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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 computationWed, 18 Nov 2020 16:23:57 +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/Nov/18/t1605713064bfilweh2hrnjtkj.htm/, Retrieved Wed, 21 Apr 2021 08:11:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319294, Retrieved Wed, 21 Apr 2021 08:11:52 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMultiple regression, curry
Estimated Impact24
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regressi...] [2020-11-18 15:23:57] [cf5c3d94c26454c6c14f113bfcafa766] [Current]
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Dataseries X:
4 1 1 1
5 1 1 1
3 1 1 1
4 1 1 1
5 1 1 1
3 1 1 1
7 1 1 1
5 1 1 1
6 1 1 1
3 1 1 1
2 1 1 1
4 1 1 1
5 1 1 1
2 1 1 1
3 1 1 1
6 1 1 1
4 1 1 1
4 1 1 1
6 1 1 1
2 1 1 1
3 1 0 0
5 1 0 0
4 1 0 0
2 1 0 0
7 1 0 0
1 1 0 0
4 1 0 0
4 1 0 0
7 1 0 0
4 1 0 0
3 1 0 0
3 1 0 0
3 1 0 0
3 1 0 0
2 1 0 0
5 1 0 0
5 1 0 0
3 1 0 0
6 1 0 0
2 1 0 0
8 0 1 0
9 0 1 0
10 0 1 0
7 0 1 0
8 0 1 0
9 0 1 0
10 0 1 0
6 0 1 0
6 0 1 0
7 0 1 0
8 0 1 0
9 0 1 0
8 0 1 0
7 0 1 0
5 0 1 0
11 0 1 0
7 0 1 0
8 0 1 0
10 0 1 0
9 0 1 0
3 0 0 0
5 0 0 0
4 0 0 0
2 0 0 0
6 0 0 0
1 0 0 0
4 0 0 0
4 0 0 0
5 0 0 0
4 0 0 0
3 0 0 0
3 0 0 0
4 0 0 0
3 0 0 0
2 0 0 0
5 0 0 0
4 0 0 0
3 0 0 0
6 0 0 0
2 0 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=319294&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=319294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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
Rate[t] = + 3.65 + 0.15StatusDummy[t] + 4.45CurryDummy[t] -4.1Interaction[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rate[t] =  +  3.65 +  0.15StatusDummy[t] +  4.45CurryDummy[t] -4.1Interaction[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rate[t] =  +  3.65 +  0.15StatusDummy[t] +  4.45CurryDummy[t] -4.1Interaction[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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
Rate[t] = + 3.65 + 0.15StatusDummy[t] + 4.45CurryDummy[t] -4.1Interaction[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.65 0.3365+1.0850e+01 4.244e-17 2.122e-17
StatusDummy+0.15 0.4759+3.1520e-01 0.7535 0.3767
CurryDummy+4.45 0.4759+9.3510e+00 2.842e-14 1.421e-14
Interaction-4.1 0.673-6.0920e+00 4.248e-08 2.124e-08

\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) & +3.65 &  0.3365 & +1.0850e+01 &  4.244e-17 &  2.122e-17 \tabularnewline
StatusDummy & +0.15 &  0.4759 & +3.1520e-01 &  0.7535 &  0.3767 \tabularnewline
CurryDummy & +4.45 &  0.4759 & +9.3510e+00 &  2.842e-14 &  1.421e-14 \tabularnewline
Interaction & -4.1 &  0.673 & -6.0920e+00 &  4.248e-08 &  2.124e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&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]+3.65[/C][C] 0.3365[/C][C]+1.0850e+01[/C][C] 4.244e-17[/C][C] 2.122e-17[/C][/ROW]
[ROW][C]StatusDummy[/C][C]+0.15[/C][C] 0.4759[/C][C]+3.1520e-01[/C][C] 0.7535[/C][C] 0.3767[/C][/ROW]
[ROW][C]CurryDummy[/C][C]+4.45[/C][C] 0.4759[/C][C]+9.3510e+00[/C][C] 2.842e-14[/C][C] 1.421e-14[/C][/ROW]
[ROW][C]Interaction[/C][C]-4.1[/C][C] 0.673[/C][C]-6.0920e+00[/C][C] 4.248e-08[/C][C] 2.124e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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)+3.65 0.3365+1.0850e+01 4.244e-17 2.122e-17
StatusDummy+0.15 0.4759+3.1520e-01 0.7535 0.3767
CurryDummy+4.45 0.4759+9.3510e+00 2.842e-14 1.421e-14
Interaction-4.1 0.673-6.0920e+00 4.248e-08 2.124e-08







