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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 18 Nov 2012 06:40:27 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/18/t1353238907i1b7uq2xfmxhfx8.htm/, Retrieved Mon, 29 Apr 2024 19:47:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190159, Retrieved Mon, 29 Apr 2024 19:47:20 +0000

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Estimated Impact

123

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [WS 7] [2012-11-18 11:40:27] [0d750c380655c9fc6c0776885d6cbda7] [Current]

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1.34 1.98 1.97 2.62 5.05 8.02
1.34 1.97 1.98 2.62 5.04 7.98
1.34 1.98 1.98 2.61 5.02 7.98
1.34 1.98 1.98 2.61 5.03 7.97
1.34 1.98 1.98 2.60 5.01 7.96
1.33 1.97 1.98 2.59 5.00 7.95
1.33 1.97 1.98 2.59 5.00 7.94
1.33 1.97 1.97 2.59 5.00 7.91
1.33 1.97 1.97 2.58 5.00 7.90
1.33 1.96 1.97 2.58 4.97 7.90
1.33 1.96 1.97 2.58 4.97 7.88
1.33 1.96 1.97 2.57 4.96 7.88
1.32 1.95 1.97 2.56 4.93 7.86

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	'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190159&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190159&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190159&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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Brood400[t] = + 0.405066975654454 + 0.431394519710709Brood800[t] -0.0554140776204353Bruin800[t] + 0.500229194254866meergraan800[t] -0.131582968320259kramiek[t] -0.0568656412345975broodje[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Brood400[t] =  +  0.405066975654454 +  0.431394519710709Brood800[t] -0.0554140776204353Bruin800[t] +  0.500229194254866meergraan800[t] -0.131582968320259kramiek[t] -0.0568656412345975broodje[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190159&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Brood400[t] =  +  0.405066975654454 +  0.431394519710709Brood800[t] -0.0554140776204353Bruin800[t] +  0.500229194254866meergraan800[t] -0.131582968320259kramiek[t] -0.0568656412345975broodje[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190159&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190159&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
Brood400[t] = + 0.405066975654454 + 0.431394519710709Brood800[t] -0.0554140776204353Bruin800[t] + 0.500229194254866meergraan800[t] -0.131582968320259kramiek[t] -0.0568656412345975broodje[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)	0.405066975654454	0.282782	1.4324	0.195124	0.097562
Brood800	0.431394519710709	0.189322	2.2786	0.056751	0.028376
Bruin800	-0.0554140776204353	0.179595	-0.3085	0.766644	0.383322
meergraan800	0.500229194254866	0.180896	2.7653	0.027883	0.013942
kramiek	-0.131582968320259	0.097608	-1.3481	0.219626	0.109813
broodje	-0.0568656412345975	0.053805	-1.0569	0.325665	0.162832

\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) & 0.405066975654454 & 0.282782 & 1.4324 & 0.195124 & 0.097562 \tabularnewline
Brood800 & 0.431394519710709 & 0.189322 & 2.2786 & 0.056751 & 0.028376 \tabularnewline
Bruin800 & -0.0554140776204353 & 0.179595 & -0.3085 & 0.766644 & 0.383322 \tabularnewline
meergraan800 & 0.500229194254866 & 0.180896 & 2.7653 & 0.027883 & 0.013942 \tabularnewline
kramiek & -0.131582968320259 & 0.097608 & -1.3481 & 0.219626 & 0.109813 \tabularnewline
broodje & -0.0568656412345975 & 0.053805 & -1.0569 & 0.325665 & 0.162832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190159&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]0.405066975654454[/C][C]0.282782[/C][C]1.4324[/C][C]0.195124[/C][C]0.097562[/C][/ROW]
[ROW][C]Brood800[/C][C]0.431394519710709[/C][C]0.189322[/C][C]2.2786[/C][C]0.056751[/C][C]0.028376[/C][/ROW]
[ROW][C]Bruin800[/C][C]-0.0554140776204353[/C][C]0.179595[/C][C]-0.3085[/C][C]0.766644[/C][C]0.383322[/C][/ROW]
[ROW][C]meergraan800[/C][C]0.500229194254866[/C][C]0.180896[/C][C]2.7653[/C][C]0.027883[/C][C]0.013942[/C][/ROW]
[ROW][C]kramiek[/C][C]-0.131582968320259[/C][C]0.097608[/C][C]-1.3481[/C][C]0.219626[/C][C]0.109813[/C][/ROW]
[ROW][C]broodje[/C][C]-0.0568656412345975[/C][C]0.053805[/C][C]-1.0569[/C][C]0.325665[/C][C]0.162832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190159&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190159&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)	0.405066975654454	0.282782	1.4324	0.195124	0.097562
Brood800	0.431394519710709	0.189322	2.2786	0.056751	0.028376
Bruin800	-0.0554140776204353	0.179595	-0.3085	0.766644	0.383322
meergraan800	0.500229194254866	0.180896	2.7653	0.027883	0.013942
kramiek	-0.131582968320259	0.097608	-1.3481	0.219626	0.109813
broodje	-0.0568656412345975	0.053805	-1.0569	0.325665	0.162832

