## 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 computationSun, 11 Nov 2012 05:10:16 -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/2012/Nov/11/t13526286546zrqg9elev5qnn4.htm/, Retrieved Tue, 21 Mar 2023 08:43:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187433, Retrieved Tue, 21 Mar 2023 08:43:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-11-05 10:07:59] [63daa42bab46576bcb233b0e49169cb8]
- R       [Multiple Regression] [Verbetering berek...] [2012-11-11 10:10:16] [4c7c16453d038d093cc11140275f1ca7] [Current]
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Dataseries X:
3.57	116.96	116.70
3.85	117.40	116.90
3.49	117.52	117.15
3.65	118.25	117.53
3.66	118.97	118.04
3.37	119.01	118.44
3.18	118.99	118.81
2.81	119.15	119.03
2.26	119.00	119.04
2.32	119.21	119.09
2.86	119.47	119.21
2.76	119.52	119.30
2.79	119.87	119.52


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 17 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 & 17 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187433&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]17 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=187433&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187433&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 17 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

 Multiple Linear Regression - Estimated Regression Equation inflatie[t] = + 40.7377099127032 + 0.962899381855201gezondheidsindex[t] -1.28355435732012afgevlakte_index[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  40.7377099127032 +  0.962899381855201gezondheidsindex[t] -1.28355435732012afgevlakte_index[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187433&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  40.7377099127032 +  0.962899381855201gezondheidsindex[t] -1.28355435732012afgevlakte_index[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187433&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187433&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 inflatie[t] = + 40.7377099127032 + 0.962899381855201gezondheidsindex[t] -1.28355435732012afgevlakte_index[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 40.7377099127032 7.764026 5.247 0.000375 0.000188 gezondheidsindex 0.962899381855201 0.240593 4.0022 0.002509 0.001255 afgevlakte_index -1.28355435732012 0.219177 -5.8563 0.00016 8e-05

\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) & 40.7377099127032 & 7.764026 & 5.247 & 0.000375 & 0.000188 \tabularnewline
gezondheidsindex & 0.962899381855201 & 0.240593 & 4.0022 & 0.002509 & 0.001255 \tabularnewline
afgevlakte_index & -1.28355435732012 & 0.219177 & -5.8563 & 0.00016 & 8e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187433&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]40.7377099127032[/C][C]7.764026[/C][C]5.247[/C][C]0.000375[/C][C]0.000188[/C][/ROW]
[ROW][C]gezondheidsindex[/C][C]0.962899381855201[/C][C]0.240593[/C][C]4.0022[/C][C]0.002509[/C][C]0.001255[/C][/ROW]
[ROW][C]afgevlakte_index[/C][C]-1.28355435732012[/C][C]0.219177[/C][C]-5.8563[/C][C]0.00016[/C][C]8e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187433&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187433&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 40.7377099127032 7.764026 5.247 0.000375 0.000188 gezondheidsindex 0.962899381855201 0.240593 4.0022 0.002509 0.001255 afgevlakte_index -1.28355435732012 0.219177 -5.8563 0.00016 8e-05

 Multiple Linear Regression - Regression Statistics Multiple R 0.937381835487468 R-squared 0.878684705501854 Adjusted R-squared 0.854421646602225 F-TEST (value) 36.2149187015857 F-TEST (DF numerator) 2 F-TEST (DF denominator) 10 p-value 2.62771211948509e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.199863437626663 Sum Squared Residuals 0.399453936999472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.937381835487468 \tabularnewline
R-squared & 0.878684705501854 \tabularnewline
F-TEST (value) & 36.2149187015857 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 10 \tabularnewline
p-value & 2.62771211948509e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.199863437626663 \tabularnewline
Sum Squared Residuals & 0.399453936999472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187433&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.937381835487468[/C][/ROW]
[ROW][C]R-squared[/C][C]0.878684705501854[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.2149187015857[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]10[/C][/ROW]
[ROW][C]p-value[/C][C]2.62771211948509e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.199863437626663[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.399453936999472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187433&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187433&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 R 0.937381835487468 R-squared 0.878684705501854 Adjusted R-squared 0.854421646602225 F-TEST (value) 36.2149187015857 F-TEST (DF numerator) 2 F-TEST (DF denominator) 10 p-value 2.62771211948509e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.199863437626663 Sum Squared Residuals 0.399453936999472

