1
6 4 9 15 16 31 0 1
6 8 10 17 21 39 0 0
5 4 11 11 16 23 0 0
4 5 7 11 14 25 0 0
7 6 12 10 16 32 1 0
5 5 8 13 19 32 0 1
8 9 13 14 19 36 0 1
6 6 11 13 19 39 0 0
9 8 11 13 18 31 0 1
6 4 12 12 16 32 1 0
4 4 11 10 14 28 0 1
7 8 12 15 19 34 0 0
2 4 3 6 11 28 0 1
8 10 10 15 18 38 1 1
9 8 13 15 18 35 1 1
6 5 10 11 16 32 1 0
5 3 6 14 20 26 0 1
7 7 11 14 18 32 0 1
8 6 12 16 20 28 1 1
4 5 9 12 16 31 1 0
9 5 10 15 18 33 0 1
9 9 15 20 19 38 1 0
9 2 9 12 19 38 0 1
7 7 6 9 15 36 0 0
5 7 9 13 17 31 1 1
7 5 15 15 21 36 0 0
9 9 15 19 24 43 1 1
8 4 9 11 16 37 1 1
6 5 11 11 13 28 0 1
9 9 9 17 21 35 0 1
8 7 11 15 16 34 1 1
7 6 10 14 17 40 1 1
7 8 9 15 17 31 1 0
7 7 6 11 18 41 0 0
8 6 12 12 18 35 1 0
10 8 13 15 23 38 1 1
6 6 12 16 20 37 0 0
6 7 12 16 20 31 0 0




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig 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=319050&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=319050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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 Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.02844 + 0.328297Relative_Advantage[t] + 0.0917069Perceived_Usefulness[t] + 0.103309Perceived_Ease_of_Use[t] + 0.000830561Information_Quality[t] + 0.0876004System_Quality[t] + 0.895018groupB[t] + 0.188132genderB[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -1.02844 +  0.328297Relative_Advantage[t] +  0.0917069Perceived_Usefulness[t] +  0.103309Perceived_Ease_of_Use[t] +  0.000830561Information_Quality[t] +  0.0876004System_Quality[t] +  0.895018groupB[t] +  0.188132genderB[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.02844 +  0.328297Relative_Advantage[t] +  0.0917069Perceived_Usefulness[t] +  0.103309Perceived_Ease_of_Use[t] +  0.000830561Information_Quality[t] +  0.0876004System_Quality[t] +  0.895018groupB[t] +  0.188132genderB[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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
Intention_to_Use[t] = -1.02844 + 0.328297Relative_Advantage[t] + 0.0917069Perceived_Usefulness[t] + 0.103309Perceived_Ease_of_Use[t] + 0.000830561Information_Quality[t] + 0.0876004System_Quality[t] + 0.895018groupB[t] + 0.188132genderB[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.028 0.7824-1.3140e+00 0.1905 0.09523
Relative_Advantage+0.3283 0.06025+5.4490e+00 1.74e-07 8.701e-08
Perceived_Usefulness+0.09171 0.05917+1.5500e+00 0.123 0.0615
Perceived_Ease_of_Use+0.1033 0.05366+1.9250e+00 0.05584 0.02792
Information_Quality+0.0008306 0.05958+1.3940e-02 0.9889 0.4944
System_Quality+0.0876 0.02888+3.0330e+00 0.002796 0.001398
groupB+0.895 0.2479+3.6100e+00 0.0004021 0.0002011
genderB+0.1881 0.2056+9.1510e-01 0.3614 0.1807

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.028 &  0.7824 & -1.3140e+00 &  0.1905 &  0.09523 \tabularnewline
Relative_Advantage & +0.3283 &  0.06025 & +5.4490e+00 &  1.74e-07 &  8.701e-08 \tabularnewline
Perceived_Usefulness & +0.09171 &  0.05917 & +1.5500e+00 &  0.123 &  0.0615 \tabularnewline
Perceived_Ease_of_Use & +0.1033 &  0.05366 & +1.9250e+00 &  0.05584 &  0.02792 \tabularnewline
Information_Quality & +0.0008306 &  0.05958 & +1.3940e-02 &  0.9889 &  0.4944 \tabularnewline
System_Quality & +0.0876 &  0.02888 & +3.0330e+00 &  0.002796 &  0.001398 \tabularnewline
groupB & +0.895 &  0.2479 & +3.6100e+00 &  0.0004021 &  0.0002011 \tabularnewline
genderB & +0.1881 &  0.2056 & +9.1510e-01 &  0.3614 &  0.1807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.028[/C][C] 0.7824[/C][C]-1.3140e+00[/C][C] 0.1905[/C][C] 0.09523[/C][/ROW]
[ROW][C]Relative_Advantage[/C][C]+0.3283[/C][C] 0.06025[/C][C]+5.4490e+00[/C][C] 1.74e-07[/C][C] 8.701e-08[/C][/ROW]
[ROW][C]Perceived_Usefulness[/C][C]+0.09171[/C][C] 0.05917[/C][C]+1.5500e+00[/C][C] 0.123[/C][C] 0.0615[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.1033[/C][C] 0.05366[/C][C]+1.9250e+00[/C][C] 0.05584[/C][C] 0.02792[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.0008306[/C][C] 0.05958[/C][C]+1.3940e-02[/C][C] 0.9889[/C][C] 0.4944[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.0876[/C][C] 0.02888[/C][C]+3.0330e+00[/C][C] 0.002796[/C][C] 0.001398[/C][/ROW]
[ROW][C]groupB[/C][C]+0.895[/C][C] 0.2479[/C][C]+3.6100e+00[/C][C] 0.0004021[/C][C] 0.0002011[/C][/ROW]
[ROW][C]genderB[/C][C]+0.1881[/C][C] 0.2056[/C][C]+9.1510e-01[/C][C] 0.3614[/C][C] 0.1807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.028 0.7824-1.3140e+00 0.1905 0.09523
Relative_Advantage+0.3283 0.06025+5.4490e+00 1.74e-07 8.701e-08
Perceived_Usefulness+0.09171 0.05917+1.5500e+00 0.123 0.0615
Perceived_Ease_of_Use+0.1033 0.05366+1.9250e+00 0.05584 0.02792
Information_Quality+0.0008306 0.05958+1.3940e-02 0.9889 0.4944
System_Quality+0.0876 0.02888+3.0330e+00 0.002796 0.001398
groupB+0.895 0.2479+3.6100e+00 0.0004021 0.0002011
genderB+0.1881 0.2056+9.1510e-01 0.3614 0.1807







Multiple Linear Regression - Regression Statistics
Multiple R 0.7512
R-squared 0.5644
Adjusted R-squared 0.5465
F-TEST (value) 31.65
F-TEST (DF numerator)7
F-TEST (DF denominator)171
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.322
Sum Squared Residuals 298.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7512 \tabularnewline
R-squared &  0.5644 \tabularnewline
Adjusted R-squared &  0.5465 \tabularnewline
F-TEST (value) &  31.65 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 171 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.322 \tabularnewline
Sum Squared Residuals &  298.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7512[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5644[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5465[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 31.65[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]171[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.322[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 298.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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 0.7512
R-squared 0.5644
Adjusted R-squared 0.5465
F-TEST (value) 31.65
F-TEST (DF numerator)7
F-TEST (DF denominator)171
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.322
Sum Squared Residuals 298.9







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\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=319050&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=319050&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10 8.271 1.729
2 8 7.878 0.1224
3 8 7.476 0.5238
4 9 9.502-0.5018
5 5 6.864-1.864
6 10 9.928 0.07174
7 8 8.381-0.3806
8 9 9.299-0.2985
9 8 6.057 1.943
10 7 8.248-1.248
11 10 8.647 1.353
12 10 7.156 2.844
13 9 7.877 1.123
14 4 6.325-2.325
15 4 6.899-2.899
16 8 7.753 0.2469
17 9 9.731-0.7311
18 10 8.022 1.978
19 8 8.089-0.08931
20 5 6.649-1.649
21 10 8.21 1.79
22 8 8.655-0.655
23 7 7.955-0.9552
24 8 8.501-0.5014
25 8 9.475-1.475
26 9 6.574 2.426
27 8 8.389-0.3887
28 6 7.42-1.42
29 8 8.436-0.4357
30 8 7.412 0.5883
31 5 6.731-1.731
32 9 8.584 0.4162
33 8 8.136-0.1356
34 8 6.439 1.561
35 8 8.619-0.6195
36 6 5.87 0.1301
37 6 6.486-0.4857
38 9 7.81 1.19
39 8 7.495 0.5049
40 9 9.267-0.2667
41 10 8.101 1.899
42 8 7.016 0.9836
43 8 7.779 0.221
44 7 7.177-0.1769
45 7 7.162-0.1622
46 10 9.211 0.7891
47 8 6.588 1.412
48 7 6.482 0.5182
49 10 7.635 2.365
50 7 8.244-1.244
51 7 5.909 1.091
52 9 8.578 0.4222
53 9 9.998-0.998
54 8 7.245 0.7547
55 6 7.444-1.444
56 8 7.431 0.5686
57 9 7.646 1.354
58 2 3.343-1.343
59 6 6.097-0.09686
60 8 7.757 0.2428
61 8 7.744 0.2557
62 7 7.243-0.2432
63 8 7.538 0.4616
64 6 5.935 0.06508
65 10 7.74 2.26
66 10 8.195 1.805
67 10 7.675 2.325
68 8 7.317 0.6828
69 8 8.336-0.3356
70 7 7.998-0.9979
71 10 9.025 0.9751
72 5 6.173-1.173
73 3 3-0.0003373
74 2 3.715-1.715
75 3 4.349-1.349
76 4 5.652-1.652
77 2 3.483-1.483
78 6 5.075 0.925
79 8 8.227-0.2265
