## Free Statistics

of Irreproducible Research!

Author's title
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
QR Codes:

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 Output view raw output of R engine Computing time 3 seconds R Server Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=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 Output view raw output of R engine Computing time 3 seconds R Server Big 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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-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
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]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 Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

\begin{tabular}{lllllllll}
\hline
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]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 Description Link Histogram Compute Central Tendency Compute QQ Plot Compute Kernel Density Plot Compute Skewness/Kurtosis Test Compute Skewness-Kurtosis Plot Compute Harrell-Davis Plot Compute Bootstrap Plot -- Central Tendency Compute Blocked Bootstrap Plot -- Central Tendency Compute (Partial) Autocorrelation Plot Compute Spectral Analysis Compute Tukey lambda PPCC Plot Compute Box-Cox Normality Plot Compute Summary Statistics Compute

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 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 Index Actuals InterpolationForecast ResidualsPrediction 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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-values Alternative Hypothesis breakpoint index greater 2-sided less 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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

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

 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 

library(lattice)library(lmtest)library(car)library(MASS)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testmywarning <- ''par6 <- as.numeric(par6)if(is.na(par6)) {par6 <- 12mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'}par1 <- as.numeric(par1)if(is.na(par1)) {par1 <- 1mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'}if (par4=='') par4 <- 0par4 <- as.numeric(par4)if (!is.numeric(par4)) par4 <- 0if (par5=='') par5 <- 0par5 <- as.numeric(par5)if (!is.numeric(par5)) par5 <- 0x <- 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 <- x1if (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 + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)print(z)plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$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-STATH0: 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$resid)myra <-table.start()a <- table.row.start(a)a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <- table.element(a,'Description',1,TRUE)a <- table.element(a,'Link',1,TRUE)a <- table.row.end(a)a <- table.row.start(a)a <-table.element(a,'Histogram',1,header=TRUE)a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$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$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)a <- table.row.end(a)a<-table.end(a)table.save(a,file='mytable7.tab')if(n < 200) {a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))a<-table.element(a,formatC(signif(x[i]-mysum$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')