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 3.57 3.56762811522933 0.00237188477066889 2 3.85 3.73459297178157 0.115407028218427 3 3.49 3.52925230827416 -0.0392523082741574 4 3.65 3.74441820124682 -0.094418201246818 5 3.66 3.78309303394929 -0.123093033949293 6 3.37 3.30818726629547 0.0618127337045307 7 3.18 2.8140141664499 0.365985833550095 8 2.81 2.68569610893632 0.124303891063678 9 2.26 2.52842565808483 -0.268425658084829 10 2.32 2.66645681040841 -0.346456810408413 11 2.86 2.76278412681237 0.0972158731876324 12 2.76 2.69540920374631 0.0645907962536902 13 2.79 2.75004202878521 0.0399579712147872

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.57 & 3.56762811522933 & 0.00237188477066889 \tabularnewline
2 & 3.85 & 3.73459297178157 & 0.115407028218427 \tabularnewline
3 & 3.49 & 3.52925230827416 & -0.0392523082741574 \tabularnewline
4 & 3.65 & 3.74441820124682 & -0.094418201246818 \tabularnewline
5 & 3.66 & 3.78309303394929 & -0.123093033949293 \tabularnewline
6 & 3.37 & 3.30818726629547 & 0.0618127337045307 \tabularnewline
7 & 3.18 & 2.8140141664499 & 0.365985833550095 \tabularnewline
8 & 2.81 & 2.68569610893632 & 0.124303891063678 \tabularnewline
9 & 2.26 & 2.52842565808483 & -0.268425658084829 \tabularnewline
10 & 2.32 & 2.66645681040841 & -0.346456810408413 \tabularnewline
11 & 2.86 & 2.76278412681237 & 0.0972158731876324 \tabularnewline
12 & 2.76 & 2.69540920374631 & 0.0645907962536902 \tabularnewline
13 & 2.79 & 2.75004202878521 & 0.0399579712147872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187433&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]3.57[/C][C]3.56762811522933[/C][C]0.00237188477066889[/C][/ROW]
[ROW][C]2[/C][C]3.85[/C][C]3.73459297178157[/C][C]0.115407028218427[/C][/ROW]
[ROW][C]3[/C][C]3.49[/C][C]3.52925230827416[/C][C]-0.0392523082741574[/C][/ROW]
[ROW][C]4[/C][C]3.65[/C][C]3.74441820124682[/C][C]-0.094418201246818[/C][/ROW]
[ROW][C]5[/C][C]3.66[/C][C]3.78309303394929[/C][C]-0.123093033949293[/C][/ROW]
[ROW][C]6[/C][C]3.37[/C][C]3.30818726629547[/C][C]0.0618127337045307[/C][/ROW]
[ROW][C]7[/C][C]3.18[/C][C]2.8140141664499[/C][C]0.365985833550095[/C][/ROW]
[ROW][C]8[/C][C]2.81[/C][C]2.68569610893632[/C][C]0.124303891063678[/C][/ROW]
[ROW][C]9[/C][C]2.26[/C][C]2.52842565808483[/C][C]-0.268425658084829[/C][/ROW]
[ROW][C]10[/C][C]2.32[/C][C]2.66645681040841[/C][C]-0.346456810408413[/C][/ROW]
[ROW][C]11[/C][C]2.86[/C][C]2.76278412681237[/C][C]0.0972158731876324[/C][/ROW]
[ROW][C]12[/C][C]2.76[/C][C]2.69540920374631[/C][C]0.0645907962536902[/C][/ROW]
[ROW][C]13[/C][C]2.79[/C][C]2.75004202878521[/C][C]0.0399579712147872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187433&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187433&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 3.57 3.56762811522933 0.00237188477066889 2 3.85 3.73459297178157 0.115407028218427 3 3.49 3.52925230827416 -0.0392523082741574 4 3.65 3.74441820124682 -0.094418201246818 5 3.66 3.78309303394929 -0.123093033949293 6 3.37 3.30818726629547 0.0618127337045307 7 3.18 2.8140141664499 0.365985833550095 8 2.81 2.68569610893632 0.124303891063678 9 2.26 2.52842565808483 -0.268425658084829 10 2.32 2.66645681040841 -0.346456810408413 11 2.86 2.76278412681237 0.0972158731876324 12 2.76 2.69540920374631 0.0645907962536902 13 2.79 2.75004202878521 0.0399579712147872

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}