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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 19 Dec 2017 20:01:19 +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/2017/Dec/19/t1513710222fbq731chzd3raup.htm/, Retrieved Thu, 31 Oct 2024 23:10:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310399, Retrieved Thu, 31 Oct 2024 23:10:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-12-19 19:01:19] [8829069b4432872c842806a35f4fa8df] [Current]
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Dataseries X:
10113.2	8640.1	18753.3
924.7	30545.5	31470.2
37945.3	18410.4	56355.7
4768.5	8282	13050.5
10788.3	5404.7	16192.9
28541.9	19990.2	48532.1
38134.1	28113.1	66247.2
8743.4	7767.3	16510.7
12827.4	9445	22272.4
28349.6	16279.5	44629.1
2953.5	10243	13196.6
3920.7	6480.1	10400.7
9060.8	13421.1	22481.9
17014.1	29231	46245.1
10016.6	12949.8	22966.5
13261.1	7453.9	20715
10650.6	5713.2	16363.8
60487.7	1937.3	62425
9461.3	2517.3	11978.7
50805.4	17727.6	68533
15058.3	2716.9	17775.2
7440.1	3530.4	10970.4
12124	2216.6	14340.6
17840.8	2571.6	20412.5
40921.1	5398.8	46319.9
46508.3	8	46516.3
56876.1	14645.9	71522
11952.1	2600.7	14552.8
8307.3	4012.2	12319.5
35261.4	5361.9	40623.4
33343.2	0	33343.2
26334	61.7	26395.6
31187	1.7	31188.7
14359.6	10.3	14369.9
6643.6	316.3	6959.9
9767.2	264.4	10031.6
407.7	0	407.7
76990.1	0	0
0	0	0
820090.6	304269.6	1124360.2
0	11718.4	11718.4
0	0	0
820090.6	315988	1136078.6




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&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]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310399&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 time7 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
a[t] = + 2002.74 -1.02515b[t] + 1.00434c[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  2002.74 -1.02515b[t] +  1.00434c[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  2002.74 -1.02515b[t] +  1.00434c[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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
a[t] = + 2002.74 -1.02515b[t] + 1.00434c[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2003 1938+1.0340e+00 0.3075 0.1538
b-1.025 0.2694-3.8050e+00 0.0004762 0.0002381
c+1.004 0.07414+1.3550e+01 1.563e-16 7.817e-17

\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) & +2003 &  1938 & +1.0340e+00 &  0.3075 &  0.1538 \tabularnewline
b & -1.025 &  0.2694 & -3.8050e+00 &  0.0004762 &  0.0002381 \tabularnewline
c & +1.004 &  0.07414 & +1.3550e+01 &  1.563e-16 &  7.817e-17 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&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]+2003[/C][C] 1938[/C][C]+1.0340e+00[/C][C] 0.3075[/C][C] 0.1538[/C][/ROW]
[ROW][C]b[/C][C]-1.025[/C][C] 0.2694[/C][C]-3.8050e+00[/C][C] 0.0004762[/C][C] 0.0002381[/C][/ROW]
[ROW][C]c[/C][C]+1.004[/C][C] 0.07414[/C][C]+1.3550e+01[/C][C] 1.563e-16[/C][C] 7.817e-17[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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)+2003 1938+1.0340e+00 0.3075 0.1538
b-1.025 0.2694-3.8050e+00 0.0004762 0.0002381
c+1.004 0.07414+1.3550e+01 1.563e-16 7.817e-17







Multiple Linear Regression - Regression Statistics
Multiple R 0.9977
R-squared 0.9953
Adjusted R-squared 0.9951
F-TEST (value) 4258
F-TEST (DF numerator)2
F-TEST (DF denominator)40
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.201e+04
Sum Squared Residuals 5.773e+09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9977 \tabularnewline
R-squared &  0.9953 \tabularnewline
Adjusted R-squared &  0.9951 \tabularnewline
F-TEST (value) &  4258 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.201e+04 \tabularnewline
Sum Squared Residuals &  5.773e+09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9977[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9953[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9951[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4258[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.201e+04[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 5.773e+09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 0.9977
R-squared 0.9953
Adjusted R-squared 0.9951
F-TEST (value) 4258
F-TEST (DF numerator)2
F-TEST (DF denominator)40
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.201e+04
Sum Squared Residuals 5.773e+09