Multiple Linear Regression - Regression Statistics
Multiple R 0.7823
R-squared 0.612
Adjusted R-squared 0.5967
F-TEST (value) 39.96
F-TEST (DF numerator)3
F-TEST (DF denominator)76
p-value 1.332e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.505
Sum Squared Residuals 172.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7823 \tabularnewline
R-squared &  0.612 \tabularnewline
Adjusted R-squared &  0.5967 \tabularnewline
F-TEST (value) &  39.96 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value &  1.332e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.505 \tabularnewline
Sum Squared Residuals &  172.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7823[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.612[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5967[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 39.96[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/C][/ROW]
[ROW][C]p-value[/C][C] 1.332e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.505[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 172.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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 0.7823
R-squared 0.612
Adjusted R-squared 0.5967
F-TEST (value) 39.96
F-TEST (DF numerator)3
F-TEST (DF denominator)76
p-value 1.332e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.505
Sum Squared Residuals 172.1







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=319294&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=319294&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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 4 4.15-0.15
2 5 4.15 0.85
3 3 4.15-1.15
4 4 4.15-0.15
5 5 4.15 0.85
6 3 4.15-1.15
7 7 4.15 2.85
8 5 4.15 0.85
9 6 4.15 1.85
10 3 4.15-1.15
11 2 4.15-2.15
12 4 4.15-0.15
13 5 4.15 0.85
14 2 4.15-2.15
15 3 4.15-1.15
16 6 4.15 1.85
17 4 4.15-0.15
18 4 4.15-0.15
19 6 4.15 1.85
20 2 4.15-2.15
21 3 3.8-0.8
22 5 3.8 1.2
23 4 3.8 0.2
24 2 3.8-1.8
25 7 3.8 3.2
26 1 3.8-2.8
27 4 3.8 0.2
28 4 3.8 0.2
29 7 3.8 3.2
30 4 3.8 0.2
31 3 3.8-0.8
32 3 3.8-0.8
33 3 3.8-0.8
34 3 3.8-0.8
35 2 3.8-1.8
36 5 3.8 1.2
37 5 3.8 1.2
38 3 3.8-0.8
39 6 3.8 2.2
40 2 3.8-1.8
41 8 8.1-0.1
42 9 8.1 0.9
43 10 8.1 1.9
44 7 8.1-1.1
45 8 8.1-0.1
46 9 8.1 0.9
47 10 8.1 1.9
48 6 8.1-2.1
49 6 8.1-2.1
50 7 8.1-1.1
51 8 8.1-0.1
52 9 8.1 0.9
53 8 8.1-0.1
54 7 8.1-1.1
55 5 8.1-3.1
56 11 8.1 2.9
57 7 8.1-1.1
58 8 8.1-0.1
59 10 8.1 1.9
60 9 8.1 0.9
61 3 3.65-0.65
62 5 3.65 1.35
63 4 3.65 0.35
64 2 3.65-1.65
65 6 3.65 2.35
66 1 3.65-2.65
67 4 3.65 0.35
68 4 3.65 0.35
69 5 3.65 1.35
70 4 3.65 0.35
71 3 3.65-0.65
72 3 3.65-0.65
73 4 3.65 0.35
74 3 3.65-0.65
75 2 3.65-1.65
76 5 3.65 1.35
77 4 3.65 0.35
78 3 3.65-0.65
79 6 3.65 2.35
80 2 3.65-1.65

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  4.15 & -0.15 \tabularnewline
2 &  5 &  4.15 &  0.85 \tabularnewline
3 &  3 &  4.15 & -1.15 \tabularnewline
4 &  4 &  4.15 & -0.15 \tabularnewline
5 &  5 &  4.15 &  0.85 \tabularnewline
6 &  3 &  4.15 & -1.15 \tabularnewline
7 &  7 &  4.15 &  2.85 \tabularnewline
8 &  5 &  4.15 &  0.85 \tabularnewline
9 &  6 &  4.15 &  1.85 \tabularnewline
10 &  3 &  4.15 & -1.15 \tabularnewline
11 &  2 &  4.15 & -2.15 \tabularnewline
12 &  4 &  4.15 & -0.15 \tabularnewline
13 &  5 &  4.15 &  0.85 \tabularnewline
14 &  2 &  4.15 & -2.15 \tabularnewline
15 &  3 &  4.15 & -1.15 \tabularnewline
16 &  6 &  4.15 &  1.85 \tabularnewline
17 &  4 &  4.15 & -0.15 \tabularnewline
18 &  4 &  4.15 & -0.15 \tabularnewline