80 8 7.225 0.7749
81 5 5.34-0.3398
82 10 9.107 0.8931
83 9 9.865-0.8646
84 8 9.952-1.952
85 9 9.113-0.1132
86 8 6.975 1.025
87 5 6.257-1.257
88 7 7.602-0.6021
89 9 9.832-0.8323
90 8 8.447-0.4466
91 4 8.011-4.011
92 7 6.711 0.2885
93 8 9.008-1.008
94 7 7.573-0.5728
95 7 7.297-0.2974
96 9 7.784 1.216
97 6 6.727-0.7275
98 7 7.844-0.8439
99 4 5.209-1.209
100 6 6.65-0.6497
101 10 6.8 3.2
102 9 8.427 0.5727
103 10 10.02-0.01996
104 8 7.538 0.4623
105 4 5.287-1.287
106 8 9.808-1.808
107 5 7.154-2.154
108 8 7.292 0.7078
109 9 7.636 1.364
110 8 7.685 0.3147
111 4 8.1-4.1
112 8 6.73 1.27
113 10 8.199 1.801
114 6 6.43-0.4297
115 7 6.47 0.53
116 10 8.812 1.188
117 9 9.398-0.3982
118 8 8.422-0.4219
119 3 5.69-2.69
120 8 6.986 1.014
121 7 7.542-0.5421
122 7 7.369-0.3687
123 8 6.686 1.314
124 8 8.429-0.4287
125 7 7.664-0.6636
126 7 5.628 1.372
127 9 10.27-1.273
128 9 8.19 0.8102
129 9 7.426 1.574
130 4 5.025-1.025
131 6 6.999-0.9989
132 6 6.054-0.05362
133 6 4.337 1.663
134 8 8.178-0.178
135 3 4.092-1.092
136 8 6.032 1.968
137 8 7.366 0.6342
138 6 4.612 1.388
139 10 9.244 0.7557
140 2 4.321-2.321
141 9 7.38 1.62
142 6 5.577 0.4232
143 6 7.705-1.705
144 5 4.458 0.542
145 4 4.593-0.593
146 7 6.786 0.2136
147 5 5.697-0.6968
148 8 7.922 0.07773
149 6 6.725-0.7253
150 9 6.868 2.132
151 6 6.336-0.3365
152 4 4.979-0.9792
153 7 7.242-0.2422
154 2 3.83-1.83
155 8 9.148-1.148
156 9 8.504 0.4961
157 6 6.378-0.378
158 5 4.435 0.5646
159 7 6.731 0.269
160 8 7.247 0.7527
161 4 6.302-2.302
162 9 6.174 2.826
163 9 9.608-0.6076
164 9 5.226 3.774
165 7 5.916 1.084
166 5 7.251-2.251
167 7 6.709 0.2907
168 9 10.13-1.135
169 8 6.584 1.416
170 6 5.41 0.59
171 9 7.779 1.221
172 8 7.903 0.0971
173 7 7.906-0.906
174 7 7.598-0.5977
175 7 6.563 0.4372
176 8 7.258 0.7425
177 10 8.771 1.229
178 6 6.953-0.9526
179 6 6.755-0.7553

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  8.271 &  1.729 \tabularnewline
2 &  8 &  7.878 &  0.1224 \tabularnewline
3 &  8 &  7.476 &  0.5238 \tabularnewline
4 &  9 &  9.502 & -0.5018 \tabularnewline
5 &  5 &  6.864 & -1.864 \tabularnewline
6 &  10 &  9.928 &  0.07174 \tabularnewline
7 &  8 &  8.381 & -0.3806 \tabularnewline
8 &  9 &  9.299 & -0.2985 \tabularnewline
9 &  8 &  6.057 &  1.943 \tabularnewline
10 &  7 &  8.248 & -1.248 \tabularnewline
11 &  10 &  8.647 &  1.353 \tabularnewline
12 &  10 &  7.156 &  2.844 \tabularnewline
13 &  9 &  7.877 &  1.123 \tabularnewline
14 &  4 &  6.325 & -2.325 \tabularnewline
15 &  4 &  6.899 & -2.899 \tabularnewline
16 &  8 &  7.753 &  0.2469 \tabularnewline
17 &  9 &  9.731 & -0.7311 \tabularnewline
18 &  10 &  8.022 &  1.978 \tabularnewline
19 &  8 &  8.089 & -0.08931 \tabularnewline
20 &  5 &  6.649 & -1.649 \tabularnewline
21 &  10 &  8.21 &  1.79 \tabularnewline
22 &  8 &  8.655 & -0.655 \tabularnewline
23 &  7 &  7.955 & -0.9552 \tabularnewline
24 &  8 &  8.501 & -0.5014 \tabularnewline
25 &  8 &  9.475 & -1.475 \tabularnewline
26 &  9 &  6.574 &  2.426 \tabularnewline
27 &  8 &  8.389 & -0.3887 \tabularnewline
28 &  6 &  7.42 & -1.42 \tabularnewline
29 &  8 &  8.436 & -0.4357 \tabularnewline
30 &  8 &  7.412 &  0.5883 \tabularnewline
31 &  5 &  6.731 & -1.731 \tabularnewline
32 &  9 &  8.584 &  0.4162 \tabularnewline
33 &  8 &  8.136 & -0.1356 \tabularnewline
34 &  8 &  6.439 &  1.561 \tabularnewline
35 &  8 &  8.619 & -0.6195 \tabularnewline
36 &  6 &  5.87 &  0.1301 \tabularnewline
37 &  6 &  6.486 & -0.4857 \tabularnewline
38 &  9 &  7.81 &  1.19 \tabularnewline
39 &  8 &  7.495 &  0.5049 \tabularnewline
40 &  9 &  9.267 & -0.2667 \tabularnewline
41 &  10 &  8.101 &  1.899 \tabularnewline
42 &  8 &  7.016 &  0.9836 \tabularnewline
43 &  8 &  7.779 &  0.221 \tabularnewline
44 &  7 &  7.177 & -0.1769 \tabularnewline
45 &  7 &  7.162 & -0.1622 \tabularnewline
46 &  10 &  9.211 &  0.7891 \tabularnewline
47 &  8 &  6.588 &  1.412 \tabularnewline
48 &  7 &  6.482 &  0.5182 \tabularnewline
49 &  10 &  7.635 &  2.365 \tabularnewline
50 &  7 &  8.244 & -1.244 \tabularnewline
51 &  7 &  5.909 &  1.091 \tabularnewline
52 &  9 &  8.578 &  0.4222 \tabularnewline
53 &  9 &  9.998 & -0.998 \tabularnewline
54 &  8 &  7.245 &  0.7547 \tabularnewline
55 &  6 &  7.444 & -1.444 \tabularnewline
56 &  8 &  7.431 &  0.5686 \tabularnewline
57 &  9 &  7.646 &  1.354 \tabularnewline
58 &  2 &  3.343 & -1.343 \tabularnewline
59 &  6 &  6.097 & -0.09686 \tabularnewline
60 &  8 &  7.757 &  0.2428 \tabularnewline
61 &  8 &  7.744 &  0.2557 \tabularnewline
62 &  7 &  7.243 & -0.2432 \tabularnewline
63 &  8 &  7.538 &  0.4616 \tabularnewline
64 &  6 &  5.935 &  0.06508 \tabularnewline
65 &  10 &  7.74 &  2.26 \tabularnewline
66 &  10 &  8.195 &  1.805 \tabularnewline
67 &  10 &  7.675 &  2.325 \tabularnewline
68 &  8 &  7.317 &  0.6828 \tabularnewline
69 &  8 &  8.336 & -0.3356 \tabularnewline
70 &  7 &  7.998 & -0.9979 \tabularnewline
71 &  10 &  9.025 &  0.9751 \tabularnewline
72 &  5 &  6.173 & -1.173 \tabularnewline
73 &  3 &  3 & -0.0003373 \tabularnewline
74 &  2 &  3.715 & -1.715 \tabularnewline
75 &  3 &  4.349 & -1.349 \tabularnewline
76 &  4 &  5.652 & -1.652 \tabularnewline
77 &  2 &  3.483 & -1.483 \tabularnewline
78 &  6 &  5.075 &  0.925 \tabularnewline
79 &  8 &  8.227 & -0.2265 \tabularnewline
80 &  8 &  7.225 &  0.7749 \tabularnewline
81 &  5 &  5.34 & -0.3398 \tabularnewline
82 &  10 &  9.107 &  0.8931 \tabularnewline
83 &  9 &  9.865 & -0.8646 \tabularnewline
84 &  8 &  9.952 & -1.952 \tabularnewline
85 &  9 &  9.113 & -0.1132 \tabularnewline
86 &  8 &  6.975 &  1.025 \tabularnewline
87 &  5 &  6.257 & -1.257 \tabularnewline
88 &  7 &  7.602 & -0.6021 \tabularnewline
89 &  9 &  9.832 & -0.8323 \tabularnewline
90 &  8 &  8.447 & -0.4466 \tabularnewline
91 &  4 &  8.011 & -4.011 \tabularnewline
92 &  7 &  6.711 &  0.2885 \tabularnewline
93 &  8 &  9.008 & -1.008 \tabularnewline
94 &  7 &  7.573 & -0.5728 \tabularnewline
95 &  7 &  7.297 & -0.2974 \tabularnewline
96 &  9 &  7.784 &  1.216 \tabularnewline
97 &  6 &  6.727 & -0.7275 \tabularnewline
98 &  7 &  7.844 & -0.8439 \tabularnewline
99 &  4 &  5.209 & -1.209 \tabularnewline
100 &  6 &  6.65 & -0.6497 \tabularnewline
101 &  10 &  6.8 &  3.2 \tabularnewline
102 &  9 &  8.427 &  0.5727 \tabularnewline
103 &  10 &  10.02 & -0.01996 \tabularnewline
104 &  8 &  7.538 &  0.4623 \tabularnewline
105 &  4 &  5.287 & -1.287 \tabularnewline
106 &  8 &  9.808 & -1.808 \tabularnewline
107 &  5 &  7.154 & -2.154 \tabularnewline
108 &  8 &  7.292 &  0.7078 \tabularnewline
109 &  9 &  7.636 &  1.364 \tabularnewline
110 &  8 &  7.685 &  0.3147 \tabularnewline
111 &  4 &  8.1 & -4.1 \tabularnewline
112 &  8 &  6.73 &  1.27 \tabularnewline
113 &  10 &  8.199 &  1.801 \tabularnewline
114 &  6 &  6.43 & -0.4297 \tabularnewline
115 &  7 &  6.47 &  0.53 \tabularnewline
116 &  10 &  8.812 &  1.188 \tabularnewline
117 &  9 &  9.398 & -0.3982 \tabularnewline
118 &  8 &  8.422 & -0.4219 \tabularnewline
119 &  3 &  5.69 & -2.69 \tabularnewline
120 &  8 &  6.986 &  1.014 \tabularnewline
121 &  7 &  7.542 & -0.5421 \tabularnewline
122 &  7 &  7.369 & -0.3687 \tabularnewline
123 &  8 &  6.686 &  1.314 \tabularnewline
124 &  8 &  8.429 & -0.4287 \tabularnewline
125 &  7 &  7.664 & -0.6636 \tabularnewline
126 &  7 &  5.628 &  1.372 \tabularnewline
127 &  9 &  10.27 & -1.273 \tabularnewline
128 &  9 &  8.19 &  0.8102 \tabularnewline
129 &  9 &  7.426 &  1.574 \tabularnewline
130 &  4 &  5.025 & -1.025 \tabularnewline
131 &  6 &  6.999 & -0.9989 \tabularnewline
132 &  6 &  6.054 & -0.05362 \tabularnewline
133 &  6 &  4.337 &  1.663 \tabularnewline
134 &  8 &  8.178 & -0.178 \tabularnewline
135 &  3 &  4.092 & -1.092 \tabularnewline
136 &  8 &  6.032 &  1.968 \tabularnewline
137 &  8 &  7.366 &  0.6342 \tabularnewline
138 &  6 &  4.612 &  1.388 \tabularnewline
139 &  10 &  9.244 &  0.7557 \tabularnewline
