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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 computationFri, 02 Nov 2012 10:55:47 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/02/t1351868436tl3pa3aevbotmb8.htm/, Retrieved Mon, 27 Jun 2022 04:48:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185631, Retrieved Mon, 27 Jun 2022 04:48:39 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [njknjpn] [2012-11-02 14:49:40] [ab3b69a6ba62161ce820f1641d3c59e0]
- R P     [Multiple Regression] [mutiple regression] [2012-11-02 14:55:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
8500 14000 8558
8350	8500	7000
8300	9500	7400
8400	11811 7200
9000	10000 8600
8300	9500	7800
7000	9500	7500
10300 9452 9000
7150	9500	7429
8100	8600	7206
7200	11763 7613
6000	9766	7200
6750	11400	7500
9200	9500	7500
7600	11994	9071
7000	8400	7600
8288	7360	8359
8400	7400	15000




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 11 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185631&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185631&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185631&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 time11 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
A[t] = + 8215.60010190161 -0.121914808989571B[t] + 0.2369237126587C[t] -101.189041547279t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  8215.60010190161 -0.121914808989571B[t] +  0.2369237126587C[t] -101.189041547279t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185631&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  8215.60010190161 -0.121914808989571B[t] +  0.2369237126587C[t] -101.189041547279t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185631&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185631&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
A[t] = + 8215.60010190161 -0.121914808989571B[t] + 0.2369237126587C[t] -101.189041547279t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8215.600101901612058.6444483.99080.001340.00067
B-0.1219148089895710.149066-0.81790.427140.21357
C0.23692371265870.1417731.67110.1168860.058443
t-101.18904154727950.978457-1.98490.0671030.033552

\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) & 8215.60010190161 & 2058.644448 & 3.9908 & 0.00134 & 0.00067 \tabularnewline
B & -0.121914808989571 & 0.149066 & -0.8179 & 0.42714 & 0.21357 \tabularnewline
C & 0.2369237126587 & 0.141773 & 1.6711 & 0.116886 & 0.058443 \tabularnewline
t & -101.189041547279 & 50.978457 & -1.9849 & 0.067103 & 0.033552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185631&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]8215.60010190161[/C][C]2058.644448[/C][C]3.9908[/C][C]0.00134[/C][C]0.00067[/C][/ROW]
[ROW][C]B[/C][C]-0.121914808989571[/C][C]0.149066[/C][C]-0.8179[/C][C]0.42714[/C][C]0.21357[/C][/ROW]
[ROW][C]C[/C][C]0.2369237126587[/C][C]0.141773[/C][C]1.6711[/C][C]0.116886[/C][C]0.058443[/C][/ROW]
[ROW][C]t[/C][C]-101.189041547279[/C][C]50.978457[/C][C]-1.9849[/C][C]0.067103[/C][C]0.033552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185631&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185631&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)8215.600101901612058.6444483.99080.001340.00067
B-0.1219148089895710.149066-0.81790.427140.21357
C0.23692371265870.1417731.67110.1168860.058443
t-101.18904154727950.978457-1.98490.0671030.033552







Multiple Linear Regression - Regression Statistics
Multiple R0.520550812196351
R-squared0.270973148078281
Adjusted R-squared0.114753108380769
F-TEST (value)1.73456074267402
F-TEST (DF numerator)3
F-TEST (DF denominator)14
p-value0.205786884736227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation962.190885202705
Sum Squared Residuals12961358.1939403

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.520550812196351 \tabularnewline
R-squared & 0.270973148078281 \tabularnewline
Adjusted R-squared & 0.114753108380769 \tabularnewline
F-TEST (value) & 1.73456074267402 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 14 \tabularnewline
p-value & 0.205786884736227 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 962.190885202705 \tabularnewline
Sum Squared Residuals & 12961358.1939403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185631&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.520550812196351[/C][/ROW]
[ROW][C]R-squared[/C][C]0.270973148078281[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.114753108380769[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.73456074267402[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]14[/C][/ROW]
[ROW][C]p-value[/C][C]0.205786884736227[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]962.190885202705[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12961358.1939403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185631&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185631&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 R0.520550812196351
R-squared0.270973148078281
Adjusted R-squared0.114753108380769
F-TEST (value)1.73456074267402
F-TEST (DF numerator)3
F-TEST (DF denominator)14
p-value0.205786884736227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation962.190885202705
Sum Squared Residuals12961358.1939403







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
185008435.1968674334964.8031325665114
283508635.4121310066-285.412131006599
383008507.07776553323-207.077765533229
484008076.75885787931323.24114212069
590008528.05073313433471.949266865676
683008298.280125954871.71987404512914
770008126.01397060998-1126.01397060998
8103008386.062408882251913.93759111775
971507906.81430391666-756.814303916655
1081007862.5146025371237.4853974629
1172007472.1369712079-272.136971207898
1260007516.56230988475-1516.56230988475
1367507287.24158424612-537.241584246121
1492007417.690679779031782.30932022097
1576007384.65325719857215.346742801426
1670007373.11125783887-373.111257838867
1782887578.53871554869709.461284451305
1884009045.88345740826-645.883457408258