19 &  6 &  4.15 &  1.85 \tabularnewline
20 &  2 &  4.15 & -2.15 \tabularnewline
21 &  3 &  3.8 & -0.8 \tabularnewline
22 &  5 &  3.8 &  1.2 \tabularnewline
23 &  4 &  3.8 &  0.2 \tabularnewline
24 &  2 &  3.8 & -1.8 \tabularnewline
25 &  7 &  3.8 &  3.2 \tabularnewline
26 &  1 &  3.8 & -2.8 \tabularnewline
27 &  4 &  3.8 &  0.2 \tabularnewline
28 &  4 &  3.8 &  0.2 \tabularnewline
29 &  7 &  3.8 &  3.2 \tabularnewline
30 &  4 &  3.8 &  0.2 \tabularnewline
31 &  3 &  3.8 & -0.8 \tabularnewline
32 &  3 &  3.8 & -0.8 \tabularnewline
33 &  3 &  3.8 & -0.8 \tabularnewline
34 &  3 &  3.8 & -0.8 \tabularnewline
35 &  2 &  3.8 & -1.8 \tabularnewline
36 &  5 &  3.8 &  1.2 \tabularnewline
37 &  5 &  3.8 &  1.2 \tabularnewline
38 &  3 &  3.8 & -0.8 \tabularnewline
39 &  6 &  3.8 &  2.2 \tabularnewline
40 &  2 &  3.8 & -1.8 \tabularnewline
41 &  8 &  8.1 & -0.1 \tabularnewline
42 &  9 &  8.1 &  0.9 \tabularnewline
43 &  10 &  8.1 &  1.9 \tabularnewline
44 &  7 &  8.1 & -1.1 \tabularnewline
45 &  8 &  8.1 & -0.1 \tabularnewline
46 &  9 &  8.1 &  0.9 \tabularnewline
47 &  10 &  8.1 &  1.9 \tabularnewline
48 &  6 &  8.1 & -2.1 \tabularnewline
49 &  6 &  8.1 & -2.1 \tabularnewline
50 &  7 &  8.1 & -1.1 \tabularnewline
51 &  8 &  8.1 & -0.1 \tabularnewline
52 &  9 &  8.1 &  0.9 \tabularnewline
53 &  8 &  8.1 & -0.1 \tabularnewline
54 &  7 &  8.1 & -1.1 \tabularnewline
55 &  5 &  8.1 & -3.1 \tabularnewline
56 &  11 &  8.1 &  2.9 \tabularnewline
57 &  7 &  8.1 & -1.1 \tabularnewline
58 &  8 &  8.1 & -0.1 \tabularnewline
59 &  10 &  8.1 &  1.9 \tabularnewline
60 &  9 &  8.1 &  0.9 \tabularnewline
61 &  3 &  3.65 & -0.65 \tabularnewline
62 &  5 &  3.65 &  1.35 \tabularnewline
63 &  4 &  3.65 &  0.35 \tabularnewline
64 &  2 &  3.65 & -1.65 \tabularnewline
65 &  6 &  3.65 &  2.35 \tabularnewline
66 &  1 &  3.65 & -2.65 \tabularnewline
67 &  4 &  3.65 &  0.35 \tabularnewline
68 &  4 &  3.65 &  0.35 \tabularnewline
69 &  5 &  3.65 &  1.35 \tabularnewline
70 &  4 &  3.65 &  0.35 \tabularnewline
71 &  3 &  3.65 & -0.65 \tabularnewline
72 &  3 &  3.65 & -0.65 \tabularnewline
73 &  4 &  3.65 &  0.35 \tabularnewline
74 &  3 &  3.65 & -0.65 \tabularnewline
75 &  2 &  3.65 & -1.65 \tabularnewline
76 &  5 &  3.65 &  1.35 \tabularnewline
77 &  4 &  3.65 &  0.35 \tabularnewline
78 &  3 &  3.65 & -0.65 \tabularnewline
79 &  6 &  3.65 &  2.35 \tabularnewline
80 &  2 &  3.65 & -1.65 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&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] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 4.15[/C][C] 0.85[/C][/ROW]
[ROW][C]3[/C][C] 3[/C][C] 4.15[/C][C]-1.15[/C][/ROW]
[ROW][C]4[/C][C] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 4.15[/C][C] 0.85[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 4.15[/C][C]-1.15[/C][/ROW]
[ROW][C]7[/C][C] 7[/C][C] 4.15[/C][C] 2.85[/C][/ROW]
[ROW][C]8[/C][C] 5[/C][C] 4.15[/C][C] 0.85[/C][/ROW]
[ROW][C]9[/C][C] 6[/C][C] 4.15[/C][C] 1.85[/C][/ROW]
[ROW][C]10[/C][C] 3[/C][C] 4.15[/C][C]-1.15[/C][/ROW]
[ROW][C]11[/C][C] 2[/C][C] 4.15[/C][C]-2.15[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]13[/C][C] 5[/C][C] 4.15[/C][C] 0.85[/C][/ROW]
[ROW][C]14[/C][C] 2[/C][C] 4.15[/C][C]-2.15[/C][/ROW]
[ROW][C]15[/C][C] 3[/C][C] 4.15[/C][C]-1.15[/C][/ROW]
[ROW][C]16[/C][C] 6[/C][C] 4.15[/C][C] 1.85[/C][/ROW]
[ROW][C]17[/C][C] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]19[/C][C] 6[/C][C] 4.15[/C][C] 1.85[/C][/ROW]
[ROW][C]20[/C][C] 2[/C][C] 4.15[/C][C]-2.15[/C][/ROW]