140 &  2 &  4.321 & -2.321 \tabularnewline
141 &  9 &  7.38 &  1.62 \tabularnewline
142 &  6 &  5.577 &  0.4232 \tabularnewline
143 &  6 &  7.705 & -1.705 \tabularnewline
144 &  5 &  4.458 &  0.542 \tabularnewline
145 &  4 &  4.593 & -0.593 \tabularnewline
146 &  7 &  6.786 &  0.2136 \tabularnewline
147 &  5 &  5.697 & -0.6968 \tabularnewline
148 &  8 &  7.922 &  0.07773 \tabularnewline
149 &  6 &  6.725 & -0.7253 \tabularnewline
150 &  9 &  6.868 &  2.132 \tabularnewline
151 &  6 &  6.336 & -0.3365 \tabularnewline
152 &  4 &  4.979 & -0.9792 \tabularnewline
153 &  7 &  7.242 & -0.2422 \tabularnewline
154 &  2 &  3.83 & -1.83 \tabularnewline
155 &  8 &  9.148 & -1.148 \tabularnewline
156 &  9 &  8.504 &  0.4961 \tabularnewline
157 &  6 &  6.378 & -0.378 \tabularnewline
158 &  5 &  4.435 &  0.5646 \tabularnewline
159 &  7 &  6.731 &  0.269 \tabularnewline
160 &  8 &  7.247 &  0.7527 \tabularnewline
161 &  4 &  6.302 & -2.302 \tabularnewline
162 &  9 &  6.174 &  2.826 \tabularnewline
163 &  9 &  9.608 & -0.6076 \tabularnewline
164 &  9 &  5.226 &  3.774 \tabularnewline
165 &  7 &  5.916 &  1.084 \tabularnewline
166 &  5 &  7.251 & -2.251 \tabularnewline
167 &  7 &  6.709 &  0.2907 \tabularnewline
168 &  9 &  10.13 & -1.135 \tabularnewline
169 &  8 &  6.584 &  1.416 \tabularnewline
170 &  6 &  5.41 &  0.59 \tabularnewline
171 &  9 &  7.779 &  1.221 \tabularnewline
172 &  8 &  7.903 &  0.0971 \tabularnewline
173 &  7 &  7.906 & -0.906 \tabularnewline
174 &  7 &  7.598 & -0.5977 \tabularnewline
175 &  7 &  6.563 &  0.4372 \tabularnewline
176 &  8 &  7.258 &  0.7425 \tabularnewline
177 &  10 &  8.771 &  1.229 \tabularnewline
178 &  6 &  6.953 & -0.9526 \tabularnewline
179 &  6 &  6.755 & -0.7553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&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] 10[/C][C] 8.271[/C][C] 1.729[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 7.878[/C][C] 0.1224[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 7.476[/C][C] 0.5238[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 9.502[/C][C]-0.5018[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 6.864[/C][C]-1.864[/C][/ROW]
[ROW][C]6[/C][C] 10[/C][C] 9.928[/C][C] 0.07174[/C][/ROW]
[ROW][C]7[/C][C] 8[/C][C] 8.381[/C][C]-0.3806[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 9.299[/C][C]-0.2985[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 6.057[/C][C] 1.943[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 8.248[/C][C]-1.248[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 8.647[/C][C] 1.353[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.156[/C][C] 2.844[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 7.877[/C][C] 1.123[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 6.325[/C][C]-2.325[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 6.899[/C][C]-2.899[/C][/ROW]
[ROW][C]16[/C][C] 8[/C][C] 7.753[/C][C] 0.2469[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 9.731[/C][C]-0.7311[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 8.022[/C][C] 1.978[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 8.089[/C][C]-0.08931[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.649[/C][C]-1.649[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 8.21[/C][C] 1.79[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 8.655[/C][C]-0.655[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 7.955[/C][C]-0.9552[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 8.501[/C][C]-0.5014[/C][/ROW]
[ROW][C]25[/C][C] 8[/C][C] 9.475[/C][C]-1.475[/C][/ROW]
[ROW][C]26[/C][C] 9[/C][C] 6.574[/C][C] 2.426[/C][/ROW]
[ROW][C]27[/C][C] 8[/C][C] 8.389[/C][C]-0.3887[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 7.42[/C][C]-1.42[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 8.436[/C][C]-0.4357[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 7.412[/C][C] 0.5883[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 6.731[/C][C]-1.731[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 8.584[/C][C] 0.4162[/C][/ROW]
[ROW][C]33[/C][C] 8[/C][C] 8.136[/C][C]-0.1356[/C][/ROW]
[ROW][C]34[/C][C] 8[/C][C] 6.439[/C][C] 1.561[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 8.619[/C][C]-0.6195[/C][/ROW]
[ROW][C]36[/C][C] 6[/C][C] 5.87[/C][C] 0.1301[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 6.486[/C][C]-0.4857[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 7.81[/C][C] 1.19[/C][/ROW]
[ROW][C]39[/C][C] 8[/C][C] 7.495[/C][C] 0.5049[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 9.267[/C][C]-0.2667[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 8.101[/C][C] 1.899[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 7.016[/C][C] 0.9836[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 7.779[/C][C] 0.221[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 7.177[/C][C]-0.1769[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 7.162[/C][C]-0.1622[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 9.211[/C][C] 0.7891[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 6.588[/C][C] 1.412[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 6.482[/C][C] 0.5182[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 7.635[/C][C] 2.365[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 8.244[/C][C]-1.244[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 5.909[/C][C] 1.091[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.578[/C][C] 0.4222[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 9.998[/C][C]-0.998[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 7.245[/C][C] 0.7547[/C][/ROW]
[ROW][C]55[/C][C] 6[/C][C] 7.444[/C][C]-1.444[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 7.431[/C][C] 0.5686[/C][/ROW]
[ROW][C]57[/C][C] 9[/C][C] 7.646[/C][C] 1.354[/C][/ROW]
[ROW][C]58[/C][C] 2[/C][C] 3.343[/C][C]-1.343[/C][/ROW]
[ROW][C]59[/C][C] 6[/C][C] 6.097[/C][C]-0.09686[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 7.757[/C][C] 0.2428[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 7.744[/C][C] 0.2557[/C][/ROW]
[ROW][C]62[/C][C] 7[/C][C] 7.243[/C][C]-0.2432[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 7.538[/C][C] 0.4616[/C][/ROW]
[ROW][C]64[/C][C] 6[/C][C] 5.935[/C][C] 0.06508[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 7.74[/C][C] 2.26[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 8.195[/C][C] 1.805[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 7.675[/C][C] 2.325[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 7.317[/C][C] 0.6828[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 8.336[/C][C]-0.3356[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.998[/C][C]-0.9979[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 9.025[/C][C] 0.9751[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 6.173[/C][C]-1.173[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 3[/C][C]-0.0003373[/C][/ROW]
[ROW][C]74[/C][C] 2[/C][C] 3.715[/C][C]-1.715[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 4.349[/C][C]-1.349[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 5.652[/C][C]-1.652[/C][/ROW]
[ROW][C]77[/C][C] 2[/C][C] 3.483[/C][C]-1.483[/C][/ROW]
[ROW][C]78[/C][C] 6[/C][C] 5.075[/C][C] 0.925[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 8.227[/C][C]-0.2265[/C][/ROW]
[ROW][C]80[/C][C] 8[/C][C] 7.225[/C][C] 0.7749[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 5.34[/C][C]-0.3398[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 9.107[/C][C] 0.8931[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 9.865[/C][C]-0.8646[/C][/ROW]
[ROW][C]84[/C][C] 8[/C][C] 9.952[/C][C]-1.952[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 9.113[/C][C]-0.1132[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 6.975[/C][C] 1.025[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 6.257[/C][C]-1.257[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 7.602[/C][C]-0.6021[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 9.832[/C][C]-0.8323[/C][/ROW]
