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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 computationSat, 03 Nov 2012 06:04:03 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t13519371060jyez3vpbb9x80m.htm/, Retrieved Tue, 09 Aug 2022 20:59:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185703, Retrieved Tue, 09 Aug 2022 20:59:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regressi...] [2012-11-03 10:04:03] [a641906195a0eb35087b0121beaccdc9] [Current]
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Dataseries X:
5.029	4.768	4.812	5.302	5.562	5.307	5.350	5.372	5.199	5.070	4.552
24.994	23.191	22.269	23.291	24.151	24.046	25.278	25.919	26.277	25.048	24.926
6.639	6.100	6.054	6.321	6.702	6.671	7.101	7.353	7.320	7.117	6.999
3.451	3.099	2.936	3.116	3.286	3.398	3.435	3.657	3.699	3.526	3.493
5.844	5.409	5.263	5.391	5.609	5.429	5.835	5.995	6.234	5.812	5.874
3.954	3.603	3.587	3.765	3.842	3.834	4.039	3.977	4.065	3.907	3.881
5.106	4.980	4.429	4.698	4.712	4.714	4.868	4.937	4.959	4.686	4.679
15.100	14.151	13.353	13.184	13.583	13.788	14.185	14.270	14.137	13.185	12.681
279	274	277	266	279	256	283	328	335	282	287
1.567	1.451	1.439	1.441	1.562	1.532	1.616	1.497	1.578	1.430	1.411
5.616	5.082	4.801	4.676	4.826	4.851	4.990	5.149	4.934	4.728	4.524
4.560	4.425	4.182	4.170	4.321	4.318	4.429	4.518	4.441	4.063	3.946
1.262	1.277	1.158	1.121	1.102	1.209	1.212	1.253	1.229	1.133	1.141
2.095	1.916	1.773	1.776	1.772	1.878	1.938	1.853	1.955	1.831	1.659


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185703&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185703&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185703&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 7 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

 Multiple Linear Regression - Estimated Regression Equation 2000[t] = -0.00379764888319456 + 0.7446210599861052001[t] + 1.469475639706472002[t] -1.10699759664092003[t] + 0.4454194100285522004[t] + 0.7053836263144072005[t] -1.593075580421192006[t] -0.850058938679992007[t] -0.29029246710592008[t] + 1.657276391799572009[t] + 0.05958011549452232010[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
2000[t] =  -0.00379764888319456 +  0.7446210599861052001[t] +  1.469475639706472002[t] -1.10699759664092003[t] +  0.4454194100285522004[t] +  0.7053836263144072005[t] -1.593075580421192006[t] -0.850058938679992007[t] -0.29029246710592008[t] +  1.657276391799572009[t] +  0.05958011549452232010[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185703&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]2000[t] =  -0.00379764888319456 +  0.7446210599861052001[t] +  1.469475639706472002[t] -1.10699759664092003[t] +  0.4454194100285522004[t] +  0.7053836263144072005[t] -1.593075580421192006[t] -0.850058938679992007[t] -0.29029246710592008[t] +  1.657276391799572009[t] +  0.05958011549452232010[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185703&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185703&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 2000[t] = -0.00379764888319456 + 0.7446210599861052001[t] + 1.469475639706472002[t] -1.10699759664092003[t] + 0.4454194100285522004[t] + 0.7053836263144072005[t] -1.593075580421192006[t] -0.850058938679992007[t] -0.29029246710592008[t] + 1.657276391799572009[t] + 0.05958011549452232010[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) -0.00379764888319456 0.032571 -0.1166 0.914549 0.457274 2001 0.744621059986105 0.301762 2.4676 0.090258 0.045129 2002 1.46947563970647 0.46086 3.1885 0.049766 0.024883 2003 -1.1069975966409 0.56029 -1.9758 0.142645 0.071322 2004 0.445419410028552 0.639927 0.696 0.536477 0.268239 2005 0.705383626314407 0.477564 1.477 0.236166 0.118083 2006 -1.59307558042119 0.705436 -2.2583 0.109099 0.054549 2007 -0.85005893867999 0.348086 -2.4421 0.092327 0.046164 2008 -0.2902924671059 0.300657 -0.9655 0.405503 0.202752 2009 1.65727639179957 0.551187 3.0067 0.05736 0.02868 2010 0.0595801154945223 0.353018 0.1688 0.876712 0.438356