[ROW][C]21[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]22[/C][C] 5[/C][C] 3.8[/C][C] 1.2[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 3.8[/C][C] 0.2[/C][/ROW]
[ROW][C]24[/C][C] 2[/C][C] 3.8[/C][C]-1.8[/C][/ROW]
[ROW][C]25[/C][C] 7[/C][C] 3.8[/C][C] 3.2[/C][/ROW]
[ROW][C]26[/C][C] 1[/C][C] 3.8[/C][C]-2.8[/C][/ROW]
[ROW][C]27[/C][C] 4[/C][C] 3.8[/C][C] 0.2[/C][/ROW]
[ROW][C]28[/C][C] 4[/C][C] 3.8[/C][C] 0.2[/C][/ROW]
[ROW][C]29[/C][C] 7[/C][C] 3.8[/C][C] 3.2[/C][/ROW]
[ROW][C]30[/C][C] 4[/C][C] 3.8[/C][C] 0.2[/C][/ROW]
[ROW][C]31[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]32[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]33[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]34[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]35[/C][C] 2[/C][C] 3.8[/C][C]-1.8[/C][/ROW]
[ROW][C]36[/C][C] 5[/C][C] 3.8[/C][C] 1.2[/C][/ROW]
[ROW][C]37[/C][C] 5[/C][C] 3.8[/C][C] 1.2[/C][/ROW]
[ROW][C]38[/C][C] 3[/C][C] 3.8[/C][C]-0.8[/C][/ROW]
[ROW][C]39[/C][C] 6[/C][C] 3.8[/C][C] 2.2[/C][/ROW]
[ROW][C]40[/C][C] 2[/C][C] 3.8[/C][C]-1.8[/C][/ROW]
[ROW][C]41[/C][C] 8[/C][C] 8.1[/C][C]-0.1[/C][/ROW]
[ROW][C]42[/C][C] 9[/C][C] 8.1[/C][C] 0.9[/C][/ROW]
[ROW][C]43[/C][C] 10[/C][C] 8.1[/C][C] 1.9[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 8.1[/C][C]-1.1[/C][/ROW]
[ROW][C]45[/C][C] 8[/C][C] 8.1[/C][C]-0.1[/C][/ROW]
[ROW][C]46[/C][C] 9[/C][C] 8.1[/C][C] 0.9[/C][/ROW]
[ROW][C]47[/C][C] 10[/C][C] 8.1[/C][C] 1.9[/C][/ROW]
[ROW][C]48[/C][C] 6[/C][C] 8.1[/C][C]-2.1[/C][/ROW]
[ROW][C]49[/C][C] 6[/C][C] 8.1[/C][C]-2.1[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 8.1[/C][C]-1.1[/C][/ROW]
[ROW][C]51[/C][C] 8[/C][C] 8.1[/C][C]-0.1[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.1[/C][C] 0.9[/C][/ROW]
[ROW][C]53[/C][C] 8[/C][C] 8.1[/C][C]-0.1[/C][/ROW]
[ROW][C]54[/C][C] 7[/C][C] 8.1[/C][C]-1.1[/C][/ROW]
[ROW][C]55[/C][C] 5[/C][C] 8.1[/C][C]-3.1[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 8.1[/C][C] 2.9[/C][/ROW]
[ROW][C]57[/C][C] 7[/C][C] 8.1[/C][C]-1.1[/C][/ROW]
[ROW][C]58[/C][C] 8[/C][C] 8.1[/C][C]-0.1[/C][/ROW]
[ROW][C]59[/C][C] 10[/C][C] 8.1[/C][C] 1.9[/C][/ROW]
[ROW][C]60[/C][C] 9[/C][C] 8.1[/C][C] 0.9[/C][/ROW]
[ROW][C]61[/C][C] 3[/C][C] 3.65[/C][C]-0.65[/C][/ROW]
[ROW][C]62[/C][C] 5[/C][C] 3.65[/C][C] 1.35[/C][/ROW]
[ROW][C]63[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]64[/C][C] 2[/C][C] 3.65[/C][C]-1.65[/C][/ROW]
[ROW][C]65[/C][C] 6[/C][C] 3.65[/C][C] 2.35[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 3.65[/C][C]-2.65[/C][/ROW]
[ROW][C]67[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]69[/C][C] 5[/C][C] 3.65[/C][C] 1.35[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]71[/C][C] 3[/C][C] 3.65[/C][C]-0.65[/C][/ROW]
[ROW][C]72[/C][C] 3[/C][C] 3.65[/C][C]-0.65[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]74[/C][C] 3[/C][C] 3.65[/C][C]-0.65[/C][/ROW]
[ROW][C]75[/C][C] 2[/C][C] 3.65[/C][C]-1.65[/C][/ROW]
[ROW][C]76[/C][C] 5[/C][C] 3.65[/C][C] 1.35[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 3.65[/C][C] 0.35[/C][/ROW]
[ROW][C]78[/C][C] 3[/C][C] 3.65[/C][C]-0.65[/C][/ROW]
[ROW][C]79[/C][C] 6[/C][C] 3.65[/C][C] 2.35[/C][/ROW]
[ROW][C]80[/C][C] 2[/C][C] 3.65[/C][C]-1.65[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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 4 4.15-0.15
2 5 4.15 0.85
3 3 4.15-1.15
4 4 4.15-0.15
5 5 4.15 0.85
6 3 4.15-1.15
7 7 4.15 2.85
8 5 4.15 0.85
9 6 4.15 1.85
10 3 4.15-1.15