[ROW][C]90[/C][C] 8[/C][C] 8.447[/C][C]-0.4466[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 8.011[/C][C]-4.011[/C][/ROW]
[ROW][C]92[/C][C] 7[/C][C] 6.711[/C][C] 0.2885[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 9.008[/C][C]-1.008[/C][/ROW]
[ROW][C]94[/C][C] 7[/C][C] 7.573[/C][C]-0.5728[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 7.297[/C][C]-0.2974[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.784[/C][C] 1.216[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 6.727[/C][C]-0.7275[/C][/ROW]
[ROW][C]98[/C][C] 7[/C][C] 7.844[/C][C]-0.8439[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 5.209[/C][C]-1.209[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 6.65[/C][C]-0.6497[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 6.8[/C][C] 3.2[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 8.427[/C][C] 0.5727[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 10.02[/C][C]-0.01996[/C][/ROW]
[ROW][C]104[/C][C] 8[/C][C] 7.538[/C][C] 0.4623[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 5.287[/C][C]-1.287[/C][/ROW]
[ROW][C]106[/C][C] 8[/C][C] 9.808[/C][C]-1.808[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 7.154[/C][C]-2.154[/C][/ROW]
[ROW][C]108[/C][C] 8[/C][C] 7.292[/C][C] 0.7078[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 7.636[/C][C] 1.364[/C][/ROW]
[ROW][C]110[/C][C] 8[/C][C] 7.685[/C][C] 0.3147[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 8.1[/C][C]-4.1[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 6.73[/C][C] 1.27[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 8.199[/C][C] 1.801[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 6.43[/C][C]-0.4297[/C][/ROW]
[ROW][C]115[/C][C] 7[/C][C] 6.47[/C][C] 0.53[/C][/ROW]
[ROW][C]116[/C][C] 10[/C][C] 8.812[/C][C] 1.188[/C][/ROW]
[ROW][C]117[/C][C] 9[/C][C] 9.398[/C][C]-0.3982[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.422[/C][C]-0.4219[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 5.69[/C][C]-2.69[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 6.986[/C][C] 1.014[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 7.542[/C][C]-0.5421[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 7.369[/C][C]-0.3687[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 6.686[/C][C] 1.314[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 8.429[/C][C]-0.4287[/C][/ROW]
[ROW][C]125[/C][C] 7[/C][C] 7.664[/C][C]-0.6636[/C][/ROW]
[ROW][C]126[/C][C] 7[/C][C] 5.628[/C][C] 1.372[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 10.27[/C][C]-1.273[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 8.19[/C][C] 0.8102[/C][/ROW]
[ROW][C]129[/C][C] 9[/C][C] 7.426[/C][C] 1.574[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 5.025[/C][C]-1.025[/C][/ROW]
[ROW][C]131[/C][C] 6[/C][C] 6.999[/C][C]-0.9989[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 6.054[/C][C]-0.05362[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 4.337[/C][C] 1.663[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 8.178[/C][C]-0.178[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 4.092[/C][C]-1.092[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 6.032[/C][C] 1.968[/C][/ROW]
[ROW][C]137[/C][C] 8[/C][C] 7.366[/C][C] 0.6342[/C][/ROW]
[ROW][C]138[/C][C] 6[/C][C] 4.612[/C][C] 1.388[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 9.244[/C][C] 0.7557[/C][/ROW]
[ROW][C]140[/C][C] 2[/C][C] 4.321[/C][C]-2.321[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 7.38[/C][C] 1.62[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 5.577[/C][C] 0.4232[/C][/ROW]
[ROW][C]143[/C][C] 6[/C][C] 7.705[/C][C]-1.705[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 4.458[/C][C] 0.542[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4.593[/C][C]-0.593[/C][/ROW]
[ROW][C]146[/C][C] 7[/C][C] 6.786[/C][C] 0.2136[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 5.697[/C][C]-0.6968[/C][/ROW]
[ROW][C]148[/C][C] 8[/C][C] 7.922[/C][C] 0.07773[/C][/ROW]
[ROW][C]149[/C][C] 6[/C][C] 6.725[/C][C]-0.7253[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 6.868[/C][C] 2.132[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 6.336[/C][C]-0.3365[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 4.979[/C][C]-0.9792[/C][/ROW]
[ROW][C]153[/C][C] 7[/C][C] 7.242[/C][C]-0.2422[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 3.83[/C][C]-1.83[/C][/ROW]
[ROW][C]155[/C][C] 8[/C][C] 9.148[/C][C]-1.148[/C][/ROW]
[ROW][C]156[/C][C] 9[/C][C] 8.504[/C][C] 0.4961[/C][/ROW]
[ROW][C]157[/C][C] 6[/C][C] 6.378[/C][C]-0.378[/C][/ROW]
[ROW][C]158[/C][C] 5[/C][C] 4.435[/C][C] 0.5646[/C][/ROW]
[ROW][C]159[/C][C] 7[/C][C] 6.731[/C][C] 0.269[/C][/ROW]
[ROW][C]160[/C][C] 8[/C][C] 7.247[/C][C] 0.7527[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 6.302[/C][C]-2.302[/C][/ROW]
[ROW][C]162[/C][C] 9[/C][C] 6.174[/C][C] 2.826[/C][/ROW]
[ROW][C]163[/C][C] 9[/C][C] 9.608[/C][C]-0.6076[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 5.226[/C][C] 3.774[/C][/ROW]
[ROW][C]165[/C][C] 7[/C][C] 5.916[/C][C] 1.084[/C][/ROW]
[ROW][C]166[/C][C] 5[/C][C] 7.251[/C][C]-2.251[/C][/ROW]
[ROW][C]167[/C][C] 7[/C][C] 6.709[/C][C] 0.2907[/C][/ROW]
[ROW][C]168[/C][C] 9[/C][C] 10.13[/C][C]-1.135[/C][/ROW]
[ROW][C]169[/C][C] 8[/C][C] 6.584[/C][C] 1.416[/C][/ROW]
[ROW][C]170[/C][C] 6[/C][C] 5.41[/C][C] 0.59[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 7.779[/C][C] 1.221[/C][/ROW]
[ROW][C]172[/C][C] 8[/C][C] 7.903[/C][C] 0.0971[/C][/ROW]
[ROW][C]173[/C][C] 7[/C][C] 7.906[/C][C]-0.906[/C][/ROW]
[ROW][C]174[/C][C] 7[/C][C] 7.598[/C][C]-0.5977[/C][/ROW]
[ROW][C]175[/C][C] 7[/C][C] 6.563[/C][C] 0.4372[/C][/ROW]
[ROW][C]176[/C][C] 8[/C][C] 7.258[/C][C] 0.7425[/C][/ROW]
[ROW][C]177[/C][C] 10[/C][C] 8.771[/C][C] 1.229[/C][/ROW]
[ROW][C]178[/C][C] 6[/C][C] 6.953[/C][C]-0.9526[/C][/ROW]
[ROW][C]179[/C][C] 6[/C][C] 6.755[/C][C]-0.7553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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 IndexActualsInterpolationForecastResidualsPrediction Error
1 10 8.271 1.729
2 8 7.878 0.1224
3 8 7.476 0.5238
4 9 9.502-0.5018
5 5 6.864-1.864
6 10 9.928 0.07174
7 8 8.381-0.3806
8 9 9.299-0.2985
9 8 6.057 1.943
10 7 8.248-1.248
11 10 8.647 1.353
12 10 7.156 2.844
13 9 7.877 1.123
14 4 6.325-2.325
15 4 6.899-2.899
16 8 7.753 0.2469
17 9 9.731-0.7311
18 10 8.022 1.978
19 8 8.089-0.08931
20 5 6.649-1.649
21 10 8.21 1.79
22 8 8.655-0.655
23 7 7.955-0.9552
24 8 8.501-0.5014
25 8 9.475-1.475
26 9 6.574 2.426
27 8 8.389-0.3887
28 6 7.42-1.42
29 8 8.436-0.4357
30 8 7.412 0.5883
31 5 6.731-1.731
32 9 8.584 0.4162
33 8 8.136-0.1356
34 8 6.439 1.561
35 8 8.619-0.6195
36 6 5.87 0.1301
37 6 6.486-0.4857
38 9 7.81 1.19
39 8 7.495 0.5049
40 9 9.267-0.2667
41 10 8.101 1.899
42 8 7.016 0.9836
43 8 7.779 0.221
44 7 7.177-0.1769
45 7 7.162-0.1622
46 10 9.211 0.7891
47 8 6.588 1.412
48 7 6.482 0.5182
49 10 7.635 2.365
50 7 8.244-1.244
51 7 5.909 1.091
52 9 8.578 0.4222
53 9 9.998-0.998
54 8 7.245 0.7547
55 6 7.444-1.444
56 8 7.431 0.5686
57 9 7.646 1.354
58 2 3.343-1.343
59 6 6.097-0.09686
60 8 7.757 0.2428
61 8 7.744 0.2557
62 7 7.243-0.2432
63 8 7.538 0.4616
64 6 5.935 0.06508
65 10 7.74 2.26
66 10 8.195 1.805
67 10 7.675 2.325
68 8 7.317 0.6828
69 8 8.336-0.3356
70 7 7.998-0.9979
71 10 9.025 0.9751
72 5 6.173-1.173
73 3 3-0.0003373
74 2 3.715-1.715
75 3 4.349-1.349
76 4 5.652-1.652
77 2 3.483-1.483
78 6 5.075 0.925
79 8 8.227-0.2265
80 8 7.225 0.7749
81 5 5.34-0.3398
82 10 9.107 0.8931
83 9 9.865-0.8646
84 8 9.952-1.952
85 9 9.113-0.1132
86 8 6.975 1.025
87 5 6.257-1.257
88 7 7.602-0.6021
89 9 9.832-0.8323
90 8 8.447-0.4466
