Free Statistics

of Irreproducible Research!

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, 25 Nov 2010 19:59:15 +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/2010/Nov/25/t1290715073wf9ma0alm51cawj.htm/, Retrieved Fri, 29 Mar 2024 15:00:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101482, Retrieved Fri, 29 Mar 2024 15:00:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [paper - lineaire ...] [2010-11-25 17:43:15] [58c953249d32df2a8d41ff3671d3073f]
-   PD        [Multiple Regression] [paper - lineaire ...] [2010-11-25 19:59:15] [9e558c3b35e49a9a9a626d1906801b69] [Current]
-   P           [Multiple Regression] [paper minitutoria...] [2010-11-26 08:14:23] [94f495cfd7e7946e5228cbd267a6841d]
Feedback Forum

Post a new message
Dataseries X:
1161	0
1223	0
1178	0
1225	0
1087	0
1104	0
571	0
591	0
800	1
549	1
669	1
1188	1
959	1
1158	1
1100	1
894	1
1074	1
1046	1
841	1
1107	1
536	1
459	1
564	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101482&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101482&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101482&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
aantalrokers[t] = + 1017.5 -154.566666666667rookverbod[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
aantalrokers[t] =  +  1017.5 -154.566666666667rookverbod[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101482&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]aantalrokers[t] =  +  1017.5 -154.566666666667rookverbod[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101482&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101482&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
aantalrokers[t] = + 1017.5 -154.566666666667rookverbod[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1017.592.00119511.059600
rookverbod-154.566666666667113.92309-1.35680.1892630.094632

\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) & 1017.5 & 92.001195 & 11.0596 & 0 & 0 \tabularnewline
rookverbod & -154.566666666667 & 113.92309 & -1.3568 & 0.189263 & 0.094632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101482&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]1017.5[/C][C]92.001195[/C][C]11.0596[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]rookverbod[/C][C]-154.566666666667[/C][C]113.92309[/C][C]-1.3568[/C][C]0.189263[/C][C]0.094632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101482&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101482&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)1017.592.00119511.059600
rookverbod-154.566666666667113.92309-1.35680.1892630.094632







Multiple Linear Regression - Regression Statistics
Multiple R0.283888870392276
R-squared0.0805928907326025
Adjusted R-squared0.0368115998151073
F-TEST (value)1.84080663323697
F-TEST (DF numerator)1
F-TEST (DF denominator)21
p-value0.189263185264393
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.218674829765
Sum Squared Residuals1421988.93333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.283888870392276 \tabularnewline
R-squared & 0.0805928907326025 \tabularnewline
Adjusted R-squared & 0.0368115998151073 \tabularnewline
F-TEST (value) & 1.84080663323697 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 21 \tabularnewline
p-value & 0.189263185264393 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 260.218674829765 \tabularnewline
Sum Squared Residuals & 1421988.93333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101482&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.283888870392276[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0805928907326025[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0368115998151073[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.84080663323697[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]21[/C][/ROW]
[ROW][C]p-value[/C][C]0.189263185264393[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]260.218674829765[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1421988.93333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101482&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101482&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 R0.283888870392276
R-squared0.0805928907326025
Adjusted R-squared0.0368115998151073
F-TEST (value)1.84080663323697
F-TEST (DF numerator)1
F-TEST (DF denominator)21
p-value0.189263185264393
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.218674829765
Sum Squared Residuals1421988.93333333