11 2 4.15-2.15
12 4 4.15-0.15
13 5 4.15 0.85
14 2 4.15-2.15
15 3 4.15-1.15
16 6 4.15 1.85
17 4 4.15-0.15
18 4 4.15-0.15
19 6 4.15 1.85
20 2 4.15-2.15
21 3 3.8-0.8
22 5 3.8 1.2
23 4 3.8 0.2
24 2 3.8-1.8
25 7 3.8 3.2
26 1 3.8-2.8
27 4 3.8 0.2
28 4 3.8 0.2
29 7 3.8 3.2
30 4 3.8 0.2
31 3 3.8-0.8
32 3 3.8-0.8
33 3 3.8-0.8
34 3 3.8-0.8
35 2 3.8-1.8
36 5 3.8 1.2
37 5 3.8 1.2
38 3 3.8-0.8
39 6 3.8 2.2
40 2 3.8-1.8
41 8 8.1-0.1
42 9 8.1 0.9
43 10 8.1 1.9
44 7 8.1-1.1
45 8 8.1-0.1
46 9 8.1 0.9
47 10 8.1 1.9
48 6 8.1-2.1
49 6 8.1-2.1
50 7 8.1-1.1
51 8 8.1-0.1
52 9 8.1 0.9
53 8 8.1-0.1
54 7 8.1-1.1
55 5 8.1-3.1
56 11 8.1 2.9
57 7 8.1-1.1
58 8 8.1-0.1
59 10 8.1 1.9
60 9 8.1 0.9
61 3 3.65-0.65
62 5 3.65 1.35
63 4 3.65 0.35
64 2 3.65-1.65
65 6 3.65 2.35
66 1 3.65-2.65
67 4 3.65 0.35
68 4 3.65 0.35
69 5 3.65 1.35
70 4 3.65 0.35
71 3 3.65-0.65
72 3 3.65-0.65
73 4 3.65 0.35
74 3 3.65-0.65
75 2 3.65-1.65
76 5 3.65 1.35
77 4 3.65 0.35
78 3 3.65-0.65
79 6 3.65 2.35
80 2 3.65-1.65







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.8225 0.3549 0.1775
8 0.7163 0.5673 0.2837
9 0.6907 0.6187 0.3093
10 0.6749 0.6502 0.3251
11 0.77 0.46 0.23
12 0.681 0.638 0.319
13 0.6057 0.7886 0.3943
14 0.685 0.63 0.315
15 0.641 0.718 0.359
16 0.6695 0.661 0.3305
17 0.5863 0.8273 0.4137
18 0.5009 0.9982 0.4991
19 0.5611 0.8779 0.4389
20 0.604 0.792 0.396
21 0.5301 0.9399 0.4699
22 0.5112 0.9775 0.4888
23 0.4343 0.8686 0.5657
24 0.4557 0.9115 0.5443
25 0.6778 0.6444 0.3222
26 0.8083 0.3834 0.1917
27 0.7571 0.4859 0.2429
28 0.6994 0.6013 0.3006
29 0.8532 0.2935 0.1468
30 0.8115 0.3769 0.1885
31 0.7771 0.4458 0.2229
32 0.7378 0.5244 0.2622
33 0.6946 0.6108 0.3054
34 0.6488 0.7024 0.3512
35 0.6746 0.6509 0.3254
36 0.6445 0.7111 0.3555
37 0.6174 0.7653 0.3826
38 0.5694 0.8611 0.4306
39 0.674 0.652 0.326
40 0.6536 0.6927 0.3464
41 0.5897 0.8206 0.4103
42 0.542 0.9159 0.458
43 0.5548 0.8905 0.4452
44 0.5392 0.9215 0.4608
45 0.4739 0.9479 0.5261
46 0.427 0.8539 0.573
47 0.4574 0.9148 0.5426
48 0.5204 0.9593 0.4796
49 0.5764 0.8472 0.4236
50 0.5427 0.9146 0.4573
51 0.4732 0.9463 0.5268
52 0.4247 0.8494 0.5753
53 0.3564 0.7127 0.6436
54 0.3265 0.653 0.6735
55 0.6081 0.7838 0.3919
56 0.7234 0.5532 0.2766
57 0.7313 0.5373 0.2687
58 0.6996 0.6007 0.3004
59 0.6665 0.6671 0.3335
60 0.5949 0.8101 0.4051
61 0.525 0.9499 0.475
62 0.5055 0.989 0.4945
63 0.4235 0.847 0.5765
64 0.4321 0.8641 0.5679
65 0.553 0.894 0.447
66 0.744 0.512 0.256
67 0.6567 0.6867 0.3433
68 0.5563 0.8873 0.4437
69 0.5346 0.9308 0.4654
70 0.4234 0.8467 0.5766
71 0.3149 0.6298 0.6851
72 0.2147 0.4293 0.7853
73 0.1232 0.2464 0.8768

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.8225 &  0.3549 &  0.1775 \tabularnewline
8 &  0.7163 &  0.5673 &  0.2837 \tabularnewline
9 &  0.6907 &  0.6187 &  0.3093 \tabularnewline
10 &  0.6749 &  0.6502 &  0.3251 \tabularnewline
11 &  0.77 &  0.46 &  0.23 \tabularnewline
12 &  0.681 &  0.638 &  0.319 \tabularnewline
13 &  0.6057 &  0.7886 &  0.3943 \tabularnewline
14 &  0.685 &  0.63 &  0.315 \tabularnewline
15 &  0.641 &  0.718 &  0.359 \tabularnewline
16 &  0.6695 &  0.661 &  0.3305 \tabularnewline
17 &  0.5863 &  0.8273 &  0.4137 \tabularnewline
18 &  0.5009 &  0.9982 &  0.4991 \tabularnewline
19 &  0.5611 &  0.8779 &  0.4389 \tabularnewline
20 &  0.604 &  0.792 &  0.396 \tabularnewline
21 &  0.5301 &  0.9399 &  0.4699 \tabularnewline
22 &  0.5112 &  0.9775 &  0.4888 \tabularnewline
23 &  0.4343 &  0.8686 &  0.5657 \tabularnewline
24 &  0.4557 &  0.9115 &  0.5443 \tabularnewline