Multiple Linear Regression - Regression Statistics
Multiple R	0.956225922742958
R-squared	0.914368015325622
Adjusted R-squared	0.85320231198678
F-TEST (value)	14.949031326596
F-TEST (DF numerator)	5
F-TEST (DF denominator)	7
p-value	0.00128608488194948
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00241542275548296
Sum Squared Residuals	4.08398696139344e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.956225922742958 \tabularnewline
R-squared & 0.914368015325622 \tabularnewline
Adjusted R-squared & 0.85320231198678 \tabularnewline
F-TEST (value) & 14.949031326596 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 7 \tabularnewline
p-value & 0.00128608488194948 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.00241542275548296 \tabularnewline
Sum Squared Residuals & 4.08398696139344e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190159&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.956225922742958[/C][/ROW]
[ROW][C]R-squared[/C][C]0.914368015325622[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.85320231198678[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.949031326596[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]7[/C][/ROW]
[ROW][C]p-value[/C][C]0.00128608488194948[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.00241542275548296[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.08398696139344e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190159&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190159&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	0.956225922742958
R-squared	0.914368015325622
Adjusted R-squared	0.85320231198678
F-TEST (value)	14.949031326596
F-TEST (DF numerator)	5
F-TEST (DF denominator)	7
p-value	0.00128608488194948
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00241542275548296
Sum Squared Residuals	4.08398696139344e-05

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.34	1.34010644799837	-0.000106447998370173
2	1.34	1.33882881735765	0.00117118264235506
3	1.34	1.34077212997861	-0.000772129978608494
4	1.34	1.34002495670775	-2.49567077518305e-05
5	1.34	1.33822298054395	0.00177701945604552
6	1.33	1.33079122949985	-0.000791229499847144
7	1.33	1.33135988591219	-0.00135988591219311
8	1.33	1.33361999592544	-0.0036199959254354
9	1.33	1.32918636039523	0.00081363960476719
10	1.33	1.32881990424773	0.00118009575226649
11	1.33	1.32995721707243	4.27829275745145e-05
12	1.33	1.32627075481308	0.00372924518692073
13	1.32	1.32203931954772	-0.00203931954772336

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.34 & 1.34010644799837 & -0.000106447998370173 \tabularnewline
2 & 1.34 & 1.33882881735765 & 0.00117118264235506 \tabularnewline
3 & 1.34 & 1.34077212997861 & -0.000772129978608494 \tabularnewline
4 & 1.34 & 1.34002495670775 & -2.49567077518305e-05 \tabularnewline
5 & 1.34 & 1.33822298054395 & 0.00177701945604552 \tabularnewline
6 & 1.33 & 1.33079122949985 & -0.000791229499847144 \tabularnewline
7 & 1.33 & 1.33135988591219 & -0.00135988591219311 \tabularnewline
8 & 1.33 & 1.33361999592544 & -0.0036199959254354 \tabularnewline
9 & 1.33 & 1.32918636039523 & 0.00081363960476719 \tabularnewline
10 & 1.33 & 1.32881990424773 & 0.00118009575226649 \tabularnewline
11 & 1.33 & 1.32995721707243 & 4.27829275745145e-05 \tabularnewline
12 & 1.33 & 1.32627075481308 & 0.00372924518692073 \tabularnewline
13 & 1.32 & 1.32203931954772 & -0.00203931954772336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190159&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]1.34[/C][C]1.34010644799837[/C][C]-0.000106447998370173[/C][/ROW]
[ROW][C]2[/C][C]1.34[/C][C]1.33882881735765[/C][C]0.00117118264235506[/C][/ROW]
[ROW][C]3[/C][C]1.34[/C][C]1.34077212997861[/C][C]-0.000772129978608494[/C][/ROW]
[ROW][C]4[/C][C]1.34[/C][C]1.34002495670775[/C][C]-2.49567077518305e-05[/C][/ROW]
[ROW][C]5[/C][C]1.34[/C][C]1.33822298054395[/C][C]0.00177701945604552[/C][/ROW]
[ROW][C]6[/C][C]1.33[/C][C]1.33079122949985[/C][C]-0.000791229499847144[/C][/ROW]
[ROW][C]7[/C][C]1.33[/C][C]1.33135988591219[/C][C]-0.00135988591219311[/C][/ROW]
[ROW][C]8[/C][C]1.33[/C][C]1.33361999592544[/C][C]-0.0036199959254354[/C][/ROW]
[ROW][C]9[/C][C]1.33[/C][C]1.32918636039523[/C][C]0.00081363960476719[/C][/ROW]
[ROW][C]10[/C][C]1.33[/C][C]1.32881990424773[/C][C]0.00118009575226649[/C][/ROW]
[ROW][C]11[/C][C]1.33[/C][C]1.32995721707243[/C][C]4.27829275745145e-05[/C][/ROW]
[ROW][C]12[/C][C]1.33[/C][C]1.32627075481308[/C][C]0.00372924518692073[/C][/ROW]
[ROW][C]13[/C][C]1.32[/C][C]1.32203931954772[/C][C]-0.00203931954772336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190159&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190159&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	1.34	1.34010644799837	-0.000106447998370173
2	1.34	1.33882881735765	0.00117118264235506
3	1.34	1.34077212997861	-0.000772129978608494
4	1.34	1.34002495670775	-2.49567077518305e-05
5	1.34	1.33822298054395	0.00177701945604552
6	1.33	1.33079122949985	-0.000791229499847144
7	1.33	1.33135988591219	-0.00135988591219311
8	1.33	1.33361999592544	-0.0036199959254354
9	1.33	1.32918636039523	0.00081363960476719
10	1.33	1.32881990424773	0.00118009575226649
11	1.33	1.32995721707243	4.27829275745145e-05
12	1.33	1.32627075481308	0.00372924518692073
13	1.32	1.32203931954772	-0.00203931954772336

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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 test
par1 <- 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 <- x1
if (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'
}
x
k <- length(x[1,])
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code