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=310399&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=310399&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 1.011e+04 1.198e+04-1867
2 924.7 2296-1371
3 3.795e+04 3.973e+04-1784
4 4768 6620-1851
5 1.079e+04 1.273e+04-1937
6 2.854e+04 3.025e+04-1711
7 3.813e+04 3.972e+04-1583
8 8743 1.062e+04-1879
9 1.283e+04 1.469e+04-1862
10 2.835e+04 3.014e+04-1787
11 2954 4756-1803
12 3921 5806-1885
13 9061 1.082e+04-1763
14 1.701e+04 1.848e+04-1468
15 1.002e+04 1.179e+04-1777
16 1.326e+04 1.517e+04-1905
17 1.065e+04 1.258e+04-1930
18 6.049e+04 6.271e+04-2225
19 9461 1.145e+04-1992
20 5.081e+04 5.266e+04-1855
21 1.506e+04 1.707e+04-2012
22 7440 9402-1962
23 1.212e+04 1.413e+04-2009
24 1.784e+04 1.987e+04-2027
25 4.092e+04 4.299e+04-2068
26 4.651e+04 4.871e+04-2205
27 5.688e+04 5.882e+04-1945
28 1.195e+04 1.395e+04-2001
29 8307 1.026e+04-1955
30 3.526e+04 3.731e+04-2044
31 3.334e+04 3.549e+04-2148
32 2.633e+04 2.845e+04-2116
33 3.119e+04 3.333e+04-2138
34 1.436e+04 1.642e+04-2065
35 6644 8669-2025
36 9767 1.181e+04-2040
37 407.7 2412-2005
38 7.699e+04 2003 7.499e+04
39 0 2003-2003
40 8.201e+05 8.193e+05 765.8
41 0 1759-1759
42 0 2003-2003
43 8.201e+05 8.191e+05 1010