25 &  0.6778 &  0.6444 &  0.3222 \tabularnewline
26 &  0.8083 &  0.3834 &  0.1917 \tabularnewline
27 &  0.7571 &  0.4859 &  0.2429 \tabularnewline
28 &  0.6994 &  0.6013 &  0.3006 \tabularnewline
29 &  0.8532 &  0.2935 &  0.1468 \tabularnewline
30 &  0.8115 &  0.3769 &  0.1885 \tabularnewline
31 &  0.7771 &  0.4458 &  0.2229 \tabularnewline
32 &  0.7378 &  0.5244 &  0.2622 \tabularnewline
33 &  0.6946 &  0.6108 &  0.3054 \tabularnewline
34 &  0.6488 &  0.7024 &  0.3512 \tabularnewline
35 &  0.6746 &  0.6509 &  0.3254 \tabularnewline
36 &  0.6445 &  0.7111 &  0.3555 \tabularnewline
37 &  0.6174 &  0.7653 &  0.3826 \tabularnewline
38 &  0.5694 &  0.8611 &  0.4306 \tabularnewline
39 &  0.674 &  0.652 &  0.326 \tabularnewline
40 &  0.6536 &  0.6927 &  0.3464 \tabularnewline
41 &  0.5897 &  0.8206 &  0.4103 \tabularnewline
42 &  0.542 &  0.9159 &  0.458 \tabularnewline
43 &  0.5548 &  0.8905 &  0.4452 \tabularnewline
44 &  0.5392 &  0.9215 &  0.4608 \tabularnewline
45 &  0.4739 &  0.9479 &  0.5261 \tabularnewline
46 &  0.427 &  0.8539 &  0.573 \tabularnewline
47 &  0.4574 &  0.9148 &  0.5426 \tabularnewline
48 &  0.5204 &  0.9593 &  0.4796 \tabularnewline
49 &  0.5764 &  0.8472 &  0.4236 \tabularnewline
50 &  0.5427 &  0.9146 &  0.4573 \tabularnewline
51 &  0.4732 &  0.9463 &  0.5268 \tabularnewline
52 &  0.4247 &  0.8494 &  0.5753 \tabularnewline
53 &  0.3564 &  0.7127 &  0.6436 \tabularnewline
54 &  0.3265 &  0.653 &  0.6735 \tabularnewline
55 &  0.6081 &  0.7838 &  0.3919 \tabularnewline
56 &  0.7234 &  0.5532 &  0.2766 \tabularnewline
57 &  0.7313 &  0.5373 &  0.2687 \tabularnewline
58 &  0.6996 &  0.6007 &  0.3004 \tabularnewline
59 &  0.6665 &  0.6671 &  0.3335 \tabularnewline
60 &  0.5949 &  0.8101 &  0.4051 \tabularnewline
61 &  0.525 &  0.9499 &  0.475 \tabularnewline
62 &  0.5055 &  0.989 &  0.4945 \tabularnewline
63 &  0.4235 &  0.847 &  0.5765 \tabularnewline
64 &  0.4321 &  0.8641 &  0.5679 \tabularnewline
65 &  0.553 &  0.894 &  0.447 \tabularnewline
66 &  0.744 &  0.512 &  0.256 \tabularnewline
67 &  0.6567 &  0.6867 &  0.3433 \tabularnewline
68 &  0.5563 &  0.8873 &  0.4437 \tabularnewline
69 &  0.5346 &  0.9308 &  0.4654 \tabularnewline
70 &  0.4234 &  0.8467 &  0.5766 \tabularnewline
71 &  0.3149 &  0.6298 &  0.6851 \tabularnewline
72 &  0.2147 &  0.4293 &  0.7853 \tabularnewline
73 &  0.1232 &  0.2464 &  0.8768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&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]7[/C][C] 0.8225[/C][C] 0.3549[/C][C] 0.1775[/C][/ROW]
[ROW][C]8[/C][C] 0.7163[/C][C] 0.5673[/C][C] 0.2837[/C][/ROW]
[ROW][C]9[/C][C] 0.6907[/C][C] 0.6187[/C][C] 0.3093[/C][/ROW]
[ROW][C]10[/C][C] 0.6749[/C][C] 0.6502[/C][C] 0.3251[/C][/ROW]
[ROW][C]11[/C][C] 0.77[/C][C] 0.46[/C][C] 0.23[/C][/ROW]
[ROW][C]12[/C][C] 0.681[/C][C] 0.638[/C][C] 0.319[/C][/ROW]
[ROW][C]13[/C][C] 0.6057[/C][C] 0.7886[/C][C] 0.3943[/C][/ROW]
[ROW][C]14[/C][C] 0.685[/C][C] 0.63[/C][C] 0.315[/C][/ROW]
[ROW][C]15[/C][C] 0.641[/C][C] 0.718[/C][C] 0.359[/C][/ROW]
[ROW][C]16[/C][C] 0.6695[/C][C] 0.661[/C][C] 0.3305[/C][/ROW]
[ROW][C]17[/C][C] 0.5863[/C][C] 0.8273[/C][C] 0.4137[/C][/ROW]
[ROW][C]18[/C][C] 0.5009[/C][C] 0.9982[/C][C] 0.4991[/C][/ROW]
[ROW][C]19[/C][C] 0.5611[/C][C] 0.8779[/C][C] 0.4389[/C][/ROW]
[ROW][C]20[/C][C] 0.604[/C][C] 0.792[/C][C] 0.396[/C][/ROW]
[ROW][C]21[/C][C] 0.5301[/C][C] 0.9399[/C][C] 0.4699[/C][/ROW]
[ROW][C]22[/C][C] 0.5112[/C][C] 0.9775[/C][C] 0.4888[/C][/ROW]