91 4 8.011-4.011
92 7 6.711 0.2885
93 8 9.008-1.008
94 7 7.573-0.5728
95 7 7.297-0.2974
96 9 7.784 1.216
97 6 6.727-0.7275
98 7 7.844-0.8439
99 4 5.209-1.209
100 6 6.65-0.6497
101 10 6.8 3.2
102 9 8.427 0.5727
103 10 10.02-0.01996
104 8 7.538 0.4623
105 4 5.287-1.287
106 8 9.808-1.808
107 5 7.154-2.154
108 8 7.292 0.7078
109 9 7.636 1.364
110 8 7.685 0.3147
111 4 8.1-4.1
112 8 6.73 1.27
113 10 8.199 1.801
114 6 6.43-0.4297
115 7 6.47 0.53
116 10 8.812 1.188
117 9 9.398-0.3982
118 8 8.422-0.4219
119 3 5.69-2.69
120 8 6.986 1.014
121 7 7.542-0.5421
122 7 7.369-0.3687
123 8 6.686 1.314
124 8 8.429-0.4287
125 7 7.664-0.6636
126 7 5.628 1.372
127 9 10.27-1.273
128 9 8.19 0.8102
129 9 7.426 1.574
130 4 5.025-1.025
131 6 6.999-0.9989
132 6 6.054-0.05362
133 6 4.337 1.663
134 8 8.178-0.178
135 3 4.092-1.092
136 8 6.032 1.968
137 8 7.366 0.6342
138 6 4.612 1.388
139 10 9.244 0.7557
140 2 4.321-2.321
141 9 7.38 1.62
142 6 5.577 0.4232
143 6 7.705-1.705
144 5 4.458 0.542
145 4 4.593-0.593
146 7 6.786 0.2136
147 5 5.697-0.6968
148 8 7.922 0.07773
149 6 6.725-0.7253
150 9 6.868 2.132
151 6 6.336-0.3365
152 4 4.979-0.9792
153 7 7.242-0.2422
154 2 3.83-1.83
155 8 9.148-1.148
156 9 8.504 0.4961
157 6 6.378-0.378
158 5 4.435 0.5646
159 7 6.731 0.269
160 8 7.247 0.7527
161 4 6.302-2.302
162 9 6.174 2.826
163 9 9.608-0.6076
164 9 5.226 3.774
165 7 5.916 1.084
166 5 7.251-2.251
167 7 6.709 0.2907
168 9 10.13-1.135
169 8 6.584 1.416
170 6 5.41 0.59
171 9 7.779 1.221
172 8 7.903 0.0971
173 7 7.906-0.906
174 7 7.598-0.5977
175 7 6.563 0.4372
176 8 7.258 0.7425
177 10 8.771 1.229
178 6 6.953-0.9526
179 6 6.755-0.7553







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.7583 0.4835 0.2417
12 0.8911 0.2178 0.1089
13 0.8352 0.3296 0.1648
14 0.9764 0.04726 0.02363
15 0.9825 0.03509 0.01754
16 0.9777 0.04462 0.02231
17 0.9675 0.06494 0.03247
18 0.96 0.08007 0.04004
19 0.9421 0.1157 0.05787
20 0.94 0.12 0.06002
21 0.9557 0.08866 0.04433
22 0.9483 0.1033 0.05166
23 0.9328 0.1345 0.06725
24 0.9093 0.1813 0.09066
25 0.9142 0.1716 0.08581
26 0.972 0.05599 0.028
27 0.9629 0.07428 0.03714
28 0.9607 0.07859 0.03929
29 0.9465 0.107 0.05349
30 0.9282 0.1436 0.07182
31 0.9126 0.1749 0.08744
32 0.89 0.2199 0.11
33 0.8623 0.2755 0.1377
34 0.8742 0.2516 0.1258
35 0.8509 0.2981 0.1491
36 0.8195 0.3611 0.1805
37 0.7874 0.4252 0.2126
38 0.7858 0.4284 0.2142
39 0.7473 0.5055 0.2527
40 0.7015 0.597 0.2985
41 0.7472 0.5056 0.2528
42 0.7594 0.4813 0.2406
43 0.7169 0.5662 0.2831
44 0.6709 0.6583 0.3291
45 0.6225 0.7549 0.3775
46 0.5873 0.8253 0.4127
47 0.5873 0.8253 0.4127
48 0.5399 0.9203 0.4601
49 0.6316 0.7367 0.3684
50 0.6257 0.7487 0.3743
51 0.5942 0.8117 0.4058
52 0.5535 0.8931 0.4465
53 0.5196 0.9609 0.4804
54 0.4798 0.9596 0.5202
55 0.4889 0.9779 0.5111
56 0.4502 0.9003 0.5498
57 0.441 0.882 0.559
58 0.4306 0.8612 0.5694
59 0.3865 0.7729 0.6135
60 0.3423 0.6845 0.6577
61 0.3182 0.6363 0.6818
62 0.2772 0.5543 0.7228
63 0.2433 0.4866 0.7567
64 0.2078 0.4156 0.7922
65 0.2744 0.5488 0.7256
66 0.3091 0.6182 0.6909
67 0.3918 0.7836 0.6082
68 0.359 0.7181 0.641
69 0.3215 0.6431 0.6785
70 0.3072 0.6145 0.6928
71 0.2898 0.5796 0.7102
72 0.2713 0.5425 0.7287
73 0.2354 0.4709 0.7646
74 0.2426 0.4852 0.7574
75 0.2276 0.4552 0.7724
76 0.23 0.4599 0.77
77 0.2223 0.4447 0.7777
78 0.2193 0.4386 0.7807
79 0.1923 0.3845 0.8077
80 0.1715 0.343 0.8285
81 0.1453 0.2907 0.8547
82 0.1373 0.2747 0.8627
83 0.125 0.25 0.875
84 0.1544 0.3087 0.8456
85 0.1295 0.2591 0.8705
86 0.1219 0.2438 0.8781
87 0.1257 0.2514 0.8743
88 0.1086 0.2171 0.8914
89 0.09603 0.1921 0.904
90 0.08163 0.1633 0.9184
91 0.3212 0.6424 0.6788
92 0.285 0.5701 0.715
93 0.2675 0.5351 0.7325
94 0.2403 0.4807 0.7597
95 0.2088 0.4176 0.7912
96 0.208 0.4159 0.792
97 0.1872 0.3743 0.8128
98 0.1698 0.3396 0.8302
99 0.1668 0.3336 0.8332
100 0.1467 0.2934 0.8533
101 0.3209 0.6418 0.6791
102 0.2879 0.5759 0.7121
103 0.2511 0.5022 0.7489
104 0.2259 0.4519 0.7741
105 0.2146 0.4292 0.7854
106 0.2425 0.485 0.7575
107 0.2926 0.5851 0.7074
108 0.2794 0.5588 0.7206
109 0.29 0.58 0.71
110 0.257 0.5139 0.743
111 0.636 0.728 0.364
112 0.6422 0.7156 0.3578
113 0.6657 0.6685 0.3343
114 0.6259 0.7482 0.3741
115 0.5982 0.8036 0.4018
116 0.585 0.83 0.415
117 0.5434 0.9132 0.4566
118 0.5072 0.9856 0.4928
119 0.6489 0.7022 0.3511
120 0.6509 0.6982 0.3491
121 0.6101 0.7798 0.3899
122 0.5665 0.867 0.4335
123 0.5797 0.8405 0.4203
124 0.5379 0.9243 0.4621
125 0.4965 0.9929 0.5035
126 0.4908 0.9817 0.5092
127 0.4759 0.9518 0.5241
128 0.4425 0.8851 0.5575
129 0.5287 0.9426 0.4713
130 0.5406 0.9188 0.4594
131 0.5028 0.9944 0.4972
132 0.451 0.9021 0.549
133 0.5219 0.9561 0.4781
134 0.4817 0.9634 0.5183
135 0.4576 0.9152 0.5424
136 0.5436 0.9127 0.4564
137 0.497 0.9941 0.503
138 0.4923 0.9846 0.5077
139 0.5117 0.9766 0.4883
140 0.6708 0.6584 0.3292
141 0.6702 0.6596 0.3298
142 0.6179 0.7642 0.3821
143 0.6396 0.7207 0.3604
144 0.596 0.808 0.404
145 0.5393 0.9214 0.4607
146 0.5023 0.9954 0.4977
147 0.5232 0.9536 0.4768
148 0.4686 0.9373 0.5314
149 0.465 0.9301 0.535
150 0.5536 0.8929 0.4464
151 0.4869 0.9738 0.5131
152 0.4985 0.997 0.5015
153 0.4284 0.8567 0.5716
154 0.5979 0.8043 0.4021
155 0.5463 0.9073 0.4537
156 0.488 0.976 0.512
157 0.4123 0.8246 0.5877
158 0.3894 0.7789 0.6106
159 0.3286 0.6572 0.6714
160 0.2822 0.5644 0.7178
161 0.3539 0.7078 0.6461
162 0.4083 0.8167 0.5917
163 0.4903 0.9807 0.5097
164 0.5346 0.9307 0.4654
165 0.4199 0.8398 0.5801
166 0.9452 0.1097 0.05484
167 0.9247 0.1507 0.07535
168 0.8286 0.3427 0.1714

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.7583 &  0.4835 &  0.2417 \tabularnewline
12 &  0.8911 &  0.2178 &  0.1089 \tabularnewline
13 &  0.8352 &  0.3296 &  0.1648 \tabularnewline
14 &  0.9764 &  0.04726 &  0.02363 \tabularnewline
15 &  0.9825 &  0.03509 &  0.01754 \tabularnewline
16 &  0.9777 &  0.04462 &  0.02231 \tabularnewline
17 &  0.9675 &  0.06494 &  0.03247 \tabularnewline
18 &  0.96 &  0.08007 &  0.04004 \tabularnewline
19 &  0.9421 &  0.1157 &  0.05787 \tabularnewline
20 &  0.94 &  0.12 &  0.06002 \tabularnewline
21 &  0.9557 &  0.08866 &  0.04433 \tabularnewline
22 &  0.9483 &  0.1033 &  0.05166 \tabularnewline
23 &  0.9328 &  0.1345 &  0.06725 \tabularnewline
24 &  0.9093 &  0.1813 &  0.09066 \tabularnewline
25 &  0.9142 &  0.1716 &  0.08581 \tabularnewline
26 &  0.972 &  0.05599 &  0.028 \tabularnewline
27 &  0.9629 &  0.07428 &  0.03714 \tabularnewline
28 &  0.9607 &  0.07859 &  0.03929 \tabularnewline
29 &  0.9465 &  0.107 &  0.05349 \tabularnewline
30 &  0.9282 &  0.1436 &  0.07182 \tabularnewline
31 &  0.9126 &  0.1749 &  0.08744 \tabularnewline
32 &  0.89 &  0.2199 &  0.11 \tabularnewline
33 &  0.8623 &  0.2755 &  0.1377 \tabularnewline
34 &  0.8742 &  0.2516 &  0.1258 \tabularnewline
35 &  0.8509 &  0.2981 &  0.1491 \tabularnewline
36 &  0.8195 &  0.3611 &  0.1805 \tabularnewline
37 &  0.7874 &  0.4252 &  0.2126 \tabularnewline
38 &  0.7858 &  0.4284 &  0.2142 \tabularnewline
39 &  0.7473 &  0.5055 &  0.2527 \tabularnewline
40 &  0.7015 &  0.597 &  0.2985 \tabularnewline
41 &  0.7472 &  0.5056 &  0.2528 \tabularnewline
42 &  0.7594 &  0.4813 &  0.2406 \tabularnewline
43 &  0.7169 &  0.5662 &  0.2831 \tabularnewline
44 &  0.6709 &  0.6583 &  0.3291 \tabularnewline
45 &  0.6225 &  0.7549 &  0.3775 \tabularnewline
46 &  0.5873 &  0.8253 &  0.4127 \tabularnewline
47 &  0.5873 &  0.8253 &  0.4127 \tabularnewline
48 &  0.5399 &  0.9203 &  0.4601 \tabularnewline
49 &  0.6316 &  0.7367 &  0.3684 \tabularnewline
50 &  0.6257 &  0.7487 &  0.3743 \tabularnewline
51 &  0.5942 &  0.8117 &  0.4058 \tabularnewline
52 &  0.5535 &  0.8931 &  0.4465 \tabularnewline
53 &  0.5196 &  0.9609 &  0.4804 \tabularnewline
54 &  0.4798 &  0.9596 &  0.5202 \tabularnewline
55 &  0.4889 &  0.9779 &  0.5111 \tabularnewline
56 &  0.4502 &  0.9003 &  0.5498 \tabularnewline
