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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 computationThu, 24 Nov 2011 09:59:42 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t13221468784rrcw9pbikuea2l.htm/, Retrieved Thu, 28 Mar 2024 10:53:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146913, Retrieved Thu, 28 Mar 2024 10:53:15 +0000
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Original text written by user:workshop 7
IsPrivate?No (this computation is public)
User-defined keywordsworkshop 7
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [workshop 7.1] [2011-11-24 14:59:42] [c96824ac395d6a5bf5e3e103e13cc1ae] [Current]
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Dataseries X:
8	17	2	6	0
3	16	0	6	-2
-3	15	0	0	-3
4	8	3	6	1
-5	5	-2	2	-2
-1	6	0	2	-1
5	5	1	2	1
0	12	-1	3	-3
-6	8	-1	-1	-4
-13	17	-1	-4	-9
-15	22	-1	4	-9
-8	24	1	5	-7
-20	36	-2	3	-14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146913&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146913&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146913&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
c[t] = -0.519620914522577 -0.0870056115138375a[t] + 0.211943793425693b[t] -0.0491629467416928d[t] + 0.720998820259653e[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
c[t] =  -0.519620914522577 -0.0870056115138375a[t] +  0.211943793425693b[t] -0.0491629467416928d[t] +  0.720998820259653e[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146913&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]c[t] =  -0.519620914522577 -0.0870056115138375a[t] +  0.211943793425693b[t] -0.0491629467416928d[t] +  0.720998820259653e[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146913&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146913&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
c[t] = -0.519620914522577 -0.0870056115138375a[t] + 0.211943793425693b[t] -0.0491629467416928d[t] + 0.720998820259653e[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.5196209145225770.671136-0.77420.4610550.230527
a-0.08700561151383750.216207-0.40240.6979150.348958
b0.2119437934256930.1560811.35790.2115480.105774
d-0.04916294674169280.181073-0.27150.7928740.396437
e0.7209988202596530.6279141.14820.2840410.142021

\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) & -0.519620914522577 & 0.671136 & -0.7742 & 0.461055 & 0.230527 \tabularnewline
a & -0.0870056115138375 & 0.216207 & -0.4024 & 0.697915 & 0.348958 \tabularnewline
b & 0.211943793425693 & 0.156081 & 1.3579 & 0.211548 & 0.105774 \tabularnewline
d & -0.0491629467416928 & 0.181073 & -0.2715 & 0.792874 & 0.396437 \tabularnewline
e & 0.720998820259653 & 0.627914 & 1.1482 & 0.284041 & 0.142021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146913&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]-0.519620914522577[/C][C]0.671136[/C][C]-0.7742[/C][C]0.461055[/C][C]0.230527[/C][/ROW]
[ROW][C]a[/C][C]-0.0870056115138375[/C][C]0.216207[/C][C]-0.4024[/C][C]0.697915[/C][C]0.348958[/C][/ROW]
[ROW][C]b[/C][C]0.211943793425693[/C][C]0.156081[/C][C]1.3579[/C][C]0.211548[/C][C]0.105774[/C][/ROW]
[ROW][C]d[/C][C]-0.0491629467416928[/C][C]0.181073[/C][C]-0.2715[/C][C]0.792874[/C][C]0.396437[/C][/ROW]
[ROW][C]e[/C][C]0.720998820259653[/C][C]0.627914[/C][C]1.1482[/C][C]0.284041[/C][C]0.142021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146913&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146913&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)-0.5196209145225770.671136-0.77420.4610550.230527
a-0.08700561151383750.216207-0.40240.6979150.348958
b0.2119437934256930.1560811.35790.2115480.105774
d-0.04916294674169280.181073-0.27150.7928740.396437
e0.7209988202596530.6279141.14820.2840410.142021







Multiple Linear Regression - Regression Statistics
Multiple R0.799726366631694
R-squared0.639562261485931
Adjusted R-squared0.459343392228897
F-TEST (value)3.54880853554666
F-TEST (DF numerator)4
F-TEST (DF denominator)8
p-value0.0600561332084104
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.10136806745033
Sum Squared Residuals9.70409295999416

