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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 08:04:56 -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/t1322140004j8u3fz1n428onb7.htm/, Retrieved Thu, 28 Mar 2024 10:26:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146750, Retrieved Thu, 28 Mar 2024 10:26:22 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2011-11-24 13:04:56] [6601a4463d1f95e8006e851903a6d39a] [Current]
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Dataseries X:
889	1230415	1823260
912	1264683	1749711
908	1308350	1654546
893	1350054	1572975
922	1398408	1485359
912	1431105	1419648
876	1467265	1347148
851	1498400	1288482
880	1526344	1226618
958	1553700	1165914
1058	1571355	1095738
1067	1580200	1034479
1165	1614233	989483
1109	1609551	922923
1324	1607613	884976
1407	1619055	833101
1423	1602649	768585
1442	1584092	728991
1545	1571698	698804




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146750&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146750&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146750&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] = + 6452.95403951024 -0.00247482291441174Aantal_benzinewagens[t] -0.00140175934816603Aantal_dieselwagens[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] =  +  6452.95403951024 -0.00247482291441174Aantal_benzinewagens[t] -0.00140175934816603Aantal_dieselwagens[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146750&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] =  +  6452.95403951024 -0.00247482291441174Aantal_benzinewagens[t] -0.00140175934816603Aantal_dieselwagens[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146750&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146750&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
Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] = + 6452.95403951024 -0.00247482291441174Aantal_benzinewagens[t] -0.00140175934816603Aantal_dieselwagens[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6452.95403951024686.7703959.396100
Aantal_benzinewagens-0.002474822914411740.00036-6.86664e-062e-06
Aantal_dieselwagens-0.001401759348166030.00013-10.80500

\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) & 6452.95403951024 & 686.770395 & 9.3961 & 0 & 0 \tabularnewline
Aantal_benzinewagens & -0.00247482291441174 & 0.00036 & -6.8666 & 4e-06 & 2e-06 \tabularnewline
Aantal_dieselwagens & -0.00140175934816603 & 0.00013 & -10.805 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146750&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]6452.95403951024[/C][C]686.770395[/C][C]9.3961[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Aantal_benzinewagens[/C][C]-0.00247482291441174[/C][C]0.00036[/C][C]-6.8666[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Aantal_dieselwagens[/C][C]-0.00140175934816603[/C][C]0.00013[/C][C]-10.805[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146750&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146750&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)6452.95403951024686.7703959.396100
Aantal_benzinewagens-0.002474822914411740.00036-6.86664e-062e-06
Aantal_dieselwagens-0.001401759348166030.00013-10.80500







Multiple Linear Regression - Regression Statistics
Multiple R0.965122895300035
R-squared0.931462203032322
Adjusted R-squared0.922894978411362
F-TEST (value)108.723915181761
F-TEST (DF numerator)2
F-TEST (DF denominator)16
p-value4.86901519103355e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation64.5087809139153
Sum Squared Residuals66582.1250399923

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.965122895300035 \tabularnewline
R-squared & 0.931462203032322 \tabularnewline
Adjusted R-squared & 0.922894978411362 \tabularnewline
F-TEST (value) & 108.723915181761 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 16 \tabularnewline
p-value & 4.86901519103355e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 64.5087809139153 \tabularnewline
Sum Squared Residuals & 66582.1250399923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146750&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.965122895300035[/C][/ROW]
[ROW][C]R-squared[/C][C]0.931462203032322[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.922894978411362[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]108.723915181761[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]16[/C][/ROW]
[ROW][C]p-value[/C][C]4.86901519103355e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]64.5087809139153[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]66582.1250399923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146750&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146750&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 R0.965122895300035
R-squared0.931462203032322
Adjusted R-squared0.922894978411362
F-TEST (value)108.723915181761
F-TEST (DF numerator)2
F-TEST (DF denominator)16
p-value4.86901519103355e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation64.5087809139153
Sum Squared Residuals66582.1250399923







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1889852.12305413712336.8769458628769
2912870.41382080432541.5861791956747
3908895.74415696892812.2558430310721
4893906.877053935552-13.8770539355518
5922910.02601378100111.9739862189988
6912921.217737475818-9.21773747581843
7876933.355693632727-57.355693632727
8851938.537696112026-87.5376961120257
9880956.099684906647-76.0996849066472
10958973.49082873107-15.4908287310702
1110581028.1676941940329.8323058059698
1210671092.14826142536-25.1482614253611
1311651070.9961768092694.003823190735
1411091175.88439990847-66.8843999084716
1513241233.8731687014690.1268312985422
1614071278.27251110087128.727488899129
1714231409.3103619409913.6896380590102
1814421510.73691039501-68.7369103950142
1915451583.72477503932-38.7247750393212