[ROW][C]23[/C][C] 0.4343[/C][C] 0.8686[/C][C] 0.5657[/C][/ROW]
[ROW][C]24[/C][C] 0.4557[/C][C] 0.9115[/C][C] 0.5443[/C][/ROW]
[ROW][C]25[/C][C] 0.6778[/C][C] 0.6444[/C][C] 0.3222[/C][/ROW]
[ROW][C]26[/C][C] 0.8083[/C][C] 0.3834[/C][C] 0.1917[/C][/ROW]
[ROW][C]27[/C][C] 0.7571[/C][C] 0.4859[/C][C] 0.2429[/C][/ROW]
[ROW][C]28[/C][C] 0.6994[/C][C] 0.6013[/C][C] 0.3006[/C][/ROW]
[ROW][C]29[/C][C] 0.8532[/C][C] 0.2935[/C][C] 0.1468[/C][/ROW]
[ROW][C]30[/C][C] 0.8115[/C][C] 0.3769[/C][C] 0.1885[/C][/ROW]
[ROW][C]31[/C][C] 0.7771[/C][C] 0.4458[/C][C] 0.2229[/C][/ROW]
[ROW][C]32[/C][C] 0.7378[/C][C] 0.5244[/C][C] 0.2622[/C][/ROW]
[ROW][C]33[/C][C] 0.6946[/C][C] 0.6108[/C][C] 0.3054[/C][/ROW]
[ROW][C]34[/C][C] 0.6488[/C][C] 0.7024[/C][C] 0.3512[/C][/ROW]
[ROW][C]35[/C][C] 0.6746[/C][C] 0.6509[/C][C] 0.3254[/C][/ROW]
[ROW][C]36[/C][C] 0.6445[/C][C] 0.7111[/C][C] 0.3555[/C][/ROW]
[ROW][C]37[/C][C] 0.6174[/C][C] 0.7653[/C][C] 0.3826[/C][/ROW]
[ROW][C]38[/C][C] 0.5694[/C][C] 0.8611[/C][C] 0.4306[/C][/ROW]
[ROW][C]39[/C][C] 0.674[/C][C] 0.652[/C][C] 0.326[/C][/ROW]
[ROW][C]40[/C][C] 0.6536[/C][C] 0.6927[/C][C] 0.3464[/C][/ROW]
[ROW][C]41[/C][C] 0.5897[/C][C] 0.8206[/C][C] 0.4103[/C][/ROW]
[ROW][C]42[/C][C] 0.542[/C][C] 0.9159[/C][C] 0.458[/C][/ROW]
[ROW][C]43[/C][C] 0.5548[/C][C] 0.8905[/C][C] 0.4452[/C][/ROW]
[ROW][C]44[/C][C] 0.5392[/C][C] 0.9215[/C][C] 0.4608[/C][/ROW]
[ROW][C]45[/C][C] 0.4739[/C][C] 0.9479[/C][C] 0.5261[/C][/ROW]
[ROW][C]46[/C][C] 0.427[/C][C] 0.8539[/C][C] 0.573[/C][/ROW]
[ROW][C]47[/C][C] 0.4574[/C][C] 0.9148[/C][C] 0.5426[/C][/ROW]
[ROW][C]48[/C][C] 0.5204[/C][C] 0.9593[/C][C] 0.4796[/C][/ROW]
[ROW][C]49[/C][C] 0.5764[/C][C] 0.8472[/C][C] 0.4236[/C][/ROW]
[ROW][C]50[/C][C] 0.5427[/C][C] 0.9146[/C][C] 0.4573[/C][/ROW]
[ROW][C]51[/C][C] 0.4732[/C][C] 0.9463[/C][C] 0.5268[/C][/ROW]
[ROW][C]52[/C][C] 0.4247[/C][C] 0.8494[/C][C] 0.5753[/C][/ROW]
[ROW][C]53[/C][C] 0.3564[/C][C] 0.7127[/C][C] 0.6436[/C][/ROW]
[ROW][C]54[/C][C] 0.3265[/C][C] 0.653[/C][C] 0.6735[/C][/ROW]
[ROW][C]55[/C][C] 0.6081[/C][C] 0.7838[/C][C] 0.3919[/C][/ROW]
[ROW][C]56[/C][C] 0.7234[/C][C] 0.5532[/C][C] 0.2766[/C][/ROW]
[ROW][C]57[/C][C] 0.7313[/C][C] 0.5373[/C][C] 0.2687[/C][/ROW]
[ROW][C]58[/C][C] 0.6996[/C][C] 0.6007[/C][C] 0.3004[/C][/ROW]
[ROW][C]59[/C][C] 0.6665[/C][C] 0.6671[/C][C] 0.3335[/C][/ROW]
[ROW][C]60[/C][C] 0.5949[/C][C] 0.8101[/C][C] 0.4051[/C][/ROW]
[ROW][C]61[/C][C] 0.525[/C][C] 0.9499[/C][C] 0.475[/C][/ROW]
[ROW][C]62[/C][C] 0.5055[/C][C] 0.989[/C][C] 0.4945[/C][/ROW]
[ROW][C]63[/C][C] 0.4235[/C][C] 0.847[/C][C] 0.5765[/C][/ROW]
[ROW][C]64[/C][C] 0.4321[/C][C] 0.8641[/C][C] 0.5679[/C][/ROW]
[ROW][C]65[/C][C] 0.553[/C][C] 0.894[/C][C] 0.447[/C][/ROW]
[ROW][C]66[/C][C] 0.744[/C][C] 0.512[/C][C] 0.256[/C][/ROW]
[ROW][C]67[/C][C] 0.6567[/C][C] 0.6867[/C][C] 0.3433[/C][/ROW]
[ROW][C]68[/C][C] 0.5563[/C][C] 0.8873[/C][C] 0.4437[/C][/ROW]
[ROW][C]69[/C][C] 0.5346[/C][C] 0.9308[/C][C] 0.4654[/C][/ROW]
[ROW][C]70[/C][C] 0.4234[/C][C] 0.8467[/C][C] 0.5766[/C][/ROW]
[ROW][C]71[/C][C] 0.3149[/C][C] 0.6298[/C][C] 0.6851[/C][/ROW]
[ROW][C]72[/C][C] 0.2147[/C][C] 0.4293[/C][C] 0.7853[/C][/ROW]
[ROW][C]73[/C][C] 0.1232[/C][C] 0.2464[/C][C] 0.8768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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
7 0.8225 0.3549 0.1775
8 0.7163 0.5673 0.2837
9 0.6907 0.6187 0.3093
10 0.6749 0.6502 0.3251
11 0.77 0.46 0.23
12 0.681 0.638 0.319
13 0.6057 0.7886 0.3943
14 0.685 0.63 0.315
15 0.641 0.718 0.359