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1.011e+04 &  1.198e+04 & -1867 \tabularnewline
2 &  924.7 &  2296 & -1371 \tabularnewline
3 &  3.795e+04 &  3.973e+04 & -1784 \tabularnewline
4 &  4768 &  6620 & -1851 \tabularnewline
5 &  1.079e+04 &  1.273e+04 & -1937 \tabularnewline
6 &  2.854e+04 &  3.025e+04 & -1711 \tabularnewline
7 &  3.813e+04 &  3.972e+04 & -1583 \tabularnewline
8 &  8743 &  1.062e+04 & -1879 \tabularnewline
9 &  1.283e+04 &  1.469e+04 & -1862 \tabularnewline
10 &  2.835e+04 &  3.014e+04 & -1787 \tabularnewline
11 &  2954 &  4756 & -1803 \tabularnewline
12 &  3921 &  5806 & -1885 \tabularnewline
13 &  9061 &  1.082e+04 & -1763 \tabularnewline
14 &  1.701e+04 &  1.848e+04 & -1468 \tabularnewline
15 &  1.002e+04 &  1.179e+04 & -1777 \tabularnewline
16 &  1.326e+04 &  1.517e+04 & -1905 \tabularnewline
17 &  1.065e+04 &  1.258e+04 & -1930 \tabularnewline
18 &  6.049e+04 &  6.271e+04 & -2225 \tabularnewline
19 &  9461 &  1.145e+04 & -1992 \tabularnewline
20 &  5.081e+04 &  5.266e+04 & -1855 \tabularnewline
21 &  1.506e+04 &  1.707e+04 & -2012 \tabularnewline
22 &  7440 &  9402 & -1962 \tabularnewline
23 &  1.212e+04 &  1.413e+04 & -2009 \tabularnewline
24 &  1.784e+04 &  1.987e+04 & -2027 \tabularnewline
25 &  4.092e+04 &  4.299e+04 & -2068 \tabularnewline
26 &  4.651e+04 &  4.871e+04 & -2205 \tabularnewline
27 &  5.688e+04 &  5.882e+04 & -1945 \tabularnewline
28 &  1.195e+04 &  1.395e+04 & -2001 \tabularnewline
29 &  8307 &  1.026e+04 & -1955 \tabularnewline
30 &  3.526e+04 &  3.731e+04 & -2044 \tabularnewline
31 &  3.334e+04 &  3.549e+04 & -2148 \tabularnewline
32 &  2.633e+04 &  2.845e+04 & -2116 \tabularnewline
33 &  3.119e+04 &  3.333e+04 & -2138 \tabularnewline
34 &  1.436e+04 &  1.642e+04 & -2065 \tabularnewline
35 &  6644 &  8669 & -2025 \tabularnewline
36 &  9767 &  1.181e+04 & -2040 \tabularnewline
37 &  407.7 &  2412 & -2005 \tabularnewline
38 &  7.699e+04 &  2003 &  7.499e+04 \tabularnewline
39 &  0 &  2003 & -2003 \tabularnewline
40 &  8.201e+05 &  8.193e+05 &  765.8 \tabularnewline
41 &  0 &  1759 & -1759 \tabularnewline
42 &  0 &  2003 & -2003 \tabularnewline
43 &  8.201e+05 &  8.191e+05 &  1010 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&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] 1.011e+04[/C][C] 1.198e+04[/C][C]-1867[/C][/ROW]
[ROW][C]2[/C][C] 924.7[/C][C] 2296[/C][C]-1371[/C][/ROW]
[ROW][C]3[/C][C] 3.795e+04[/C][C] 3.973e+04[/C][C]-1784[/C][/ROW]
[ROW][C]4[/C][C] 4768[/C][C] 6620[/C][C]-1851[/C][/ROW]
[ROW][C]5[/C][C] 1.079e+04[/C][C] 1.273e+04[/C][C]-1937[/C][/ROW]
[ROW][C]6[/C][C] 2.854e+04[/C][C] 3.025e+04[/C][C]-1711[/C][/ROW]
[ROW][C]7[/C][C] 3.813e+04[/C][C] 3.972e+04[/C][C]-1583[/C][/ROW]
[ROW][C]8[/C][C] 8743[/C][C] 1.062e+04[/C][C]-1879[/C][/ROW]
[ROW][C]9[/C][C] 1.283e+04[/C][C] 1.469e+04[/C][C]-1862[/C][/ROW]
[ROW][C]10[/C][C] 2.835e+04[/C][C] 3.014e+04[/C][C]-1787[/C][/ROW]
[ROW][C]11[/C][C] 2954[/C][C] 4756[/C][C]-1803[/C][/ROW]
[ROW][C]12[/C][C] 3921[/C][C] 5806[/C][C]-1885[/C][/ROW]
[ROW][C]13[/C][C] 9061[/C][C] 1.082e+04[/C][C]-1763[/C][/ROW]
[ROW][C]14[/C][C] 1.701e+04[/C][C] 1.848e+04[/C][C]-1468[/C][/ROW]
[ROW][C]15[/C][C] 1.002e+04[/C][C] 1.179e+04[/C][C]-1777[/C][/ROW]
[ROW][C]16[/C][C] 1.326e+04[/C][C] 1.517e+04[/C][C]-1905[/C][/ROW]
[ROW][C]17[/C][C] 1.065e+04[/C][C] 1.258e+04[/C][C]-1930[/C][/ROW]
[ROW][C]18[/C][C] 6.049e+04[/C][C] 6.271e+04[/C][C]-2225[/C][/ROW]
[ROW][C]19[/C][C] 9461[/C][C] 1.145e+04[/C][C]-1992[/C][/ROW]
[ROW][C]20[/C][C] 5.081e+04[/C][C] 5.266e+04[/C][C]-1855[/C][/ROW]
[ROW][C]21[/C][C] 1.506e+04[/C][C] 1.707e+04[/C][C]-2012[/C][/ROW]
[ROW][C]22[/C][C] 7440[/C][C] 9402[/C][C]-1962[/C][/ROW]
[ROW][C]23[/C][C] 1.212e+04[/C][C] 1.413e+04[/C][C]-2009[/C][/ROW]
[ROW][C]24[/C][C] 1.784e+04[/C][C] 1.987e+04[/C][C]-2027[/C][/ROW]
[ROW][C]25[/C][C] 4.092e+04[/C][C] 4.299e+04[/C][C]-2068[/C][/ROW]
[ROW][C]26[/C][C] 4.651e+04[/C][C] 4.871e+04[/C][C]-2205[/C][/ROW]
[ROW][C]27[/C][C] 5.688e+04[/C][C] 5.882e+04[/C][C]-1945[/C][/ROW]
[ROW][C]28[/C][C] 1.195e+04[/C][C] 1.395e+04[/C][C]-2001[/C][/ROW]
[ROW][C]29[/C][C] 8307[/C][C] 1.026e+04[/C][C]-1955[/C][/ROW]
[ROW][C]30[/C][C] 3.526e+04[/C][C] 3.731e+04[/C][C]-2044[/C][/ROW]
[ROW][C]31[/C][C] 3.334e+04[/C][C] 3.549e+04[/C][C]-2148[/C][/ROW]
[ROW][C]32[/C][C] 2.633e+04[/C][C] 2.845e+04[/C][C]-2116[/C][/ROW]
[ROW][C]33[/C][C] 3.119e+04[/C][C] 3.333e+04[/C][C]-2138[/C][/ROW]
[ROW][C]34[/C][C] 1.436e+04[/C][C] 1.642e+04[/C][C]-2065[/C][/ROW]
[ROW][C]35[/C][C] 6644[/C][C] 8669[/C][C]-2025[/C][/ROW]
[ROW][C]36[/C][C] 9767[/C][C] 1.181e+04[/C][C]-2040[/C][/ROW]
[ROW][C]37[/C][C] 407.7[/C][C] 2412[/C][C]-2005[/C][/ROW]
[ROW][C]38[/C][C] 7.699e+04[/C][C] 2003[/C][C] 7.499e+04[/C][/ROW]
[ROW][C]39[/C][C] 0[/C][C] 2003[/C][C]-2003[/C][/ROW]
[ROW][C]40[/C][C] 8.201e+05[/C][C] 8.193e+05[/C][C] 765.8[/C][/ROW]
[ROW][C]41[/C][C] 0[/C][C] 1759[/C][C]-1759[/C][/ROW]
[ROW][C]42[/C][C] 0[/C][C] 2003[/C][C]-2003[/C][/ROW]
[ROW][C]43[/C][C] 8.201e+05[/C][C] 8.191e+05[/C][C] 1010[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 1.011e+04 1.198e+04-1867
2 924.7 2296-1371
3 3.795e+04 3.973e+04-1784
4 4768 6620-1851
5 1.079e+04 1.273e+04-1937
6 2.854e+04 3.025e+04-1711
7 3.813e+04 3.972e+04-1583
8 8743 1.062e+04-1879
9 1.283e+04 1.469e+04-1862
10 2.835e+04 3.014e+04-1787
11 2954 4756-1803
12 3921 5806-1885
13 9061 1.082e+04-1763
14 1.701e+04 1.848e+04-1468
15 1.002e+04 1.179e+04-1777
16 1.326e+04 1.517e+04-1905
17 1.065e+04 1.258e+04-1930
18 6.049e+04 6.271e+04-2225
19 9461 1.145e+04-1992
20 5.081e+04 5.266e+04-1855
21 1.506e+04 1.707e+04-2012
22 7440 9402-1962
23 1.212e+04 1.413e+04-2009
24 1.784e+04 1.987e+04-2027
25 4.092e+04 4.299e+04-2068
26 4.651e+04 4.871e+04-2205
27 5.688e+04 5.882e+04-1945
28 1.195e+04 1.395e+04-2001
29 8307 1.026e+04-1955
30 3.526e+04 3.731e+04-2044
31 3.334e+04 3.549e+04-2148
32 2.633e+04 2.845e+04-2116
33 3.119e+04 3.333e+04-2138
34 1.436e+04 1.642e+04-2065
35 6644 8669-2025
36 9767 1.181e+04-2040
37 407.7 2412-2005
38 7.699e+04 2003 7.499e+04
39 0 2003-2003
40 8.201e+05 8.193e+05 765.8
41 0 1759-1759
42 0 2003-2003
43 8.201e+05 8.191e+05 1010