\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) & -0.00379764888319456 & 0.032571 & -0.1166 & 0.914549 & 0.457274 \tabularnewline
2001 & 0.744621059986105 & 0.301762 & 2.4676 & 0.090258 & 0.045129 \tabularnewline
2002 & 1.46947563970647 & 0.46086 & 3.1885 & 0.049766 & 0.024883 \tabularnewline
2003 & -1.1069975966409 & 0.56029 & -1.9758 & 0.142645 & 0.071322 \tabularnewline
2004 & 0.445419410028552 & 0.639927 & 0.696 & 0.536477 & 0.268239 \tabularnewline
2005 & 0.705383626314407 & 0.477564 & 1.477 & 0.236166 & 0.118083 \tabularnewline
2006 & -1.59307558042119 & 0.705436 & -2.2583 & 0.109099 & 0.054549 \tabularnewline
2007 & -0.85005893867999 & 0.348086 & -2.4421 & 0.092327 & 0.046164 \tabularnewline
2008 & -0.2902924671059 & 0.300657 & -0.9655 & 0.405503 & 0.202752 \tabularnewline
2009 & 1.65727639179957 & 0.551187 & 3.0067 & 0.05736 & 0.02868 \tabularnewline
2010 & 0.0595801154945223 & 0.353018 & 0.1688 & 0.876712 & 0.438356 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185703&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]-0.00379764888319456[/C][C]0.032571[/C][C]-0.1166[/C][C]0.914549[/C][C]0.457274[/C][/ROW]
[ROW][C]2001[/C][C]0.744621059986105[/C][C]0.301762[/C][C]2.4676[/C][C]0.090258[/C][C]0.045129[/C][/ROW]
[ROW][C]2002[/C][C]1.46947563970647[/C][C]0.46086[/C][C]3.1885[/C][C]0.049766[/C][C]0.024883[/C][/ROW]
[ROW][C]2003[/C][C]-1.1069975966409[/C][C]0.56029[/C][C]-1.9758[/C][C]0.142645[/C][C]0.071322[/C][/ROW]
[ROW][C]2004[/C][C]0.445419410028552[/C][C]0.639927[/C][C]0.696[/C][C]0.536477[/C][C]0.268239[/C][/ROW]
[ROW][C]2005[/C][C]0.705383626314407[/C][C]0.477564[/C][C]1.477[/C][C]0.236166[/C][C]0.118083[/C][/ROW]
[ROW][C]2006[/C][C]-1.59307558042119[/C][C]0.705436[/C][C]-2.2583[/C][C]0.109099[/C][C]0.054549[/C][/ROW]
[ROW][C]2007[/C][C]-0.85005893867999[/C][C]0.348086[/C][C]-2.4421[/C][C]0.092327[/C][C]0.046164[/C][/ROW]
[ROW][C]2008[/C][C]-0.2902924671059[/C][C]0.300657[/C][C]-0.9655[/C][C]0.405503[/C][C]0.202752[/C][/ROW]
[ROW][C]2009[/C][C]1.65727639179957[/C][C]0.551187[/C][C]3.0067[/C][C]0.05736[/C][C]0.02868[/C][/ROW]
[ROW][C]2010[/C][C]0.0595801154945223[/C][C]0.353018[/C][C]0.1688[/C][C]0.876712[/C][C]0.438356[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185703&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185703&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) -0.00379764888319456 0.032571 -0.1166 0.914549 0.457274 2001 0.744621059986105 0.301762 2.4676 0.090258 0.045129 2002 1.46947563970647 0.46086 3.1885 0.049766 0.024883 2003 -1.1069975966409 0.56029 -1.9758 0.142645 0.071322 2004 0.445419410028552 0.639927 0.696 0.536477 0.268239 2005 0.705383626314407 0.477564 1.477 0.236166 0.118083 2006 -1.59307558042119 0.705436 -2.2583 0.109099 0.054549 2007 -0.85005893867999 0.348086 -2.4421 0.092327 0.046164 2008 -0.2902924671059 0.300657 -0.9655 0.405503 0.202752 2009 1.65727639179957 0.551187 3.0067 0.05736 0.02868 2010 0.0595801154945223 0.353018 0.1688 0.876712 0.438356