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 889 & 852.123054137123 & 36.8769458628769 \tabularnewline
2 & 912 & 870.413820804325 & 41.5861791956747 \tabularnewline
3 & 908 & 895.744156968928 & 12.2558430310721 \tabularnewline
4 & 893 & 906.877053935552 & -13.8770539355518 \tabularnewline
5 & 922 & 910.026013781001 & 11.9739862189988 \tabularnewline
6 & 912 & 921.217737475818 & -9.21773747581843 \tabularnewline
7 & 876 & 933.355693632727 & -57.355693632727 \tabularnewline
8 & 851 & 938.537696112026 & -87.5376961120257 \tabularnewline
9 & 880 & 956.099684906647 & -76.0996849066472 \tabularnewline
10 & 958 & 973.49082873107 & -15.4908287310702 \tabularnewline
11 & 1058 & 1028.16769419403 & 29.8323058059698 \tabularnewline
12 & 1067 & 1092.14826142536 & -25.1482614253611 \tabularnewline
13 & 1165 & 1070.99617680926 & 94.003823190735 \tabularnewline
14 & 1109 & 1175.88439990847 & -66.8843999084716 \tabularnewline
15 & 1324 & 1233.87316870146 & 90.1268312985422 \tabularnewline
16 & 1407 & 1278.27251110087 & 128.727488899129 \tabularnewline
17 & 1423 & 1409.31036194099 & 13.6896380590102 \tabularnewline
18 & 1442 & 1510.73691039501 & -68.7369103950142 \tabularnewline
19 & 1545 & 1583.72477503932 & -38.7247750393212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146750&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]889[/C][C]852.123054137123[/C][C]36.8769458628769[/C][/ROW]
[ROW][C]2[/C][C]912[/C][C]870.413820804325[/C][C]41.5861791956747[/C][/ROW]
[ROW][C]3[/C][C]908[/C][C]895.744156968928[/C][C]12.2558430310721[/C][/ROW]
[ROW][C]4[/C][C]893[/C][C]906.877053935552[/C][C]-13.8770539355518[/C][/ROW]
[ROW][C]5[/C][C]922[/C][C]910.026013781001[/C][C]11.9739862189988[/C][/ROW]
[ROW][C]6[/C][C]912[/C][C]921.217737475818[/C][C]-9.21773747581843[/C][/ROW]
[ROW][C]7[/C][C]876[/C][C]933.355693632727[/C][C]-57.355693632727[/C][/ROW]
[ROW][C]8[/C][C]851[/C][C]938.537696112026[/C][C]-87.5376961120257[/C][/ROW]
[ROW][C]9[/C][C]880[/C][C]956.099684906647[/C][C]-76.0996849066472[/C][/ROW]
[ROW][C]10[/C][C]958[/C][C]973.49082873107[/C][C]-15.4908287310702[/C][/ROW]
[ROW][C]11[/C][C]1058[/C][C]1028.16769419403[/C][C]29.8323058059698[/C][/ROW]
[ROW][C]12[/C][C]1067[/C][C]1092.14826142536[/C][C]-25.1482614253611[/C][/ROW]
[ROW][C]13[/C][C]1165[/C][C]1070.99617680926[/C][C]94.003823190735[/C][/ROW]
[ROW][C]14[/C][C]1109[/C][C]1175.88439990847[/C][C]-66.8843999084716[/C][/ROW]
[ROW][C]15[/C][C]1324[/C][C]1233.87316870146[/C][C]90.1268312985422[/C][/ROW]
[ROW][C]16[/C][C]1407[/C][C]1278.27251110087[/C][C]128.727488899129[/C][/ROW]
[ROW][C]17[/C][C]1423[/C][C]1409.31036194099[/C][C]13.6896380590102[/C][/ROW]
[ROW][C]18[/C][C]1442[/C][C]1510.73691039501[/C][C]-68.7369103950142[/C][/ROW]
[ROW][C]19[/C][C]1545[/C][C]1583.72477503932[/C][C]-38.7247750393212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146750&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146750&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
1889852.12305413712336.8769458628769
2912870.41382080432541.5861791956747
3908895.74415696892812.2558430310721
4893906.877053935552-13.8770539355518
5922910.02601378100111.9739862189988
6912921.217737475818-9.21773747581843
7876933.355693632727-57.355693632727
8851938.537696112026-87.5376961120257
9880956.099684906647-76.0996849066472
10958973.49082873107-15.4908287310702
1110581028.1676941940329.8323058059698
1210671092.14826142536-25.1482614253611
1311651070.9961768092694.003823190735
1411091175.88439990847-66.8843999084716
1513241233.8731687014690.1268312985422
1614071278.27251110087128.727488899129
1714231409.3103619409913.6896380590102
1814421510.73691039501-68.7369103950142
1915451583.72477503932-38.7247750393212



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')
}