WS8 - Multiple Regression

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 29 Nov 2010 21:09:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz.htm/, Retrieved Mon, 29 Nov 2010 22:11:11 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Geboortes_per_maand[t] = + 9330.58695652173 + 107.620600414074M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490709M4[t] -879.764492753623M5[t] + 75.725672877847M6[t] -309.617494824016M7[t] -141.293995859213M8[t] -196.970496894410M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.0098343685301t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 9330.58695652173 136.432397 68.3898 0 0 M1 107.620600414074 162.984839 0.6603 0.5115 0.25575 M2 -635.532091097308 162.916845 -3.901 0.000238 0.000119 M3 -287.827639751553 162.863941 -1.7673 0.082101 0.04105 M4 8.74534161490709 169.407011 0.0516 0.958995 0.479497 M5 -879.764492753623 169.297972 -5.1965 2e-06 1e-06 M6 75.725672877847 169.203414 0.4475 0.656043 0.328022 M7 -309.617494824016 169.123363 -1.8307 0.071949 0.035975 M8 -141.293995859213 169.057838 -0.8358 0.406492 0.203246 M9 -196.970496894410 169.006856 -1.1655 0.248298 0.124149 M10 367.186335403727 168.970432 2.1731 0.033604 0.016802 M11 234.50983436853 168.948573 1.3881 0.170088 0.085044 t 11.0098343685301 1.569122 7.0166 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.846483642463992 R-squared 0.716534556959107 Adjusted R-squared 0.66167027766087 F-TEST (value) 13.0601288511253 F-TEST (DF numerator) 12 F-TEST (DF denominator) 62 p-value 8.07798272717264e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 292.614891203775 Sum Squared Residuals 5308655.42236023 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9700 9449.21739130438 250.782608695625 2 9081 8717.07453416149 363.925465838511 3 9084 9075.78881987578 8.21118012422474 4 9743 9383.37163561077 359.628364389235 5 8587 8505.87163561077 81.128364389235 6 9731 9472.37163561077 258.628364389235 7 9563 9098.03830227743 464.961697722568 8 9998 9277.37163561077 720.628364389235 9 9437 9232.7049689441 204.295031055902 10 10038 9807.87163561077 230.128364389235 11 9918 9686.2049689441 231.795031055901 12 9252 9462.7049689441 -210.704968944098 13 9737 9581.3354037267 155.664596273298 14 9035 8849.19254658385 185.80745341615 15 9133 9207.90683229814 -74.9068322981358 16 9487 9515.48964803313 -28.4896480331258 17 8700 8637.98964803313 62.0103519668743 18 9627 9604.48964803313 22.5103519668743 19 8947 9230.1563146998 -283.156314699792 20 9283 9409.48964803313 -126.489648033126 21 8829 9364.82298136646 -535.822981366459 22 9947 9939.98964803313 7.01035196687427 23 9628 9818.32298136646 -190.322981366459 24 9318 9594.82298136646 -276.822981366459 25 9605 9713.45341614906 -108.453416149063 26 8640 8981.31055900621 -341.310559006211 27 9214 9340.0248447205 -126.024844720497 28 9567 9647.60766045549 -80.6076604554864 29 8547 8770.10766045549 -223.107660455487 30 9185 9736.60766045549 -551.607660455486 31 9470 9362.27432712215 107.725672877847 32 9123 9541.60766045549 -418.607660455486 33 9278 9496.94099378882 -218.940993788820 34 10170 10072.1076604555 97.8923395445137 35 9434 9950.44099378882 -516.44099378882 36 9655 9726.94099378882 -71.9409937888196 37 9429 9845.57142857142 -416.571428571424 38 8739 9113.42857142857 -374.428571428572 39 9552 9472.14285714286 79.8571428571428 40 9687 9779.72567287785 -92.725672877847 41 9019 8902.22567287785 116.774327122153 42 9672 9868.72567287785 -196.725672877847 43 9206 9494.39233954451 -288.392339544514 44 9069 9673.72567287785 -604.725672877847 45 9788 9629.05900621118 158.940993788820 46 10312 10204.2256728778 107.774327122153 47 10105 10082.5590062112 22.4409937888197 48 9863 9859.05900621118 3.94099378881966 49 9656 9977.68944099378 -321.689440993785 50 9295 9245.54658385093 49.4534161490679 51 9946 9604.26086956522 341.739130434782 52 9701 9911.8436853002 -210.843685300208 53 9049 9034.3436853002 14.6563146997924 54 10190 10000.8436853002 189.156314699792 55 9706 9626.51035196687 79.4896480331256 56 9765 9805.8436853002 -40.8436853002078 57 9893 9761.17701863354 131.822981366459 58 9994 10336.3436853002 -342.343685300208 59 10433 10214.6770186335 218.322981366459 60 10073 9991.17701863354 81.8229813664592 61 10112 10109.8074534161 2.19254658385478 62 9266 9377.6645962733 -111.664596273293 63 9820 9736.37888198758 83.6211180124216 64 10097 10043.9616977226 53.0383022774317 65 9115 9166.46169772257 -51.4616977225683 66 10411 10132.9616977226 278.038302277432 