 Multiple Linear Regression - Regression Statistics Multiple R 0.999999875739288 R-squared 0.999999751478592 Adjusted R-squared 0.9999989230739 F-TEST (value) 1207139.16917269 F-TEST (DF numerator) 10 F-TEST (DF denominator) 3 p-value 1.11793729828946e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0758428501486384 Sum Squared Residuals 0.0172564137560065

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999999875739288 \tabularnewline
R-squared & 0.999999751478592 \tabularnewline
Adjusted R-squared & 0.9999989230739 \tabularnewline
F-TEST (value) & 1207139.16917269 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 3 \tabularnewline
p-value & 1.11793729828946e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0758428501486384 \tabularnewline
Sum Squared Residuals & 0.0172564137560065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185703&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999999875739288[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999999751478592[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.9999989230739[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1207139.16917269[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]3[/C][/ROW]
[ROW][C]p-value[/C][C]1.11793729828946e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0758428501486384[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0172564137560065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185703&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185703&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 0.999999875739288 R-squared 0.999999751478592 Adjusted R-squared 0.9999989230739 F-TEST (value) 1207139.16917269 F-TEST (DF numerator) 10 F-TEST (DF denominator) 3 p-value 1.11793729828946e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0758428501486384 Sum Squared Residuals 0.0172564137560065

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 5.029 5.04416323126643 -0.0151632312664322 2 24.994 24.9904559094071 0.00354409059291746 3 6.639 6.65206296548628 -0.0130629654862816 4 3.451 3.42629863421247 0.0247013657875283 5 5.844 5.89845048944829 -0.0544504894482891 6 3.954 3.90903113889813 0.0449688611018674 7 5.106 5.08942208582926 0.0165779141707391 8 15.1 15.111290059657 -0.0112900596570223 9 279 278.999726021671 0.000273978329242531 10 1.567 1.52137515289144 0.0456248471085576 11 5.616 5.57684797501311 0.0391520249868846 12 4.56 4.57395169653826 -0.0139516965382585 13 1.262 1.34442709170879 -0.0824270917087895 14 2.095 2.07949754797266 0.0155024520273359

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.029 & 5.04416323126643 & -0.0151632312664322 \tabularnewline
2 & 24.994 & 24.9904559094071 & 0.00354409059291746 \tabularnewline
3 & 6.639 & 6.65206296548628 & -0.0130629654862816 \tabularnewline
4 & 3.451 & 3.42629863421247 & 0.0247013657875283 \tabularnewline
5 & 5.844 & 5.89845048944829 & -0.0544504894482891 \tabularnewline
6 & 3.954 & 3.90903113889813 & 0.0449688611018674 \tabularnewline
7 & 5.106 & 5.08942208582926 & 0.0165779141707391 \tabularnewline
8 & 15.1 & 15.111290059657 & -0.0112900596570223 \tabularnewline
9 & 279 & 278.999726021671 & 0.000273978329242531 \tabularnewline
10 & 1.567 & 1.52137515289144 & 0.0456248471085576 \tabularnewline
11 & 5.616 & 5.57684797501311 & 0.0391520249868846 \tabularnewline
12 & 4.56 & 4.57395169653826 & -0.0139516965382585 \tabularnewline
13 & 1.262 & 1.34442709170879 & -0.0824270917087895 \tabularnewline
14 & 2.095 & 2.07949754797266 & 0.0155024520273359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185703&T=4

