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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 10:57:16 -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/t1322151980v0nys3p46jxc1el.htm/, Retrieved Fri, 29 Mar 2024 11:23:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147046, Retrieved Fri, 29 Mar 2024 11:23:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
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]
-   PD    [Multiple Regression] [WS7] [2011-11-24 15:57:16] [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 time5 seconds
R Server'AstonUniversity' @ aston.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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147046&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147046&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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] = -1830.86113589446 -0.000540920001505225Aantal_benzinewagens[t] + 0.001780222065552Aantal_dieselwagens[t] + 159.41617710106t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] =  -1830.86113589446 -0.000540920001505225Aantal_benzinewagens[t] +  0.001780222065552Aantal_dieselwagens[t] +  159.41617710106t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147046&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Aantal_dode_en_zwaargewonde_fietsslachtoffers[t] =  -1830.86113589446 -0.000540920001505225Aantal_benzinewagens[t] +  0.001780222065552Aantal_dieselwagens[t] +  159.41617710106t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147046&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] = -1830.86113589446 -0.000540920001505225Aantal_benzinewagens[t] + 0.001780222065552Aantal_dieselwagens[t] + 159.41617710106t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1830.861135894462949.59585-0.62070.5441030.272052
Aantal_benzinewagens-0.0005409200015052250.000739-0.7320.4754430.237722
Aantal_dieselwagens0.0017802220655520.0011171.5940.1317840.065892
t159.4161771010655.6913762.86250.0118630.005931

\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) & -1830.86113589446 & 2949.59585 & -0.6207 & 0.544103 & 0.272052 \tabularnewline
Aantal_benzinewagens & -0.000540920001505225 & 0.000739 & -0.732 & 0.475443 & 0.237722 \tabularnewline
Aantal_dieselwagens & 0.001780222065552 & 0.001117 & 1.594 & 0.131784 & 0.065892 \tabularnewline
t & 159.41617710106 & 55.691376 & 2.8625 & 0.011863 & 0.005931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147046&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]-1830.86113589446[/C][C]2949.59585[/C][C]-0.6207[/C][C]0.544103[/C][C]0.272052[/C][/ROW]
[ROW][C]Aantal_benzinewagens[/C][C]-0.000540920001505225[/C][C]0.000739[/C][C]-0.732[/C][C]0.475443[/C][C]0.237722[/C][/ROW]
[ROW][C]Aantal_dieselwagens[/C][C]0.001780222065552[/C][C]0.001117[/C][C]1.594[/C][C]0.131784[/C][C]0.065892[/C][/ROW]
[ROW][C]t[/C][C]159.41617710106[/C][C]55.691376[/C][C]2.8625[/C][C]0.011863[/C][C]0.005931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147046&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147046&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)-1830.861135894462949.59585-0.62070.5441030.272052
Aantal_benzinewagens-0.0005409200015052250.000739-0.7320.4754430.237722
Aantal_dieselwagens0.0017802220655520.0011171.5940.1317840.065892
t159.4161771010655.6913762.86250.0118630.005931







Multiple Linear Regression - Regression Statistics
Multiple R0.977586339243958
R-squared0.955675050676403
Adjusted R-squared0.946810060811684
F-TEST (value)107.803287455495
F-TEST (DF numerator)3
F-TEST (DF denominator)15
p-value2.24894547429244e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation53.5787096323589
Sum Squared Residuals43060.1718880294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.977586339243958 \tabularnewline
R-squared & 0.955675050676403 \tabularnewline
Adjusted R-squared & 0.946810060811684 \tabularnewline
F-TEST (value) & 107.803287455495 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 15 \tabularnewline
p-value & 2.24894547429244e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 53.5787096323589 \tabularnewline
Sum Squared Residuals & 43060.1718880294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147046&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.977586339243958[/C][/ROW]
[ROW][C]R-squared[/C][C]0.955675050676403[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.946810060811684[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]107.803287455495[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]15[/C][/ROW]
[ROW][C]p-value[/C][C]2.24894547429244e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]53.5787096323589[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]43060.1718880294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147046&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147046&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 R0.977586339243958
R-squared0.955675050676403
Adjusted R-squared0.946810060811684
F-TEST (value)107.803287455495
F-TEST (DF numerator)3
F-TEST (DF denominator)15
p-value2.24894547429244e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation53.5787096323589
Sum Squared Residuals43060.1718880294







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1889908.806640792882-19.8066407928817
2912918.753018583078-6.75301858307786
3908885.13400911015422.8659908898461
4893876.77716435929816.2228356407019
5922854.0617592121767.938240787829
6912878.81130287452833.1886971254723
7876889.601712968639-13.6017129686392
8851927.73783812516-76.7378381251609
9880961.90688884085-81.9068888408504
10958998.459058113465-40.4590581134653
1110581023.3964289157734.6035710842262
1210671068.97354508987-1.97354508987053
1311651129.8777197181335.1222802818742
1411091173.33490358309-64.3349035830926
1513241266.2452969255757.7547030744317
1614071327.123247718979.876752281104
1714231380.560951583542.4390484165017
1814421479.52886868903-37.5288686890253
1915451591.90964479592-46.9096447959233