16 0.6695 0.661 0.3305
17 0.5863 0.8273 0.4137
18 0.5009 0.9982 0.4991
19 0.5611 0.8779 0.4389
20 0.604 0.792 0.396
21 0.5301 0.9399 0.4699
22 0.5112 0.9775 0.4888
23 0.4343 0.8686 0.5657
24 0.4557 0.9115 0.5443
25 0.6778 0.6444 0.3222
26 0.8083 0.3834 0.1917
27 0.7571 0.4859 0.2429
28 0.6994 0.6013 0.3006
29 0.8532 0.2935 0.1468
30 0.8115 0.3769 0.1885
31 0.7771 0.4458 0.2229
32 0.7378 0.5244 0.2622
33 0.6946 0.6108 0.3054
34 0.6488 0.7024 0.3512
35 0.6746 0.6509 0.3254
36 0.6445 0.7111 0.3555
37 0.6174 0.7653 0.3826
38 0.5694 0.8611 0.4306
39 0.674 0.652 0.326
40 0.6536 0.6927 0.3464
41 0.5897 0.8206 0.4103
42 0.542 0.9159 0.458
43 0.5548 0.8905 0.4452
44 0.5392 0.9215 0.4608
45 0.4739 0.9479 0.5261
46 0.427 0.8539 0.573
47 0.4574 0.9148 0.5426
48 0.5204 0.9593 0.4796
49 0.5764 0.8472 0.4236
50 0.5427 0.9146 0.4573
51 0.4732 0.9463 0.5268
52 0.4247 0.8494 0.5753
53 0.3564 0.7127 0.6436
54 0.3265 0.653 0.6735
55 0.6081 0.7838 0.3919
56 0.7234 0.5532 0.2766
57 0.7313 0.5373 0.2687
58 0.6996 0.6007 0.3004
59 0.6665 0.6671 0.3335
60 0.5949 0.8101 0.4051
61 0.525 0.9499 0.475
62 0.5055 0.989 0.4945
63 0.4235 0.847 0.5765
64 0.4321 0.8641 0.5679
65 0.553 0.894 0.447
66 0.744 0.512 0.256
67 0.6567 0.6867 0.3433
68 0.5563 0.8873 0.4437
69 0.5346 0.9308 0.4654
70 0.4234 0.8467 0.5766
71 0.3149 0.6298 0.6851
72 0.2147 0.4293 0.7853
73 0.1232 0.2464 0.8768







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319294&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319294&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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 level00OK
10% type I error level00OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 74, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 70, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 74, p-value = 1

\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 = 0, df1 = 2, df2 = 74, p-value = 1
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 70, p-value = 1
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 74, p-value = 1
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319294&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 = 0, df1 = 2, df2 = 74, p-value = 1
[/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, df1 = 6, df2 = 70, p-value = 1
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 74, p-value = 1
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319294&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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 = 0, df1 = 2, df2 = 74, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 70, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 74, p-value = 1







Variance Inflation Factors (Multicollinearity)
> vif
StatusDummy  CurryDummy Interaction 
          2           2           3 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
StatusDummy  CurryDummy Interaction 
          2           2           3 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319294&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
StatusDummy  CurryDummy Interaction 
          2           2           3 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319294&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319294&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
StatusDummy  CurryDummy Interaction 
          2           2           3 



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
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')