57 &  0.441 &  0.882 &  0.559 \tabularnewline
58 &  0.4306 &  0.8612 &  0.5694 \tabularnewline
59 &  0.3865 &  0.7729 &  0.6135 \tabularnewline
60 &  0.3423 &  0.6845 &  0.6577 \tabularnewline
61 &  0.3182 &  0.6363 &  0.6818 \tabularnewline
62 &  0.2772 &  0.5543 &  0.7228 \tabularnewline
63 &  0.2433 &  0.4866 &  0.7567 \tabularnewline
64 &  0.2078 &  0.4156 &  0.7922 \tabularnewline
65 &  0.2744 &  0.5488 &  0.7256 \tabularnewline
66 &  0.3091 &  0.6182 &  0.6909 \tabularnewline
67 &  0.3918 &  0.7836 &  0.6082 \tabularnewline
68 &  0.359 &  0.7181 &  0.641 \tabularnewline
69 &  0.3215 &  0.6431 &  0.6785 \tabularnewline
70 &  0.3072 &  0.6145 &  0.6928 \tabularnewline
71 &  0.2898 &  0.5796 &  0.7102 \tabularnewline
72 &  0.2713 &  0.5425 &  0.7287 \tabularnewline
73 &  0.2354 &  0.4709 &  0.7646 \tabularnewline
74 &  0.2426 &  0.4852 &  0.7574 \tabularnewline
75 &  0.2276 &  0.4552 &  0.7724 \tabularnewline
76 &  0.23 &  0.4599 &  0.77 \tabularnewline
77 &  0.2223 &  0.4447 &  0.7777 \tabularnewline
78 &  0.2193 &  0.4386 &  0.7807 \tabularnewline
79 &  0.1923 &  0.3845 &  0.8077 \tabularnewline
80 &  0.1715 &  0.343 &  0.8285 \tabularnewline
81 &  0.1453 &  0.2907 &  0.8547 \tabularnewline
82 &  0.1373 &  0.2747 &  0.8627 \tabularnewline
83 &  0.125 &  0.25 &  0.875 \tabularnewline
84 &  0.1544 &  0.3087 &  0.8456 \tabularnewline
85 &  0.1295 &  0.2591 &  0.8705 \tabularnewline
86 &  0.1219 &  0.2438 &  0.8781 \tabularnewline
87 &  0.1257 &  0.2514 &  0.8743 \tabularnewline
88 &  0.1086 &  0.2171 &  0.8914 \tabularnewline
89 &  0.09603 &  0.1921 &  0.904 \tabularnewline
90 &  0.08163 &  0.1633 &  0.9184 \tabularnewline
91 &  0.3212 &  0.6424 &  0.6788 \tabularnewline
92 &  0.285 &  0.5701 &  0.715 \tabularnewline
93 &  0.2675 &  0.5351 &  0.7325 \tabularnewline
94 &  0.2403 &  0.4807 &  0.7597 \tabularnewline
95 &  0.2088 &  0.4176 &  0.7912 \tabularnewline
96 &  0.208 &  0.4159 &  0.792 \tabularnewline
97 &  0.1872 &  0.3743 &  0.8128 \tabularnewline
98 &  0.1698 &  0.3396 &  0.8302 \tabularnewline
99 &  0.1668 &  0.3336 &  0.8332 \tabularnewline
100 &  0.1467 &  0.2934 &  0.8533 \tabularnewline
101 &  0.3209 &  0.6418 &  0.6791 \tabularnewline
102 &  0.2879 &  0.5759 &  0.7121 \tabularnewline
103 &  0.2511 &  0.5022 &  0.7489 \tabularnewline
104 &  0.2259 &  0.4519 &  0.7741 \tabularnewline
105 &  0.2146 &  0.4292 &  0.7854 \tabularnewline
106 &  0.2425 &  0.485 &  0.7575 \tabularnewline
107 &  0.2926 &  0.5851 &  0.7074 \tabularnewline
108 &  0.2794 &  0.5588 &  0.7206 \tabularnewline
109 &  0.29 &  0.58 &  0.71 \tabularnewline
110 &  0.257 &  0.5139 &  0.743 \tabularnewline
111 &  0.636 &  0.728 &  0.364 \tabularnewline
112 &  0.6422 &  0.7156 &  0.3578 \tabularnewline
113 &  0.6657 &  0.6685 &  0.3343 \tabularnewline
114 &  0.6259 &  0.7482 &  0.3741 \tabularnewline
115 &  0.5982 &  0.8036 &  0.4018 \tabularnewline
116 &  0.585 &  0.83 &  0.415 \tabularnewline
117 &  0.5434 &  0.9132 &  0.4566 \tabularnewline
118 &  0.5072 &  0.9856 &  0.4928 \tabularnewline
119 &  0.6489 &  0.7022 &  0.3511 \tabularnewline
120 &  0.6509 &  0.6982 &  0.3491 \tabularnewline
121 &  0.6101 &  0.7798 &  0.3899 \tabularnewline
122 &  0.5665 &  0.867 &  0.4335 \tabularnewline
123 &  0.5797 &  0.8405 &  0.4203 \tabularnewline
124 &  0.5379 &  0.9243 &  0.4621 \tabularnewline
125 &  0.4965 &  0.9929 &  0.5035 \tabularnewline
126 &  0.4908 &  0.9817 &  0.5092 \tabularnewline
127 &  0.4759 &  0.9518 &  0.5241 \tabularnewline
128 &  0.4425 &  0.8851 &  0.5575 \tabularnewline
129 &  0.5287 &  0.9426 &  0.4713 \tabularnewline
130 &  0.5406 &  0.9188 &  0.4594 \tabularnewline
131 &  0.5028 &  0.9944 &  0.4972 \tabularnewline
132 &  0.451 &  0.9021 &  0.549 \tabularnewline
133 &  0.5219 &  0.9561 &  0.4781 \tabularnewline
134 &  0.4817 &  0.9634 &  0.5183 \tabularnewline
135 &  0.4576 &  0.9152 &  0.5424 \tabularnewline
136 &  0.5436 &  0.9127 &  0.4564 \tabularnewline
137 &  0.497 &  0.9941 &  0.503 \tabularnewline
138 &  0.4923 &  0.9846 &  0.5077 \tabularnewline
139 &  0.5117 &  0.9766 &  0.4883 \tabularnewline
140 &  0.6708 &  0.6584 &  0.3292 \tabularnewline
141 &  0.6702 &  0.6596 &  0.3298 \tabularnewline
142 &  0.6179 &  0.7642 &  0.3821 \tabularnewline
143 &  0.6396 &  0.7207 &  0.3604 \tabularnewline
144 &  0.596 &  0.808 &  0.404 \tabularnewline
145 &  0.5393 &  0.9214 &  0.4607 \tabularnewline
146 &  0.5023 &  0.9954 &  0.4977 \tabularnewline
147 &  0.5232 &  0.9536 &  0.4768 \tabularnewline
148 &  0.4686 &  0.9373 &  0.5314 \tabularnewline
149 &  0.465 &  0.9301 &  0.535 \tabularnewline
150 &  0.5536 &  0.8929 &  0.4464 \tabularnewline
151 &  0.4869 &  0.9738 &  0.5131 \tabularnewline
152 &  0.4985 &  0.997 &  0.5015 \tabularnewline
153 &  0.4284 &  0.8567 &  0.5716 \tabularnewline
154 &  0.5979 &  0.8043 &  0.4021 \tabularnewline
155 &  0.5463 &  0.9073 &  0.4537 \tabularnewline
156 &  0.488 &  0.976 &  0.512 \tabularnewline
157 &  0.4123 &  0.8246 &  0.5877 \tabularnewline
158 &  0.3894 &  0.7789 &  0.6106 \tabularnewline
159 &  0.3286 &  0.6572 &  0.6714 \tabularnewline
160 &  0.2822 &  0.5644 &  0.7178 \tabularnewline
161 &  0.3539 &  0.7078 &  0.6461 \tabularnewline
162 &  0.4083 &  0.8167 &  0.5917 \tabularnewline
163 &  0.4903 &  0.9807 &  0.5097 \tabularnewline
164 &  0.5346 &  0.9307 &  0.4654 \tabularnewline
165 &  0.4199 &  0.8398 &  0.5801 \tabularnewline
166 &  0.9452 &  0.1097 &  0.05484 \tabularnewline
167 &  0.9247 &  0.1507 &  0.07535 \tabularnewline
168 &  0.8286 &  0.3427 &  0.1714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&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] 0.7583[/C][C] 0.4835[/C][C] 0.2417[/C][/ROW]
[ROW][C]12[/C][C] 0.8911[/C][C] 0.2178[/C][C] 0.1089[/C][/ROW]
[ROW][C]13[/C][C] 0.8352[/C][C] 0.3296[/C][C] 0.1648[/C][/ROW]
[ROW][C]14[/C][C] 0.9764[/C][C] 0.04726[/C][C] 0.02363[/C][/ROW]
[ROW][C]15[/C][C] 0.9825[/C][C] 0.03509[/C][C] 0.01754[/C][/ROW]
[ROW][C]16[/C][C] 0.9777[/C][C] 0.04462[/C][C] 0.02231[/C][/ROW]
[ROW][C]17[/C][C] 0.9675[/C][C] 0.06494[/C][C] 0.03247[/C][/ROW]
[ROW][C]18[/C][C] 0.96[/C][C] 0.08007[/C][C] 0.04004[/C][/ROW]
[ROW][C]19[/C][C] 0.9421[/C][C] 0.1157[/C][C] 0.05787[/C][/ROW]
[ROW][C]20[/C][C] 0.94[/C][C] 0.12[/C][C] 0.06002[/C][/ROW]
[ROW][C]21[/C][C] 0.9557[/C][C] 0.08866[/C][C] 0.04433[/C][/ROW]
[ROW][C]22[/C][C] 0.9483[/C][C] 0.1033[/C][C] 0.05166[/C][/ROW]
[ROW][C]23[/C][C] 0.9328[/C][C] 0.1345[/C][C] 0.06725[/C][/ROW]
[ROW][C]24[/C][C] 0.9093[/C][C] 0.1813[/C][C] 0.09066[/C][/ROW]
[ROW][C]25[/C][C] 0.9142[/C][C] 0.1716[/C][C] 0.08581[/C][/ROW]
[ROW][C]26[/C][C] 0.972[/C][C] 0.05599[/C][C] 0.028[/C][/ROW]
[ROW][C]27[/C][C] 0.9629[/C][C] 0.07428[/C][C] 0.03714[/C][/ROW]
[ROW][C]28[/C][C] 0.9607[/C][C] 0.07859[/C][C] 0.03929[/C][/ROW]
[ROW][C]29[/C][C] 0.9465[/C][C] 0.107[/C][C] 0.05349[/C][/ROW]
[ROW][C]30[/C][C] 0.9282[/C][C] 0.1436[/C][C] 0.07182[/C][/ROW]
[ROW][C]31[/C][C] 0.9126[/C][C] 0.1749[/C][C] 0.08744[/C][/ROW]
[ROW][C]32[/C][C] 0.89[/C][C] 0.2199[/C][C] 0.11[/C][/ROW]
[ROW][C]33[/C][C] 0.8623[/C][C] 0.2755[/C][C] 0.1377[/C][/ROW]
[ROW][C]34[/C][C] 0.8742[/C][C] 0.2516[/C][C] 0.1258[/C][/ROW]
[ROW][C]35[/C][C] 0.8509[/C][C] 0.2981[/C][C] 0.1491[/C][/ROW]
[ROW][C]36[/C][C] 0.8195[/C][C] 0.3611[/C][C] 0.1805[/C][/ROW]
[ROW][C]37[/C][C] 0.7874[/C][C] 0.4252[/C][C] 0.2126[/C][/ROW]
[ROW][C]38[/C][C] 0.7858[/C][C] 0.4284[/C][C] 0.2142[/C][/ROW]
[ROW][C]39[/C][C] 0.7473[/C][C] 0.5055[/C][C] 0.2527[/C][/ROW]
[ROW][C]40[/C][C] 0.7015[/C][C] 0.597[/C][C] 0.2985[/C][/ROW]
[ROW][C]41[/C][C] 0.7472[/C][C] 0.5056[/C][C] 0.2528[/C][/ROW]
[ROW][C]42[/C][C] 0.7594[/C][C] 0.4813[/C][C] 0.2406[/C][/ROW]
[ROW][C]43[/C][C] 0.7169[/C][C] 0.5662[/C][C] 0.2831[/C][/ROW]
[ROW][C]44[/C][C] 0.6709[/C][C] 0.6583[/C][C] 0.3291[/C][/ROW]