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8500 & 8435.19686743349 & 64.8031325665114 \tabularnewline
2 & 8350 & 8635.4121310066 & -285.412131006599 \tabularnewline
3 & 8300 & 8507.07776553323 & -207.077765533229 \tabularnewline
4 & 8400 & 8076.75885787931 & 323.24114212069 \tabularnewline
5 & 9000 & 8528.05073313433 & 471.949266865676 \tabularnewline
6 & 8300 & 8298.28012595487 & 1.71987404512914 \tabularnewline
7 & 7000 & 8126.01397060998 & -1126.01397060998 \tabularnewline
8 & 10300 & 8386.06240888225 & 1913.93759111775 \tabularnewline
9 & 7150 & 7906.81430391666 & -756.814303916655 \tabularnewline
10 & 8100 & 7862.5146025371 & 237.4853974629 \tabularnewline
11 & 7200 & 7472.1369712079 & -272.136971207898 \tabularnewline
12 & 6000 & 7516.56230988475 & -1516.56230988475 \tabularnewline
13 & 6750 & 7287.24158424612 & -537.241584246121 \tabularnewline
14 & 9200 & 7417.69067977903 & 1782.30932022097 \tabularnewline
15 & 7600 & 7384.65325719857 & 215.346742801426 \tabularnewline
16 & 7000 & 7373.11125783887 & -373.111257838867 \tabularnewline
17 & 8288 & 7578.53871554869 & 709.461284451305 \tabularnewline
18 & 8400 & 9045.88345740826 & -645.883457408258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185631&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]8500[/C][C]8435.19686743349[/C][C]64.8031325665114[/C][/ROW]
[ROW][C]2[/C][C]8350[/C][C]8635.4121310066[/C][C]-285.412131006599[/C][/ROW]
[ROW][C]3[/C][C]8300[/C][C]8507.07776553323[/C][C]-207.077765533229[/C][/ROW]
[ROW][C]4[/C][C]8400[/C][C]8076.75885787931[/C][C]323.24114212069[/C][/ROW]
[ROW][C]5[/C][C]9000[/C][C]8528.05073313433[/C][C]471.949266865676[/C][/ROW]
[ROW][C]6[/C][C]8300[/C][C]8298.28012595487[/C][C]1.71987404512914[/C][/ROW]
[ROW][C]7[/C][C]7000[/C][C]8126.01397060998[/C][C]-1126.01397060998[/C][/ROW]
[ROW][C]8[/C][C]10300[/C][C]8386.06240888225[/C][C]1913.93759111775[/C][/ROW]
[ROW][C]9[/C][C]7150[/C][C]7906.81430391666[/C][C]-756.814303916655[/C][/ROW]
[ROW][C]10[/C][C]8100[/C][C]7862.5146025371[/C][C]237.4853974629[/C][/ROW]
[ROW][C]11[/C][C]7200[/C][C]7472.1369712079[/C][C]-272.136971207898[/C][/ROW]
[ROW][C]12[/C][C]6000[/C][C]7516.56230988475[/C][C]-1516.56230988475[/C][/ROW]
[ROW][C]13[/C][C]6750[/C][C]7287.24158424612[/C][C]-537.241584246121[/C][/ROW]
[ROW][C]14[/C][C]9200[/C][C]7417.69067977903[/C][C]1782.30932022097[/C][/ROW]
[ROW][C]15[/C][C]7600[/C][C]7384.65325719857[/C][C]215.346742801426[/C][/ROW]
[ROW][C]16[/C][C]7000[/C][C]7373.11125783887[/C][C]-373.111257838867[/C][/ROW]
[ROW][C]17[/C][C]8288[/C][C]7578.53871554869[/C][C]709.461284451305[/C][/ROW]
[ROW][C]18[/C][C]8400[/C][C]9045.88345740826[/C][C]-645.883457408258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185631&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185631&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
185008435.1968674334964.8031325665114
283508635.4121310066-285.412131006599
383008507.07776553323-207.077765533229
484008076.75885787931323.24114212069
590008528.05073313433471.949266865676
683008298.280125954871.71987404512914
770008126.01397060998-1126.01397060998
8103008386.062408882251913.93759111775
971507906.81430391666-756.814303916655
1081007862.5146025371237.4853974629
1172007472.1369712079-272.136971207898
1260007516.56230988475-1516.56230988475
1367507287.24158424612-537.241584246121
1492007417.690679779031782.30932022097
1576007384.65325719857215.346742801426
1670007373.11125783887-373.111257838867
1782887578.53871554869709.461284451305
1884009045.88345740826-645.883457408258



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}