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 5.916e-17 1.183e-16 1
7 1.726e-22 3.452e-22 1
8 5.91e-28 1.182e-27 1
9 1.704e-33 3.408e-33 1
10 4.024e-39 8.048e-39 1
11 3.238e-43 6.477e-43 1
12 5.058e-48 1.012e-47 1
13 1.787e-53 3.575e-53 1
14 6.377e-59 1.275e-58 1
15 1.536e-63 3.072e-63 1
16 5.519e-69 1.104e-68 1
17 1.888e-74 3.777e-74 1
18 6.506e-80 1.301e-79 1
19 1.288e-84 2.575e-84 1
20 4.173e-90 8.346e-90 1
21 1.288e-95 2.576e-95 1
22 2.261e-100 4.521e-100 1
23 7.053e-106 1.411e-105 1
24 1.093e-110 2.186e-110 1
25 3.182e-116 6.364e-116 1
26 8.67e-122 1.734e-121 1
27 2.287e-127 4.573e-127 1
28 6.047e-133 1.209e-132 1
29 1.612e-138 3.223e-138 1
30 1.819e-143 3.638e-143 1
31 4.004e-149 8.008e-149 1
32 5.467e-154 1.093e-153 1
33 1.225e-159 2.45e-159 1
34 3.1e-165 6.2e-165 1
35 8.51e-171 1.702e-170 1
36 3.58e-176 7.16e-176 1
37 2.18e-181 4.361e-181 1