67 9678 9758.62836438923 -80.628364389235 68 10408 9937.96169772257 470.038302277432 69 10153 9893.2950310559 259.704968944098 70 10368 10468.4616977226 -100.461697722568 71 10581 10346.7950310559 234.204968944098 72 10597 10123.2950310559 473.704968944098 73 10680 10241.9254658385 438.074534161494 74 9738 9509.78260869565 228.217391304347 75 9556 9868.49689440994 -312.496894409939 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0857950324780305 0.171590064956061 0.91420496752197 17 0.0524991423628487 0.104998284725697 0.947500857637151 18 0.0231043965667466 0.0462087931334933 0.976895603433253 19 0.231495210219770 0.462990420439541 0.76850478978023 20 0.455792536295224 0.911585072590449 0.544207463704775 21 0.504624854768491 0.990750290463018 0.495375145231509 22 0.437677666203558 0.875355332407117 0.562322333796441 23 0.340351871668433 0.680703743336866 0.659648128331567 24 0.310855860227999 0.621711720455999 0.689144139772 25 0.267236336881097 0.534472673762193 0.732763663118903 26 0.206849883256790 0.413699766513581 0.79315011674321 27 0.239916252646907 0.479832505293814 0.760083747353093 28 0.205631454710160 0.411262909420319 0.79436854528984 29 0.153268958573314 0.306537917146629 0.846731041426686 30 0.173008461432075 0.346016922864150 0.826991538567925 31 0.268002998181082 0.536005996362165 0.731997001818918 32 0.261416420975539 0.522832841951078 0.738583579024461 33 0.268458916033421 0.536917832066843 0.731541083966579 34 0.342995129165929 0.685990258331858 0.657004870834071 35 0.35877367472747 0.71754734945494 0.64122632527253 36 0.431905718587352 0.863811437174704 0.568094281412648 37 0.380362945006806 0.760725890013612 0.619637054993194 38 0.329100876838706 0.658201753677411 0.670899123161294 39 0.450505003315907 0.901010006631814 0.549494996684093 40 0.403190199995985 0.80638039999197 0.596809800004015 41 0.485094127735026 0.970188255470051 0.514905872264974 42 0.454317102071593 0.908634204143186 0.545682897928407 43 0.380756271864448 0.761512543728896 0.619243728135552 44 0.606126854145476 0.787746291709047 0.393873145854524 45 0.674996769060583 0.650006461878833 0.325003230939417 46 0.752290768263355 0.49541846347329 0.247709231736645 47 0.728811920081672 0.542376159836656 0.271188079918328 48 0.704138728110715 0.59172254377857 0.295861271889285 49 0.74712682558055 0.5057463488389 0.25287317441945 50 0.699421991351506 0.601156017296988 0.300578008648494 51 0.916224561685615 0.167550876628770 0.0837754383143848 52 0.872687059430543 0.254625881138913 0.127312940569457 53 0.8375866932 0.324826613599999 0.162413306800000 54 0.791320712635809 0.417358574728382 0.208679287364191 55 0.7894838579563 0.421032284087401 0.210516142043701 56 0.775925335602774 0.448149328794452 0.224074664397226 57 0.673058641197513 0.653882717604975 0.326941358802487 58 0.53945230881435 0.921095382371301 0.460547691185650 59 0.416110442244624 0.832220884489249 0.583889557755376 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0227272727272727 OK 10% type I error level 1 0.0227272727272727 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/10n86z1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/10n86z1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/1g79o1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/1g79o1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/29h891291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/29h891291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/39h891291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/39h891291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/49h891291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/49h891291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/5k8pt1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/5k8pt1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/6k8pt1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/6k8pt1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/7choe1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/7choe1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/8choe1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/8choe1291064988.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/9n86z1291064988.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t12910650718bvimb15orgwhuz/9n86z1291064988.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$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') }

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