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.799726366631694 \tabularnewline
R-squared & 0.639562261485931 \tabularnewline
Adjusted R-squared & 0.459343392228897 \tabularnewline
F-TEST (value) & 3.54880853554666 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 8 \tabularnewline
p-value & 0.0600561332084104 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.10136806745033 \tabularnewline
Sum Squared Residuals & 9.70409295999416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146913&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.799726366631694[/C][/ROW]
[ROW][C]R-squared[/C][C]0.639562261485931[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.459343392228897[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.54880853554666[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]8[/C][/ROW]
[ROW][C]p-value[/C][C]0.0600561332084104[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.10136806745033[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.70409295999416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146913&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146913&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 R0.799726366631694
R-squared0.639562261485931
Adjusted R-squared0.459343392228897
F-TEST (value)3.54880853554666
F-TEST (DF numerator)4
F-TEST (DF denominator)8
p-value0.0600561332084104
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.10136806745033
Sum Squared Residuals9.70409295999416







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
122.09240100115334-0.0924010011533431
200.873487624777531-0.873487624777531
300.757556360625366-0.757556360625366
431.253928126637111.74607187336289
5-2-0.565197423827618-1.43480257617238
600.0197227438023783-0.0197227438023783
710.7277429218129670.272257078187033
8-1-0.286780694418302-0.713219305581698
9-1-1.136869232330930.13686923233093
10-1-2.077841071976021.07784107197602
11-1-1.237414455753420.237414455753425
121-0.02973145572128751.02973145572129
13-2-1.39100444478111-0.608995555218888

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 2.09240100115334 & -0.0924010011533431 \tabularnewline
2 & 0 & 0.873487624777531 & -0.873487624777531 \tabularnewline
3 & 0 & 0.757556360625366 & -0.757556360625366 \tabularnewline
4 & 3 & 1.25392812663711 & 1.74607187336289 \tabularnewline
5 & -2 & -0.565197423827618 & -1.43480257617238 \tabularnewline
6 & 0 & 0.0197227438023783 & -0.0197227438023783 \tabularnewline
7 & 1 & 0.727742921812967 & 0.272257078187033 \tabularnewline
8 & -1 & -0.286780694418302 & -0.713219305581698 \tabularnewline
9 & -1 & -1.13686923233093 & 0.13686923233093 \tabularnewline
10 & -1 & -2.07784107197602 & 1.07784107197602 \tabularnewline
11 & -1 & -1.23741445575342 & 0.237414455753425 \tabularnewline
12 & 1 & -0.0297314557212875 & 1.02973145572129 \tabularnewline
13 & -2 & -1.39100444478111 & -0.608995555218888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146913&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]2[/C][C]2.09240100115334[/C][C]-0.0924010011533431[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.873487624777531[/C][C]-0.873487624777531[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.757556360625366[/C][C]-0.757556360625366[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]1.25392812663711[/C][C]1.74607187336289[/C][/ROW]
[ROW][C]5[/C][C]-2[/C][C]-0.565197423827618[/C][C]-1.43480257617238[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0197227438023783[/C][C]-0.0197227438023783[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.727742921812967[/C][C]0.272257078187033[/C][/ROW]
[ROW][C]8[/C][C]-1[/C][C]-0.286780694418302[/C][C]-0.713219305581698[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]-1.13686923233093[/C][C]0.13686923233093[/C][/ROW]
[ROW][C]10[/C][C]-1[/C][C]-2.07784107197602[/C][C]1.07784107197602[/C][/ROW]
[ROW][C]11[/C][C]-1[/C][C]-1.23741445575342[/C][C]0.237414455753425[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]-0.0297314557212875[/C][C]1.02973145572129[/C][/ROW]
[ROW][C]13[/C][C]-2[/C][C]-1.39100444478111[/C][C]-0.608995555218888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146913&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146913&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
122.09240100115334-0.0924010011533431
200.873487624777531-0.873487624777531
300.757556360625366-0.757556360625366
431.253928126637111.74607187336289
5-2-0.565197423827618-1.43480257617238
600.0197227438023783-0.0197227438023783
710.7277429218129670.272257078187033
8-1-0.286780694418302-0.713219305581698
9-1-1.136869232330930.13686923233093
10-1-2.077841071976021.07784107197602
11-1-1.237414455753420.237414455753425
121-0.02973145572128751.02973145572129
13-2-1.39100444478111-0.608995555218888



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}