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  5.916e-17 &  1.183e-16 &  1 \tabularnewline
7 &  1.726e-22 &  3.452e-22 &  1 \tabularnewline
8 &  5.91e-28 &  1.182e-27 &  1 \tabularnewline
9 &  1.704e-33 &  3.408e-33 &  1 \tabularnewline
10 &  4.024e-39 &  8.048e-39 &  1 \tabularnewline
11 &  3.238e-43 &  6.477e-43 &  1 \tabularnewline
12 &  5.058e-48 &  1.012e-47 &  1 \tabularnewline
13 &  1.787e-53 &  3.575e-53 &  1 \tabularnewline
14 &  6.377e-59 &  1.275e-58 &  1 \tabularnewline
15 &  1.536e-63 &  3.072e-63 &  1 \tabularnewline
16 &  5.519e-69 &  1.104e-68 &  1 \tabularnewline
17 &  1.888e-74 &  3.777e-74 &  1 \tabularnewline
18 &  6.506e-80 &  1.301e-79 &  1 \tabularnewline
19 &  1.288e-84 &  2.575e-84 &  1 \tabularnewline
20 &  4.173e-90 &  8.346e-90 &  1 \tabularnewline
21 &  1.288e-95 &  2.576e-95 &  1 \tabularnewline
22 &  2.261e-100 &  4.521e-100 &  1 \tabularnewline
23 &  7.053e-106 &  1.411e-105 &  1 \tabularnewline
24 &  1.093e-110 &  2.186e-110 &  1 \tabularnewline
25 &  3.182e-116 &  6.364e-116 &  1 \tabularnewline
26 &  8.67e-122 &  1.734e-121 &  1 \tabularnewline
27 &  2.287e-127 &  4.573e-127 &  1 \tabularnewline
28 &  6.047e-133 &  1.209e-132 &  1 \tabularnewline
29 &  1.612e-138 &  3.223e-138 &  1 \tabularnewline
30 &  1.819e-143 &  3.638e-143 &  1 \tabularnewline
31 &  4.004e-149 &  8.008e-149 &  1 \tabularnewline
32 &  5.467e-154 &  1.093e-153 &  1 \tabularnewline
33 &  1.225e-159 &  2.45e-159 &  1 \tabularnewline
34 &  3.1e-165 &  6.2e-165 &  1 \tabularnewline
35 &  8.51e-171 &  1.702e-170 &  1 \tabularnewline
36 &  3.58e-176 &  7.16e-176 &  1 \tabularnewline
37 &  2.18e-181 &  4.361e-181 &  1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&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]6[/C][C] 5.916e-17[/C][C] 1.183e-16[/C][C] 1[/C][/ROW]
[ROW][C]7[/C][C] 1.726e-22[/C][C] 3.452e-22[/C][C] 1[/C][/ROW]
[ROW][C]8[/C][C] 5.91e-28[/C][C] 1.182e-27[/C][C] 1[/C][/ROW]
[ROW][C]9[/C][C] 1.704e-33[/C][C] 3.408e-33[/C][C] 1[/C][/ROW]
[ROW][C]10[/C][C] 4.024e-39[/C][C] 8.048e-39[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 3.238e-43[/C][C] 6.477e-43[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 5.058e-48[/C][C] 1.012e-47[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 1.787e-53[/C][C] 3.575e-53[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 6.377e-59[/C][C] 1.275e-58[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 1.536e-63[/C][C] 3.072e-63[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 5.519e-69[/C][C] 1.104e-68[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 1.888e-74[/C][C] 3.777e-74[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 6.506e-80[/C][C] 1.301e-79[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 1.288e-84[/C][C] 2.575e-84[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 4.173e-90[/C][C] 8.346e-90[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 1.288e-95[/C][C] 2.576e-95[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 2.261e-100[/C][C] 4.521e-100[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 7.053e-106[/C][C] 1.411e-105[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 1.093e-110[/C][C] 2.186e-110[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 3.182e-116[/C][C] 6.364e-116[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 8.67e-122[/C][C] 1.734e-121[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 2.287e-127[/C][C] 4.573e-127[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 6.047e-133[/C][C] 1.209e-132[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 1.612e-138[/C][C] 3.223e-138[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 1.819e-143[/C][C] 3.638e-143[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 4.004e-149[/C][C] 8.008e-149[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 5.467e-154[/C][C] 1.093e-153[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 1.225e-159[/C][C] 2.45e-159[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 3.1e-165[/C][C] 6.2e-165[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 8.51e-171[/C][C] 1.702e-170[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 3.58e-176[/C][C] 7.16e-176[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 2.18e-181[/C][C] 4.361e-181[/C][C] 1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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
6 5.916e-17 1.183e-16 1
7 1.726e-22 3.452e-22 1
8 5.91e-28 1.182e-27 1
9 1.704e-33 3.408e-33 1
10 4.024e-39 8.048e-39 1
11 3.238e-43 6.477e-43 1
12 5.058e-48 1.012e-47 1
13 1.787e-53 3.575e-53 1
14 6.377e-59 1.275e-58 1
15 1.536e-63 3.072e-63 1
16 5.519e-69 1.104e-68 1
17 1.888e-74 3.777e-74 1
18 6.506e-80 1.301e-79 1
19 1.288e-84 2.575e-84 1
20 4.173e-90 8.346e-90 1
21 1.288e-95 2.576e-95 1
22 2.261e-100 4.521e-100 1
23 7.053e-106 1.411e-105 1
24 1.093e-110 2.186e-110 1
25 3.182e-116 6.364e-116 1
26 8.67e-122 1.734e-121 1
27 2.287e-127 4.573e-127 1
28 6.047e-133 1.209e-132 1
29 1.612e-138 3.223e-138 1
30 1.819e-143 3.638e-143 1
31 4.004e-149 8.008e-149 1
32 5.467e-154 1.093e-153 1
33 1.225e-159 2.45e-159 1
34 3.1e-165 6.2e-165 1
35 8.51e-171 1.702e-170 1
36 3.58e-176 7.16e-176 1
37 2.18e-181 4.361e-181 1







