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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 22 Jan 2016 10:59:50 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Jan/22/t1453460467t0y25ftjl73ip9r.htm/, Retrieved Tue, 07 May 2024 20:59:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=291695, Retrieved Tue, 07 May 2024 20:59:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact48
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regressi...] [2016-01-22 10:59:50] [0a53596547d7e78bbce4f5974f7c68a2] [Current]
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Dataseries X:
0 0 1 1 3.2 0 3.2 10.24
1 1 0 0 3.3 0 0 10.89
1 1 0 1 3 1 3 9
1 1 0 0 3.5 0 0 12.25
1 0 0 1 3.7 0 3.7 13.69
0 0 1 0 2.7 0 0 7.29
1 1 0 1 3.6 1 3.6 12.96
1 1 0 0 3.5 0 0 12.25
0 0 1 1 3.8 0 3.8 14.44
1 0 0 0 3.4 0 0 11.56
0 1 0 1 3.7 0 3.7 13.69
1 0 0 0 3.5 0 0 12.25
0 0 0 1 2.8 1 2.8 7.84
0 0 1 0 3.8 1 0 14.44
1 0 0 1 4.3 0 4.3 18.49
0 1 0 0 3.3 0 0 10.89
0 0 0 1 3.6 0 3.6 12.96
0 0 1 0 3.6 1 0 12.96
1 0 1 1 3.3 0 3.3 10.89
0 0 0 0 2.8 0 0 7.84




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291695&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291695&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291695&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Fruit[t] = -1.02088 + 0.138838Alcohol[t] -0.392502Drugs[t] + 1.81484Geslacht[t] + 0.36133Gebgewicht[t] -0.190437Sport[t] -0.537809Inter[t] + 0.036791Gebgew2[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
Fruit[t] =  -1.02088 +  0.138838Alcohol[t] -0.392502Drugs[t] +  1.81484Geslacht[t] +  0.36133Gebgewicht[t] -0.190437Sport[t] -0.537809Inter[t] +  0.036791Gebgew2[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=291695&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Fruit[t] =  -1.02088 +  0.138838Alcohol[t] -0.392502Drugs[t] +  1.81484Geslacht[t] +  0.36133Gebgewicht[t] -0.190437Sport[t] -0.537809Inter[t] +  0.036791Gebgew2[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=291695&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291695&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
Fruit[t] = -1.02088 + 0.138838Alcohol[t] -0.392502Drugs[t] + 1.81484Geslacht[t] + 0.36133Gebgewicht[t] -0.190437Sport[t] -0.537809Inter[t] + 0.036791Gebgew2[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)-1.021 9.012-1.1330e-01 0.9117 0.4558
Alcohol+0.1388 0.3291+4.2190e-01 0.6805 0.3403
Drugs-0.3925 0.3168-1.2390e+00 0.2391 0.1195
Geslacht+1.815 3.86+4.7020e-01 0.6466 0.3233
Gebgewicht+0.3613 5.73+6.3050e-02 0.9508 0.4754
Sport-0.1904 0.4031-4.7240e-01 0.6451 0.3226
Inter-0.5378 1.133-4.7450e-01 0.6437 0.3218
Gebgew2+0.03679 0.9061+4.0610e-02 0.9683 0.4841