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111611017.5143.500000000000
212231017.5205.5
311781017.5160.5
412251017.5207.5
510871017.569.5
611041017.586.5
75711017.5-446.5
85911017.5-426.5
9800862.933333333333-62.9333333333334
10549862.933333333333-313.933333333333
11669862.933333333333-193.933333333333
121188862.933333333333325.066666666667
13959862.93333333333396.0666666666666
141158862.933333333333295.066666666667
151100862.933333333333237.066666666667
16894862.93333333333331.0666666666667
171074862.933333333333211.066666666667
181046862.933333333333183.066666666667
19841862.933333333333-21.9333333333333
201107862.933333333333244.066666666667
21536862.933333333333-326.933333333333
22459862.933333333333-403.933333333333
23564862.933333333333-298.933333333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1161 & 1017.5 & 143.500000000000 \tabularnewline
2 & 1223 & 1017.5 & 205.5 \tabularnewline
3 & 1178 & 1017.5 & 160.5 \tabularnewline
4 & 1225 & 1017.5 & 207.5 \tabularnewline
5 & 1087 & 1017.5 & 69.5 \tabularnewline
6 & 1104 & 1017.5 & 86.5 \tabularnewline
7 & 571 & 1017.5 & -446.5 \tabularnewline
8 & 591 & 1017.5 & -426.5 \tabularnewline
9 & 800 & 862.933333333333 & -62.9333333333334 \tabularnewline
10 & 549 & 862.933333333333 & -313.933333333333 \tabularnewline
11 & 669 & 862.933333333333 & -193.933333333333 \tabularnewline
12 & 1188 & 862.933333333333 & 325.066666666667 \tabularnewline
13 & 959 & 862.933333333333 & 96.0666666666666 \tabularnewline
14 & 1158 & 862.933333333333 & 295.066666666667 \tabularnewline
15 & 1100 & 862.933333333333 & 237.066666666667 \tabularnewline
16 & 894 & 862.933333333333 & 31.0666666666667 \tabularnewline
17 & 1074 & 862.933333333333 & 211.066666666667 \tabularnewline
18 & 1046 & 862.933333333333 & 183.066666666667 \tabularnewline
19 & 841 & 862.933333333333 & -21.9333333333333 \tabularnewline
20 & 1107 & 862.933333333333 & 244.066666666667 \tabularnewline
21 & 536 & 862.933333333333 & -326.933333333333 \tabularnewline
22 & 459 & 862.933333333333 & -403.933333333333 \tabularnewline
23 & 564 & 862.933333333333 & -298.933333333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101482&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]1161[/C][C]1017.5[/C][C]143.500000000000[/C][/ROW]
[ROW][C]2[/C][C]1223[/C][C]1017.5[/C][C]205.5[/C][/ROW]
[ROW][C]3[/C][C]1178[/C][C]1017.5[/C][C]160.5[/C][/ROW]
[ROW][C]4[/C][C]1225[/C][C]1017.5[/C][C]207.5[/C][/ROW]
[ROW][C]5[/C][C]1087[/C][C]1017.5[/C][C]69.5[/C][/ROW]
[ROW][C]6[/C][C]1104[/C][C]1017.5[/C][C]86.5[/C][/ROW]
[ROW][C]7[/C][C]571[/C][C]1017.5[/C][C]-446.5[/C][/ROW]
[ROW][C]8[/C][C]591[/C][C]1017.5[/C][C]-426.5[/C][/ROW]
[ROW][C]9[/C][C]800[/C][C]862.933333333333[/C][C]-62.9333333333334[/C][/ROW]
[ROW][C]10[/C][C]549[/C][C]862.933333333333[/C][C]-313.933333333333[/C][/ROW]
[ROW][C]11[/C][C]669[/C][C]862.933333333333[/C][C]-193.933333333333[/C][/ROW]
[ROW][C]12[/C][C]1188[/C][C]862.933333333333[/C][C]325.066666666667[/C][/ROW]
[ROW][C]13[/C][C]959[/C][C]862.933333333333[/C][C]96.0666666666666[/C][/ROW]
[ROW][C]14[/C][C]1158[/C][C]862.933333333333[/C][C]295.066666666667[/C][/ROW]
[ROW][C]15[/C][C]1100[/C][C]862.933333333333[/C][C]237.066666666667[/C][/ROW]
[ROW][C]16[/C][C]894[/C][C]862.933333333333[/C][C]31.0666666666667[/C][/ROW]
[ROW][C]17[/C][C]1074[/C][C]862.933333333333[/C][C]211.066666666667[/C][/ROW]
[ROW][C]18[/C][C]1046[/C][C]862.933333333333[/C][C]183.066666666667[/C][/ROW]
[ROW][C]19[/C][C]841[/C][C]862.933333333333[/C][C]-21.9333333333333[/C][/ROW]
[ROW][C]20[/C][C]1107[/C][C]862.933333333333[/C][C]244.066666666667[/C][/ROW]
[ROW][C]21[/C][C]536[/C][C]862.933333333333[/C][C]-326.933333333333[/C][/ROW]
[ROW][C]22[/C][C]459[/C][C]862.933333333333[/C][C]-403.933333333333[/C][/ROW]
[ROW][C]23[/C][C]564[/C][C]862.933333333333[/C][C]-298.933333333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101482&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101482&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
111611017.5143.500000000000
212231017.5205.5
311781017.5160.5
412251017.5207.5
510871017.569.5
611041017.586.5
75711017.5-446.5
85911017.5-426.5
9800862.933333333333-62.9333333333334
10549862.933333333333-313.933333333333
11669862.933333333333-193.933333333333
121188862.933333333333325.066666666667
13959862.93333333333396.0666666666666
141158862.933333333333295.066666666667
151100862.933333333333237.066666666667
16894862.93333333333331.0666666666667
171074862.933333333333211.066666666667
181046862.933333333333183.066666666667
19841862.933333333333-21.9333333333333
201107862.933333333333244.066666666667
21536862.933333333333-326.933333333333
22459862.933333333333-403.933333333333
23564862.933333333333-298.933333333333



Parameters (Session):
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')
}