[ROW][C]45[/C][C] 0.6225[/C][C] 0.7549[/C][C] 0.3775[/C][/ROW]
[ROW][C]46[/C][C] 0.5873[/C][C] 0.8253[/C][C] 0.4127[/C][/ROW]
[ROW][C]47[/C][C] 0.5873[/C][C] 0.8253[/C][C] 0.4127[/C][/ROW]
[ROW][C]48[/C][C] 0.5399[/C][C] 0.9203[/C][C] 0.4601[/C][/ROW]
[ROW][C]49[/C][C] 0.6316[/C][C] 0.7367[/C][C] 0.3684[/C][/ROW]
[ROW][C]50[/C][C] 0.6257[/C][C] 0.7487[/C][C] 0.3743[/C][/ROW]
[ROW][C]51[/C][C] 0.5942[/C][C] 0.8117[/C][C] 0.4058[/C][/ROW]
[ROW][C]52[/C][C] 0.5535[/C][C] 0.8931[/C][C] 0.4465[/C][/ROW]
[ROW][C]53[/C][C] 0.5196[/C][C] 0.9609[/C][C] 0.4804[/C][/ROW]
[ROW][C]54[/C][C] 0.4798[/C][C] 0.9596[/C][C] 0.5202[/C][/ROW]
[ROW][C]55[/C][C] 0.4889[/C][C] 0.9779[/C][C] 0.5111[/C][/ROW]
[ROW][C]56[/C][C] 0.4502[/C][C] 0.9003[/C][C] 0.5498[/C][/ROW]
[ROW][C]57[/C][C] 0.441[/C][C] 0.882[/C][C] 0.559[/C][/ROW]
[ROW][C]58[/C][C] 0.4306[/C][C] 0.8612[/C][C] 0.5694[/C][/ROW]
[ROW][C]59[/C][C] 0.3865[/C][C] 0.7729[/C][C] 0.6135[/C][/ROW]
[ROW][C]60[/C][C] 0.3423[/C][C] 0.6845[/C][C] 0.6577[/C][/ROW]
[ROW][C]61[/C][C] 0.3182[/C][C] 0.6363[/C][C] 0.6818[/C][/ROW]
[ROW][C]62[/C][C] 0.2772[/C][C] 0.5543[/C][C] 0.7228[/C][/ROW]
[ROW][C]63[/C][C] 0.2433[/C][C] 0.4866[/C][C] 0.7567[/C][/ROW]
[ROW][C]64[/C][C] 0.2078[/C][C] 0.4156[/C][C] 0.7922[/C][/ROW]
[ROW][C]65[/C][C] 0.2744[/C][C] 0.5488[/C][C] 0.7256[/C][/ROW]
[ROW][C]66[/C][C] 0.3091[/C][C] 0.6182[/C][C] 0.6909[/C][/ROW]
[ROW][C]67[/C][C] 0.3918[/C][C] 0.7836[/C][C] 0.6082[/C][/ROW]
[ROW][C]68[/C][C] 0.359[/C][C] 0.7181[/C][C] 0.641[/C][/ROW]
[ROW][C]69[/C][C] 0.3215[/C][C] 0.6431[/C][C] 0.6785[/C][/ROW]
[ROW][C]70[/C][C] 0.3072[/C][C] 0.6145[/C][C] 0.6928[/C][/ROW]
[ROW][C]71[/C][C] 0.2898[/C][C] 0.5796[/C][C] 0.7102[/C][/ROW]
[ROW][C]72[/C][C] 0.2713[/C][C] 0.5425[/C][C] 0.7287[/C][/ROW]
[ROW][C]73[/C][C] 0.2354[/C][C] 0.4709[/C][C] 0.7646[/C][/ROW]
[ROW][C]74[/C][C] 0.2426[/C][C] 0.4852[/C][C] 0.7574[/C][/ROW]
[ROW][C]75[/C][C] 0.2276[/C][C] 0.4552[/C][C] 0.7724[/C][/ROW]
[ROW][C]76[/C][C] 0.23[/C][C] 0.4599[/C][C] 0.77[/C][/ROW]
[ROW][C]77[/C][C] 0.2223[/C][C] 0.4447[/C][C] 0.7777[/C][/ROW]
[ROW][C]78[/C][C] 0.2193[/C][C] 0.4386[/C][C] 0.7807[/C][/ROW]
[ROW][C]79[/C][C] 0.1923[/C][C] 0.3845[/C][C] 0.8077[/C][/ROW]
[ROW][C]80[/C][C] 0.1715[/C][C] 0.343[/C][C] 0.8285[/C][/ROW]
[ROW][C]81[/C][C] 0.1453[/C][C] 0.2907[/C][C] 0.8547[/C][/ROW]
[ROW][C]82[/C][C] 0.1373[/C][C] 0.2747[/C][C] 0.8627[/C][/ROW]
[ROW][C]83[/C][C] 0.125[/C][C] 0.25[/C][C] 0.875[/C][/ROW]
[ROW][C]84[/C][C] 0.1544[/C][C] 0.3087[/C][C] 0.8456[/C][/ROW]
[ROW][C]85[/C][C] 0.1295[/C][C] 0.2591[/C][C] 0.8705[/C][/ROW]
[ROW][C]86[/C][C] 0.1219[/C][C] 0.2438[/C][C] 0.8781[/C][/ROW]
[ROW][C]87[/C][C] 0.1257[/C][C] 0.2514[/C][C] 0.8743[/C][/ROW]
[ROW][C]88[/C][C] 0.1086[/C][C] 0.2171[/C][C] 0.8914[/C][/ROW]
[ROW][C]89[/C][C] 0.09603[/C][C] 0.1921[/C][C] 0.904[/C][/ROW]
[ROW][C]90[/C][C] 0.08163[/C][C] 0.1633[/C][C] 0.9184[/C][/ROW]
[ROW][C]91[/C][C] 0.3212[/C][C] 0.6424[/C][C] 0.6788[/C][/ROW]
[ROW][C]92[/C][C] 0.285[/C][C] 0.5701[/C][C] 0.715[/C][/ROW]
[ROW][C]93[/C][C] 0.2675[/C][C] 0.5351[/C][C] 0.7325[/C][/ROW]
[ROW][C]94[/C][C] 0.2403[/C][C] 0.4807[/C][C] 0.7597[/C][/ROW]
[ROW][C]95[/C][C] 0.2088[/C][C] 0.4176[/C][C] 0.7912[/C][/ROW]
[ROW][C]96[/C][C] 0.208[/C][C] 0.4159[/C][C] 0.792[/C][/ROW]
[ROW][C]97[/C][C] 0.1872[/C][C] 0.3743[/C][C] 0.8128[/C][/ROW]
[ROW][C]98[/C][C] 0.1698[/C][C] 0.3396[/C][C] 0.8302[/C][/ROW]
[ROW][C]99[/C][C] 0.1668[/C][C] 0.3336[/C][C] 0.8332[/C][/ROW]
[ROW][C]100[/C][C] 0.1467[/C][C] 0.2934[/C][C] 0.8533[/C][/ROW]
[ROW][C]101[/C][C] 0.3209[/C][C] 0.6418[/C][C] 0.6791[/C][/ROW]
[ROW][C]102[/C][C] 0.2879[/C][C] 0.5759[/C][C] 0.7121[/C][/ROW]
[ROW][C]103[/C][C] 0.2511[/C][C] 0.5022[/C][C] 0.7489[/C][/ROW]
[ROW][C]104[/C][C] 0.2259[/C][C] 0.4519[/C][C] 0.7741[/C][/ROW]
[ROW][C]105[/C][C] 0.2146[/C][C] 0.4292[/C][C] 0.7854[/C][/ROW]
[ROW][C]106[/C][C] 0.2425[/C][C] 0.485[/C][C] 0.7575[/C][/ROW]
[ROW][C]107[/C][C] 0.2926[/C][C] 0.5851[/C][C] 0.7074[/C][/ROW]
[ROW][C]108[/C][C] 0.2794[/C][C] 0.5588[/C][C] 0.7206[/C][/ROW]
[ROW][C]109[/C][C] 0.29[/C][C] 0.58[/C][C] 0.71[/C][/ROW]
[ROW][C]110[/C][C] 0.257[/C][C] 0.5139[/C][C] 0.743[/C][/ROW]
[ROW][C]111[/C][C] 0.636[/C][C] 0.728[/C][C] 0.364[/C][/ROW]
[ROW][C]112[/C][C] 0.6422[/C][C] 0.7156[/C][C] 0.3578[/C][/ROW]
[ROW][C]113[/C][C] 0.6657[/C][C] 0.6685[/C][C] 0.3343[/C][/ROW]
[ROW][C]114[/C][C] 0.6259[/C][C] 0.7482[/C][C] 0.3741[/C][/ROW]
[ROW][C]115[/C][C] 0.5982[/C][C] 0.8036[/C][C] 0.4018[/C][/ROW]
[ROW][C]116[/C][C] 0.585[/C][C] 0.83[/C][C] 0.415[/C][/ROW]
[ROW][C]117[/C][C] 0.5434[/C][C] 0.9132[/C][C] 0.4566[/C][/ROW]
[ROW][C]118[/C][C] 0.5072[/C][C] 0.9856[/C][C] 0.4928[/C][/ROW]
[ROW][C]119[/C][C] 0.6489[/C][C] 0.7022[/C][C] 0.3511[/C][/ROW]
[ROW][C]120[/C][C] 0.6509[/C][C] 0.6982[/C][C] 0.3491[/C][/ROW]
[ROW][C]121[/C][C] 0.6101[/C][C] 0.7798[/C][C] 0.3899[/C][/ROW]
[ROW][C]122[/C][C] 0.5665[/C][C] 0.867[/C][C] 0.4335[/C][/ROW]
[ROW][C]123[/C][C] 0.5797[/C][C] 0.8405[/C][C] 0.4203[/C][/ROW]
[ROW][C]124[/C][C] 0.5379[/C][C] 0.9243[/C][C] 0.4621[/C][/ROW]
[ROW][C]125[/C][C] 0.4965[/C][C] 0.9929[/C][C] 0.5035[/C][/ROW]
[ROW][C]126[/C][C] 0.4908[/C][C] 0.9817[/C][C] 0.5092[/C][/ROW]
[ROW][C]127[/C][C] 0.4759[/C][C] 0.9518[/C][C] 0.5241[/C][/ROW]
[ROW][C]128[/C][C] 0.4425[/C][C] 0.8851[/C][C] 0.5575[/C][/ROW]
[ROW][C]129[/C][C] 0.5287[/C][C] 0.9426[/C][C] 0.4713[/C][/ROW]
[ROW][C]130[/C][C] 0.5406[/C][C] 0.9188[/C][C] 0.4594[/C][/ROW]
[ROW][C]131[/C][C] 0.5028[/C][C] 0.9944[/C][C] 0.4972[/C][/ROW]
[ROW][C]132[/C][C] 0.451[/C][C] 0.9021[/C][C] 0.549[/C][/ROW]
[ROW][C]133[/C][C] 0.5219[/C][C] 0.9561[/C][C] 0.4781[/C][/ROW]
[ROW][C]134[/C][C] 0.4817[/C][C] 0.9634[/C][C] 0.5183[/C][/ROW]
[ROW][C]135[/C][C] 0.4576[/C][C] 0.9152[/C][C] 0.5424[/C][/ROW]
[ROW][C]136[/C][C] 0.5436[/C][C] 0.9127[/C][C] 0.4564[/C][/ROW]
[ROW][C]137[/C][C] 0.497[/C][C] 0.9941[/C][C] 0.503[/C][/ROW]
[ROW][C]138[/C][C] 0.4923[/C][C] 0.9846[/C][C] 0.5077[/C][/ROW]
[ROW][C]139[/C][C] 0.5117[/C][C] 0.9766[/C][C] 0.4883[/C][/ROW]
[ROW][C]140[/C][C] 0.6708[/C][C] 0.6584[/C][C] 0.3292[/C][/ROW]
[ROW][C]141[/C][C] 0.6702[/C][C] 0.6596[/C][C] 0.3298[/C][/ROW]
[ROW][C]142[/C][C] 0.6179[/C][C] 0.7642[/C][C] 0.3821[/C][/ROW]
[ROW][C]143[/C][C] 0.6396[/C][C] 0.7207[/C][C] 0.3604[/C][/ROW]
[ROW][C]144[/C][C] 0.596[/C][C] 0.808[/C][C] 0.404[/C][/ROW]
[ROW][C]145[/C][C] 0.5393[/C][C] 0.9214[/C][C] 0.4607[/C][/ROW]
[ROW][C]146[/C][C] 0.5023[/C][C] 0.9954[/C][C] 0.4977[/C][/ROW]
[ROW][C]147[/C][C] 0.5232[/C][C] 0.9536[/C][C] 0.4768[/C][/ROW]
[ROW][C]148[/C][C] 0.4686[/C][C] 0.9373[/C][C] 0.5314[/C][/ROW]
[ROW][C]149[/C][C] 0.465[/C][C] 0.9301[/C][C] 0.535[/C][/ROW]
[ROW][C]150[/C][C] 0.5536[/C][C] 0.8929[/C][C] 0.4464[/C][/ROW]
[ROW][C]151[/C][C] 0.4869[/C][C] 0.9738[/C][C] 0.5131[/C][/ROW]
[ROW][C]152[/C][C] 0.4985[/C][C] 0.997[/C][C] 0.5015[/C][/ROW]
[ROW][C]153[/C][C] 0.4284[/C][C] 0.8567[/C][C] 0.5716[/C][/ROW]
[ROW][C]154[/C][C] 0.5979[/C][C] 0.8043[/C][C] 0.4021[/C][/ROW]
[ROW][C]155[/C][C] 0.5463[/C][C] 0.9073[/C][C] 0.4537[/C][/ROW]
[ROW][C]156[/C][C] 0.488[/C][C] 0.976[/C][C] 0.512[/C][/ROW]
[ROW][C]157[/C][C] 0.4123[/C][C] 0.8246[/C][C] 0.5877[/C][/ROW]
[ROW][C]158[/C][C] 0.3894[/C][C] 0.7789[/C][C] 0.6106[/C][/ROW]
[ROW][C]159[/C][C] 0.3286[/C][C] 0.6572[/C][C] 0.6714[/C][/ROW]
[ROW][C]160[/C][C] 0.2822[/C][C] 0.5644[/C][C] 0.7178[/C][/ROW]
[ROW][C]161[/C][C] 0.3539[/C][C] 0.7078[/C][C] 0.6461[/C][/ROW]
[ROW][C]162[/C][C] 0.4083[/C][C] 0.8167[/C][C] 0.5917[/C][/ROW]
[ROW][C]163[/C][C] 0.4903[/C][C] 0.9807[/C][C] 0.5097[/C][/ROW]
[ROW][C]164[/C][C] 0.5346[/C][C] 0.9307[/C][C] 0.4654[/C][/ROW]
[ROW][C]165[/C][C] 0.4199[/C][C] 0.8398[/C][C] 0.5801[/C][/ROW]
[ROW][C]166[/C][C] 0.9452[/C][C] 0.1097[/C][C] 0.05484[/C][/ROW]
[ROW][C]167[/C][C] 0.9247[/C][C] 0.1507[/C][C] 0.07535[/C][/ROW]