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.021 &  9.012 & -1.1330e-01 &  0.9117 &  0.4558 \tabularnewline
Alcohol & +0.1388 &  0.3291 & +4.2190e-01 &  0.6805 &  0.3403 \tabularnewline
Drugs & -0.3925 &  0.3168 & -1.2390e+00 &  0.2391 &  0.1195 \tabularnewline
Geslacht & +1.815 &  3.86 & +4.7020e-01 &  0.6466 &  0.3233 \tabularnewline
Gebgewicht & +0.3613 &  5.73 & +6.3050e-02 &  0.9508 &  0.4754 \tabularnewline
Sport & -0.1904 &  0.4031 & -4.7240e-01 &  0.6451 &  0.3226 \tabularnewline
Inter & -0.5378 &  1.133 & -4.7450e-01 &  0.6437 &  0.3218 \tabularnewline
Gebgew2 & +0.03679 &  0.9061 & +4.0610e-02 &  0.9683 &  0.4841 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291695&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.021[/C][C] 9.012[/C][C]-1.1330e-01[/C][C] 0.9117[/C][C] 0.4558[/C][/ROW]
[ROW][C]Alcohol[/C][C]+0.1388[/C][C] 0.3291[/C][C]+4.2190e-01[/C][C] 0.6805[/C][C] 0.3403[/C][/ROW]
[ROW][C]Drugs[/C][C]-0.3925[/C][C] 0.3168[/C][C]-1.2390e+00[/C][C] 0.2391[/C][C] 0.1195[/C][/ROW]
[ROW][C]Geslacht[/C][C]+1.815[/C][C] 3.86[/C][C]+4.7020e-01[/C][C] 0.6466[/C][C] 0.3233[/C][/ROW]
[ROW][C]Gebgewicht[/C][C]+0.3613[/C][C] 5.73[/C][C]+6.3050e-02[/C][C] 0.9508[/C][C] 0.4754[/C][/ROW]
[ROW][C]Sport[/C][C]-0.1904[/C][C] 0.4031[/C][C]-4.7240e-01[/C][C] 0.6451[/C][C] 0.3226[/C][/ROW]
[ROW][C]Inter[/C][C]-0.5378[/C][C] 1.133[/C][C]-4.7450e-01[/C][C] 0.6437[/C][C] 0.3218[/C][/ROW]
[ROW][C]Gebgew2[/C][C]+0.03679[/C][C] 0.9061[/C][C]+4.0610e-02[/C][C] 0.9683[/C][C] 0.4841[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291695&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291695&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.021 9.012-1.1330e-01 0.9117 0.4558
Alcohol+0.1388 0.3291+4.2190e-01 0.6805 0.3403
Drugs-0.3925 0.3168-1.2390e+00 0.2391 0.1195
Geslacht+1.815 3.86+4.7020e-01 0.6466 0.3233
Gebgewicht+0.3613 5.73+6.3050e-02 0.9508 0.4754
Sport-0.1904 0.4031-4.7240e-01 0.6451 0.3226
Inter-0.5378 1.133-4.7450e-01 0.6437 0.3218
Gebgew2+0.03679 0.9061+4.0610e-02 0.9683 0.4841







Multiple Linear Regression - Regression Statistics
Multiple R 0.5315
R-squared 0.2825
Adjusted R-squared-0.136
F-TEST (value) 0.6751
F-TEST (DF numerator)7
F-TEST (DF denominator)12
p-value 0.6907
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.5468
Sum Squared Residuals 3.587

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5315 \tabularnewline
R-squared &  0.2825 \tabularnewline
Adjusted R-squared & -0.136 \tabularnewline
F-TEST (value) &  0.6751 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 12 \tabularnewline
p-value &  0.6907 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.5468 \tabularnewline
Sum Squared Residuals &  3.587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291695&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5315[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2825[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.136[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.6751[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]12[/C][/ROW]
[ROW][C]p-value[/C][C] 0.6907[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.5468[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 3.587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291695&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291695&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 0.5315
R-squared 0.2825
Adjusted R-squared-0.136
F-TEST (value) 0.6751
F-TEST (DF numerator)7
F-TEST (DF denominator)12
p-value 0.6907
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.5468
Sum Squared Residuals 3.587







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0 0.2135-0.2135
2 1 0.711 0.289
3 1 0.544 0.456
4 1 0.8333 0.1667
5 1 0.6447 0.3553
6 0-0.1696 0.1696
7 1 0.5838 0.4162
8 1 0.8333 0.1667
9 0 0.2621-0.2621
10 1 0.6329 0.3671
11 0 0.7835-0.7835
12 1 0.6945 0.3055
13 0 0.3978-0.3978
14 0 0.3005-0.3005
15 1 0.7154 0.2846
16 0 0.711-0.711
17 0 0.6354-0.6354
18 0 0.1738-0.1738
19 1 0.2197 0.7803
20 0 0.2793-0.2793