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level32 1NOK
5% type I error level321NOK
10% type I error level321NOK

\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 & 32 &  1 & NOK \tabularnewline
5% type I error level & 32 & 1 & NOK \tabularnewline
10% type I error level & 32 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310399&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]32[/C][C] 1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310399&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 level32 1NOK
5% type I error level321NOK
10% type I error level321NOK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7612, df1 = 2, df2 = 38, p-value = 0.1856
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.498, df1 = 4, df2 = 36, p-value = 0.2233
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0318, df1 = 2, df2 = 38, p-value = 0.3661

\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 = 1.7612, df1 = 2, df2 = 38, p-value = 0.1856
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.498, df1 = 4, df2 = 36, p-value = 0.2233
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0318, df1 = 2, df2 = 38, p-value = 0.3661
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=310399&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 = 1.7612, df1 = 2, df2 = 38, p-value = 0.1856
[/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 = 1.498, df1 = 4, df2 = 36, p-value = 0.2233
[/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 = 1.0318, df1 = 2, df2 = 38, p-value = 0.3661
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310399&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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 = 1.7612, df1 = 2, df2 = 38, p-value = 0.1856
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.498, df1 = 4, df2 = 36, p-value = 0.2233
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0318, df1 = 2, df2 = 38, p-value = 0.3661







Variance Inflation Factors (Multicollinearity)
> vif
       b        c 
89.18191 89.18191 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c 
89.18191 89.18191 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=310399&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       b        c 
89.18191 89.18191 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310399&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310399&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
       b        c 
89.18191 89.18191 



Parameters (Session):
par1 = C ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;
Parameters (R input):
par1 = C ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')