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 889 & 908.806640792882 & -19.8066407928817 \tabularnewline
2 & 912 & 918.753018583078 & -6.75301858307786 \tabularnewline
3 & 908 & 885.134009110154 & 22.8659908898461 \tabularnewline
4 & 893 & 876.777164359298 & 16.2228356407019 \tabularnewline
5 & 922 & 854.06175921217 & 67.938240787829 \tabularnewline
6 & 912 & 878.811302874528 & 33.1886971254723 \tabularnewline
7 & 876 & 889.601712968639 & -13.6017129686392 \tabularnewline
8 & 851 & 927.73783812516 & -76.7378381251609 \tabularnewline
9 & 880 & 961.90688884085 & -81.9068888408504 \tabularnewline
10 & 958 & 998.459058113465 & -40.4590581134653 \tabularnewline
11 & 1058 & 1023.39642891577 & 34.6035710842262 \tabularnewline
12 & 1067 & 1068.97354508987 & -1.97354508987053 \tabularnewline
13 & 1165 & 1129.87771971813 & 35.1222802818742 \tabularnewline
14 & 1109 & 1173.33490358309 & -64.3349035830926 \tabularnewline
15 & 1324 & 1266.24529692557 & 57.7547030744317 \tabularnewline
16 & 1407 & 1327.1232477189 & 79.876752281104 \tabularnewline
17 & 1423 & 1380.5609515835 & 42.4390484165017 \tabularnewline
18 & 1442 & 1479.52886868903 & -37.5288686890253 \tabularnewline
19 & 1545 & 1591.90964479592 & -46.9096447959233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147046&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]908.806640792882[/C][C]-19.8066407928817[/C][/ROW]
[ROW][C]2[/C][C]912[/C][C]918.753018583078[/C][C]-6.75301858307786[/C][/ROW]
[ROW][C]3[/C][C]908[/C][C]885.134009110154[/C][C]22.8659908898461[/C][/ROW]
[ROW][C]4[/C][C]893[/C][C]876.777164359298[/C][C]16.2228356407019[/C][/ROW]
[ROW][C]5[/C][C]922[/C][C]854.06175921217[/C][C]67.938240787829[/C][/ROW]
[ROW][C]6[/C][C]912[/C][C]878.811302874528[/C][C]33.1886971254723[/C][/ROW]
[ROW][C]7[/C][C]876[/C][C]889.601712968639[/C][C]-13.6017129686392[/C][/ROW]
[ROW][C]8[/C][C]851[/C][C]927.73783812516[/C][C]-76.7378381251609[/C][/ROW]
[ROW][C]9[/C][C]880[/C][C]961.90688884085[/C][C]-81.9068888408504[/C][/ROW]
[ROW][C]10[/C][C]958[/C][C]998.459058113465[/C][C]-40.4590581134653[/C][/ROW]
[ROW][C]11[/C][C]1058[/C][C]1023.39642891577[/C][C]34.6035710842262[/C][/ROW]
[ROW][C]12[/C][C]1067[/C][C]1068.97354508987[/C][C]-1.97354508987053[/C][/ROW]
[ROW][C]13[/C][C]1165[/C][C]1129.87771971813[/C][C]35.1222802818742[/C][/ROW]
[ROW][C]14[/C][C]1109[/C][C]1173.33490358309[/C][C]-64.3349035830926[/C][/ROW]
[ROW][C]15[/C][C]1324[/C][C]1266.24529692557[/C][C]57.7547030744317[/C][/ROW]
[ROW][C]16[/C][C]1407[/C][C]1327.1232477189[/C][C]79.876752281104[/C][/ROW]
[ROW][C]17[/C][C]1423[/C][C]1380.5609515835[/C][C]42.4390484165017[/C][/ROW]
[ROW][C]18[/C][C]1442[/C][C]1479.52886868903[/C][C]-37.5288686890253[/C][/ROW]
[ROW][C]19[/C][C]1545[/C][C]1591.90964479592[/C][C]-46.9096447959233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147046&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147046&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
1889908.806640792882-19.8066407928817
2912918.753018583078-6.75301858307786
3908885.13400911015422.8659908898461
4893876.77716435929816.2228356407019
5922854.0617592121767.938240787829
6912878.81130287452833.1886971254723
7876889.601712968639-13.6017129686392
8851927.73783812516-76.7378381251609
9880961.90688884085-81.9068888408504
10958998.459058113465-40.4590581134653
1110581023.3964289157734.6035710842262
1210671068.97354508987-1.97354508987053
1311651129.8777197181335.1222802818742
1411091173.33490358309-64.3349035830926
1513241266.2452969255757.7547030744317
1614071327.123247718979.876752281104
1714231380.560951583542.4390484165017
1814421479.52886868903-37.5288686890253
1915451591.90964479592-46.9096447959233



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 = 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')
}