[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]5.029[/C][C]5.04416323126643[/C][C]-0.0151632312664322[/C][/ROW]
[ROW][C]2[/C][C]24.994[/C][C]24.9904559094071[/C][C]0.00354409059291746[/C][/ROW]
[ROW][C]3[/C][C]6.639[/C][C]6.65206296548628[/C][C]-0.0130629654862816[/C][/ROW]
[ROW][C]4[/C][C]3.451[/C][C]3.42629863421247[/C][C]0.0247013657875283[/C][/ROW]
[ROW][C]5[/C][C]5.844[/C][C]5.89845048944829[/C][C]-0.0544504894482891[/C][/ROW]
[ROW][C]6[/C][C]3.954[/C][C]3.90903113889813[/C][C]0.0449688611018674[/C][/ROW]
[ROW][C]7[/C][C]5.106[/C][C]5.08942208582926[/C][C]0.0165779141707391[/C][/ROW]
[ROW][C]8[/C][C]15.1[/C][C]15.111290059657[/C][C]-0.0112900596570223[/C][/ROW]
[ROW][C]9[/C][C]279[/C][C]278.999726021671[/C][C]0.000273978329242531[/C][/ROW]
[ROW][C]10[/C][C]1.567[/C][C]1.52137515289144[/C][C]0.0456248471085576[/C][/ROW]
[ROW][C]11[/C][C]5.616[/C][C]5.57684797501311[/C][C]0.0391520249868846[/C][/ROW]
[ROW][C]12[/C][C]4.56[/C][C]4.57395169653826[/C][C]-0.0139516965382585[/C][/ROW]
[ROW][C]13[/C][C]1.262[/C][C]1.34442709170879[/C][C]-0.0824270917087895[/C][/ROW]
[ROW][C]14[/C][C]2.095[/C][C]2.07949754797266[/C][C]0.0155024520273359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185703&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185703&T=4

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 5.029 5.04416323126643 -0.0151632312664322 2 24.994 24.9904559094071 0.00354409059291746 3 6.639 6.65206296548628 -0.0130629654862816 4 3.451 3.42629863421247 0.0247013657875283 5 5.844 5.89845048944829 -0.0544504894482891 6 3.954 3.90903113889813 0.0449688611018674 7 5.106 5.08942208582926 0.0165779141707391 8 15.1 15.111290059657 -0.0112900596570223 9 279 278.999726021671 0.000273978329242531 10 1.567 1.52137515289144 0.0456248471085576 11 5.616 5.57684797501311 0.0391520249868846 12 4.56 4.57395169653826 -0.0139516965382585 13 1.262 1.34442709170879 -0.0824270917087895 14 2.095 2.07949754797266 0.0155024520273359

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Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- as.numeric(par1)x <- 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'){x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))for (i in 1:n-1) {for (j in 1:k) {x2[i,j] <- x[i+1,j] - x[i,j]}}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[1,])if (par3 == 'Linear Trend'){x <- cbind(x, c(1:n))colnames(x)[k+1] <- 't'}xk <- length(x[1,])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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')grid()dev.off()bitmap(file='test2.png')hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')grid()dev.off()bitmap(file='test3.png')densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')dev.off()bitmap(file='test4.png')qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')qqline(mysum$resid)grid()dev.off()(myerror <- as.ts(mysum$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')if (rownames(mysum$coefficients)[i] != '(Intercept)') {myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)a<-table.element(a,mysum$coefficients[i,1])a<-table.element(a, round(mysum$coefficients[i,2],6))a<-table.element(a, round(mysum$coefficients[i,3],4))a<-table.element(a, round(mysum$coefficients[i,4],6))a<-table.element(a, round(mysum$coefficients[i,4]/2,6))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable2.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Multiple R',1,TRUE)a<-table.element(a, sqrt(mysum$r.squared))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'R-squared',1,TRUE)a<-table.element(a, mysum$r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Adjusted R-squared',1,TRUE)a<-table.element(a, mysum$adj.r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (value)',1,TRUE)a<-table.element(a, mysum$fstatistic[1])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)a<-table.element(a, mysum$fstatistic[2])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)a<-table.element(a, mysum$fstatistic[3])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'p-value',1,TRUE)a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))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, mysum$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$resid[i])a<-table.element(a,mysum\$resid[i])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,gqarr[mypoint-kp3+1,1])a<-table.element(a,gqarr[mypoint-kp3+1,2])a<-table.element(a,gqarr[mypoint-kp3+1,3])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,numsignificant1)a<-table.element(a,numsignificant1/numgqtests)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,numsignificant5)a<-table.element(a,numsignificant5/numgqtests)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,numsignificant10)a<-table.element(a,numsignificant10/numgqtests)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')}