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0 &  0.2135 & -0.2135 \tabularnewline
2 &  1 &  0.711 &  0.289 \tabularnewline
3 &  1 &  0.544 &  0.456 \tabularnewline
4 &  1 &  0.8333 &  0.1667 \tabularnewline
5 &  1 &  0.6447 &  0.3553 \tabularnewline
6 &  0 & -0.1696 &  0.1696 \tabularnewline
7 &  1 &  0.5838 &  0.4162 \tabularnewline
8 &  1 &  0.8333 &  0.1667 \tabularnewline
9 &  0 &  0.2621 & -0.2621 \tabularnewline
10 &  1 &  0.6329 &  0.3671 \tabularnewline
11 &  0 &  0.7835 & -0.7835 \tabularnewline
12 &  1 &  0.6945 &  0.3055 \tabularnewline
13 &  0 &  0.3978 & -0.3978 \tabularnewline
14 &  0 &  0.3005 & -0.3005 \tabularnewline
15 &  1 &  0.7154 &  0.2846 \tabularnewline
16 &  0 &  0.711 & -0.711 \tabularnewline
17 &  0 &  0.6354 & -0.6354 \tabularnewline
18 &  0 &  0.1738 & -0.1738 \tabularnewline
19 &  1 &  0.2197 &  0.7803 \tabularnewline
20 &  0 &  0.2793 & -0.2793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291695&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] 0[/C][C] 0.2135[/C][C]-0.2135[/C][/ROW]
[ROW][C]2[/C][C] 1[/C][C] 0.711[/C][C] 0.289[/C][/ROW]
[ROW][C]3[/C][C] 1[/C][C] 0.544[/C][C] 0.456[/C][/ROW]
[ROW][C]4[/C][C] 1[/C][C] 0.8333[/C][C] 0.1667[/C][/ROW]
[ROW][C]5[/C][C] 1[/C][C] 0.6447[/C][C] 0.3553[/C][/ROW]
[ROW][C]6[/C][C] 0[/C][C]-0.1696[/C][C] 0.1696[/C][/ROW]
[ROW][C]7[/C][C] 1[/C][C] 0.5838[/C][C] 0.4162[/C][/ROW]
[ROW][C]8[/C][C] 1[/C][C] 0.8333[/C][C] 0.1667[/C][/ROW]
[ROW][C]9[/C][C] 0[/C][C] 0.2621[/C][C]-0.2621[/C][/ROW]
[ROW][C]10[/C][C] 1[/C][C] 0.6329[/C][C] 0.3671[/C][/ROW]
[ROW][C]11[/C][C] 0[/C][C] 0.7835[/C][C]-0.7835[/C][/ROW]
[ROW][C]12[/C][C] 1[/C][C] 0.6945[/C][C] 0.3055[/C][/ROW]
[ROW][C]13[/C][C] 0[/C][C] 0.3978[/C][C]-0.3978[/C][/ROW]
[ROW][C]14[/C][C] 0[/C][C] 0.3005[/C][C]-0.3005[/C][/ROW]
[ROW][C]15[/C][C] 1[/C][C] 0.7154[/C][C] 0.2846[/C][/ROW]
[ROW][C]16[/C][C] 0[/C][C] 0.711[/C][C]-0.711[/C][/ROW]
[ROW][C]17[/C][C] 0[/C][C] 0.6354[/C][C]-0.6354[/C][/ROW]
[ROW][C]18[/C][C] 0[/C][C] 0.1738[/C][C]-0.1738[/C][/ROW]
[ROW][C]19[/C][C] 1[/C][C] 0.2197[/C][C] 0.7803[/C][/ROW]
[ROW][C]20[/C][C] 0[/C][C] 0.2793[/C][C]-0.2793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291695&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291695&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 IndexActualsInterpolationForecastResidualsPrediction Error
1 0 0.2135-0.2135
2 1 0.711 0.289
3 1 0.544 0.456
4 1 0.8333 0.1667
5 1 0.6447 0.3553
6 0-0.1696 0.1696
7 1 0.5838 0.4162
8 1 0.8333 0.1667
9 0 0.2621-0.2621
10 1 0.6329 0.3671
11 0 0.7835-0.7835
12 1 0.6945 0.3055
13 0 0.3978-0.3978
14 0 0.3005-0.3005
15 1 0.7154 0.2846
16 0 0.711-0.711
17 0 0.6354-0.6354
18 0 0.1738-0.1738
19 1 0.2197 0.7803
20 0 0.2793-0.2793



Parameters (Session):
Parameters (R input):
par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}