[ROW][C]168[/C][C] 0.8286[/C][C] 0.3427[/C][C] 0.1714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.7583 0.4835 0.2417
12 0.8911 0.2178 0.1089
13 0.8352 0.3296 0.1648
14 0.9764 0.04726 0.02363
15 0.9825 0.03509 0.01754
16 0.9777 0.04462 0.02231
17 0.9675 0.06494 0.03247
18 0.96 0.08007 0.04004
19 0.9421 0.1157 0.05787
20 0.94 0.12 0.06002
21 0.9557 0.08866 0.04433
22 0.9483 0.1033 0.05166
23 0.9328 0.1345 0.06725
24 0.9093 0.1813 0.09066
25 0.9142 0.1716 0.08581
26 0.972 0.05599 0.028
27 0.9629 0.07428 0.03714
28 0.9607 0.07859 0.03929
29 0.9465 0.107 0.05349
30 0.9282 0.1436 0.07182
31 0.9126 0.1749 0.08744
32 0.89 0.2199 0.11
33 0.8623 0.2755 0.1377
34 0.8742 0.2516 0.1258
35 0.8509 0.2981 0.1491
36 0.8195 0.3611 0.1805
37 0.7874 0.4252 0.2126
38 0.7858 0.4284 0.2142
39 0.7473 0.5055 0.2527
40 0.7015 0.597 0.2985
41 0.7472 0.5056 0.2528
42 0.7594 0.4813 0.2406
43 0.7169 0.5662 0.2831
44 0.6709 0.6583 0.3291
45 0.6225 0.7549 0.3775
46 0.5873 0.8253 0.4127
47 0.5873 0.8253 0.4127
48 0.5399 0.9203 0.4601
49 0.6316 0.7367 0.3684
50 0.6257 0.7487 0.3743
51 0.5942 0.8117 0.4058
52 0.5535 0.8931 0.4465
53 0.5196 0.9609 0.4804
54 0.4798 0.9596 0.5202
55 0.4889 0.9779 0.5111
56 0.4502 0.9003 0.5498
57 0.441 0.882 0.559
58 0.4306 0.8612 0.5694
59 0.3865 0.7729 0.6135
60 0.3423 0.6845 0.6577
61 0.3182 0.6363 0.6818
62 0.2772 0.5543 0.7228
63 0.2433 0.4866 0.7567
64 0.2078 0.4156 0.7922
65 0.2744 0.5488 0.7256
66 0.3091 0.6182 0.6909
67 0.3918 0.7836 0.6082
68 0.359 0.7181 0.641
69 0.3215 0.6431 0.6785
70 0.3072 0.6145 0.6928
71 0.2898 0.5796 0.7102
72 0.2713 0.5425 0.7287
73 0.2354 0.4709 0.7646
74 0.2426 0.4852 0.7574
75 0.2276 0.4552 0.7724
76 0.23 0.4599 0.77
77 0.2223 0.4447 0.7777
78 0.2193 0.4386 0.7807
79 0.1923 0.3845 0.8077
80 0.1715 0.343 0.8285
81 0.1453 0.2907 0.8547
82 0.1373 0.2747 0.8627
83 0.125 0.25 0.875
84 0.1544 0.3087 0.8456
85 0.1295 0.2591 0.8705
86 0.1219 0.2438 0.8781
87 0.1257 0.2514 0.8743
88 0.1086 0.2171 0.8914
89 0.09603 0.1921 0.904
90 0.08163 0.1633 0.9184
91 0.3212 0.6424 0.6788
92 0.285 0.5701 0.715
93 0.2675 0.5351 0.7325
94 0.2403 0.4807 0.7597
95 0.2088 0.4176 0.7912
96 0.208 0.4159 0.792
97 0.1872 0.3743 0.8128
98 0.1698 0.3396 0.8302
99 0.1668 0.3336 0.8332
100 0.1467 0.2934 0.8533
101 0.3209 0.6418 0.6791
102 0.2879 0.5759 0.7121
103 0.2511 0.5022 0.7489
104 0.2259 0.4519 0.7741
105 0.2146 0.4292 0.7854
106 0.2425 0.485 0.7575
107 0.2926 0.5851 0.7074
108 0.2794 0.5588 0.7206
109 0.29 0.58 0.71
110 0.257 0.5139 0.743
111 0.636 0.728 0.364
112 0.6422 0.7156 0.3578
113 0.6657 0.6685 0.3343
114 0.6259 0.7482 0.3741
115 0.5982 0.8036 0.4018
116 0.585 0.83 0.415
117 0.5434 0.9132 0.4566
118 0.5072 0.9856 0.4928
119 0.6489 0.7022 0.3511
120 0.6509 0.6982 0.3491
121 0.6101 0.7798 0.3899
122 0.5665 0.867 0.4335
123 0.5797 0.8405 0.4203
124 0.5379 0.9243 0.4621
125 0.4965 0.9929 0.5035
126 0.4908 0.9817 0.5092
127 0.4759 0.9518 0.5241
128 0.4425 0.8851 0.5575
129 0.5287 0.9426 0.4713
130 0.5406 0.9188 0.4594
131 0.5028 0.9944 0.4972
132 0.451 0.9021 0.549
133 0.5219 0.9561 0.4781
134 0.4817 0.9634 0.5183
135 0.4576 0.9152 0.5424
136 0.5436 0.9127 0.4564
137 0.497 0.9941 0.503
138 0.4923 0.9846 0.5077
139 0.5117 0.9766 0.4883
140 0.6708 0.6584 0.3292
141 0.6702 0.6596 0.3298
142 0.6179 0.7642 0.3821
143 0.6396 0.7207 0.3604
144 0.596 0.808 0.404
145 0.5393 0.9214 0.4607
146 0.5023 0.9954 0.4977
147 0.5232 0.9536 0.4768
148 0.4686 0.9373 0.5314
149 0.465 0.9301 0.535
150 0.5536 0.8929 0.4464
151 0.4869 0.9738 0.5131
152 0.4985 0.997 0.5015
153 0.4284 0.8567 0.5716
154 0.5979 0.8043 0.4021
155 0.5463 0.9073 0.4537
156 0.488 0.976 0.512
157 0.4123 0.8246 0.5877
158 0.3894 0.7789 0.6106
159 0.3286 0.6572 0.6714
160 0.2822 0.5644 0.7178
161 0.3539 0.7078 0.6461
162 0.4083 0.8167 0.5917
163 0.4903 0.9807 0.5097
164 0.5346 0.9307 0.4654
165 0.4199 0.8398 0.5801
166 0.9452 0.1097 0.05484
167 0.9247 0.1507 0.07535
168 0.8286 0.3427 0.1714







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level30.0189873OK
10% type I error level90.056962OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 3 & 0.0189873 & OK \tabularnewline
10% type I error level & 9 & 0.056962 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319050&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0189873[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.056962[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319050&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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 testsOK/NOK
1% type I error level0 0OK
5% type I error level30.0189873OK
10% type I error level90.056962OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 6.1098, df1 = 2, df2 = 169, p-value = 0.002742
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.75864, df1 = 14, df2 = 157, p-value = 0.7122
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3565, df1 = 2, df2 = 169, p-value = 0.01429

\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 = 6.1098, df1 = 2, df2 = 169, p-value = 0.002742
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.75864, df1 = 14, df2 = 157, p-value = 0.7122
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3565, df1 = 2, df2 = 169, p-value = 0.01429
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319050&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 = 6.1098, df1 = 2, df2 = 169, p-value = 0.002742
[/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 = 0.75864, df1 = 14, df2 = 157, p-value = 0.7122
[/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 = 4.3565, df1 = 2, df2 = 169, p-value = 0.01429
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319050&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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 = 6.1098, df1 = 2, df2 = 169, p-value = 0.002742
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.75864, df1 = 14, df2 = 157, p-value = 0.7122
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3565, df1 = 2, df2 = 169, p-value = 0.01429







Variance Inflation Factors (Multicollinearity)
> vif
   Relative_Advantage  Perceived_Usefulness Perceived_Ease_of_Use 
             1.601567              1.864367              2.408801 
  Information_Quality        System_Quality                groupB 
             2.725076              1.794514              1.251689 
              genderB 
             1.081904 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   Relative_Advantage  Perceived_Usefulness Perceived_Ease_of_Use 
             1.601567              1.864367              2.408801 
  Information_Quality        System_Quality                groupB 
             2.725076              1.794514              1.251689 
              genderB 
             1.081904 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319050&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
   Relative_Advantage  Perceived_Usefulness Perceived_Ease_of_Use 
             1.601567              1.864367              2.408801 
  Information_Quality        System_Quality                groupB 
             2.725076              1.794514              1.251689 
              genderB 
             1.081904 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319050&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319050&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
   Relative_Advantage  Perceived_Usefulness Perceived_Ease_of_Use 
             1.601567              1.864367              2.408801 
  Information_Quality        System_Quality                groupB 
             2.725076              1.794514              1.251689 
              genderB 
             1.081904 



Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = 7-point Likert ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
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')