*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: Fri, 20 Nov 2009 11:20:43 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh.htm/, Retrieved Fri, 20 Nov 2009 19:21:31 +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/2009/Nov/20/t1258741279lrjiepk5ury32jh.htm/}, year = {2009}, } @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 = {2009}, 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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1852 18.2 2187 1855 2218 2253 1570 18 1852 2187 1855 2218 1851 19 1570 1852 2187 1855 1954 20.7 1851 1570 1852 2187 1828 21.2 1954 1851 1570 1852 2251 20.7 1828 1954 1851 1570 2277 19.6 2251 1828 1954 1851 2085 18.6 2277 2251 1828 1954 2282 18.7 2085 2277 2251 1828 2266 23.8 2282 2085 2277 2251 1878 24.9 2266 2282 2085 2277 2267 24.8 1878 2266 2282 2085 2069 23.8 2267 1878 2266 2282 1746 22.3 2069 2267 1878 2266 2299 21.7 1746 2069 2267 1878 2360 20.7 2299 1746 2069 2267 2214 19.7 2360 2299 1746 2069 2825 18.4 2214 2360 2299 1746 2355 17.4 2825 2214 2360 2299 2333 17 2355 2825 2214 2360 3016 18 2333 2355 2825 2214 2155 23.8 3016 2333 2355 2825 2172 25.5 2155 3016 2333 2355 2150 25.6 2172 2155 3016 2333 2533 23.7 2150 2172 2155 3016 2058 22 2533 2150 2172 2155 2160 21.3 2058 2533 2150 2172 2260 20.7 2160 2058 2533 2150 2498 20.4 2260 2160 2058 2533 2695 20.3 2498 2260 2160 2058 2799 20.4 2695 2498 2260 2160 2946 19.8 2799 2695 2498 2260 2930 19.5 2946 2799 2695 2498 2318 23.1 2 etc...

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -521.125044123331 + 43.9247300401469X[t] + 0.267607684713609Y1[t] + 0.353647238620865Y2[t] + 0.0424752602074008Y3[t] + 0.087865010872984Y4[t] + 127.551804826609M1[t] -192.613937113397M2[t] + 225.212618383627M3[t] + 443.735103798471M4[t] + 133.326151066841M5[t] + 512.431188452368M6[t] + 188.750160570144M7[t] + 228.369322244913M8[t] + 514.605578151214M9[t] -216.103454805878M10[t] -264.768073178073M11[t] + 1.7513226111489t + 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) -521.125044123331 784.363785 -0.6644 0.510349 0.255174 X 43.9247300401469 28.009469 1.5682 0.124911 0.062456 Y1 0.267607684713609 0.157588 1.6982 0.097446 0.048723 Y2 0.353647238620865 0.165713 2.1341 0.039178 0.019589 Y3 0.0424752602074008 0.166763 0.2547 0.80029 0.400145 Y4 0.087865010872984 0.161445 0.5442 0.589374 0.294687 M1 127.551804826609 191.769338 0.6651 0.509881 0.25494 M2 -192.613937113397 216.309642 -0.8905 0.378682 0.189341 M3 225.212618383627 206.89073 1.0886 0.28303 0.141515 M4 443.735103798471 212.273282 2.0904 0.043149 0.021575 M5 133.326151066841 252.330248 0.5284 0.60023 0.300115 M6 512.431188452368 246.67126 2.0774 0.044398 0.022199 M7 188.750160570144 241.791598 0.7806 0.439732 0.219866 M8 228.369322244913 254.534876 0.8972 0.375116 0.187558 M9 514.605578151214 236.685299 2.1742 0.03582 0.01791 M10 -216.103454805878 201.719321 -1.0713 0.290616 0.145308 M11 -264.768073178073 185.609993 -1.4265 0.161688 0.080844 t 1.7513226111489 2.688302 0.6515 0.51857 0.259285 Multiple Linear Regression - Regression Statistics Multiple R 0.829733555062557 R-squared 0.688457772396749 Adjusted R-squared 0.552657314210717 F-TEST (value) 5.06962775820413 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 1.38179662922955e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 236.799134179636 Sum Squared Residuals 2186879.36798078 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1852 1941.05180079232 -89.0518007923169 2 1570 1623.12094946266 -53.1209494626560 3 1851 1874.89315302571 -23.8931530257124 4 1954 2160.25021167375 -206.250211673746 5 1828 1959.08061013037 -131.080610130366 6 2251 2307.8393174631 -56.8393174631012 7 2277 2035.29592757216 241.704072427837 8 2085 2192.9904768909 -107.990476890899 9 2282 2450.08072484923 -168.080724849227 10 2266 1968.22883814607 297.771161853927 11 1878 2029.14876880496 -151.148768804957 12 2267 2173.28309827660 93.7169017234037 13 2069 2242.17555942156 -173.175559421565 14 1746 1924.57025814827 -178.57025814827 15 2299 2143.76511482489 155.234885175112 16 2360 2379.64257209135 -19.6425720913465 17 2214 2207.83442245575 6.16557754425473 18 2825 2509.19881337064 315.801186629365 19 2355 2306.40051846619 48.5994815338065 20 2333 2419.66233939098 -86.6623393909767 21 3016 2592.59716913233 423.402830867667 22 2155 2327.12085177489 -172.120851774893 23 2172 2323.77943368687 -151.779433686865 24 2150 2321.82793315012 -171.827933150117 25 2533 2391.23931089213 141.760689107873 26 2058 2017.93665955256 40.0633404474396 27 2160 2415.65971824574 -255.659718245736 28 2260 2483.22722816375 -223.227228163747 29 2498 2237.70151640775 260.298483592253 30 2695 2675.81735260082 19.1826473991786 31 2799 2508.37663414389 290.623365856107 32 2946 2639.78759864091 306.212401359092 33 2930 3019.99489946441 -89.9948994644116 34 2318 2518.59747258839 -200.597472588395 35 2540 2335.20163436202 204.798365637976 36 2570 2456.93437855671 113.065621443293 37 2669 2619.01988606472 49.9801139352799 38 2450 2249.43943575277 200.560564247234 39 2842 2648.63270573609 193.367294263910 40 3440 2859.27625195842 580.723748041577 41 2678 2835.49686991502 -157.496869915020 42 2981 3186.18530344320 -205.185303443204 43 2260 2726.91987466946 -466.919874669459 44 2844 2685.10756778693 158.892432213067 45 2546 2820.31524068911 -274.315240689115 46 2456 2381.05283749064 74.947162509361 47 2295 2196.87016314615 98.1298368538463 48 2379 2413.95459001658 -34.9545900165796 49 2479 2408.51344282927 70.486557170729 50 2057 2065.93269708375 -8.93269708374724 51 2280 2349.04930816757 -69.0493081675735 52 2351 2482.60373611274 -131.603736112738 53 2276 2253.88658109112 22.1134189088789 54 2548 2620.95921312224 -72.9592131222383 55 2311 2425.00704514829 -114.007045148292 56 2201 2471.45201729028 -270.452017290284 57 2725 2616.01196586491 108.988034135087 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0348206650068658 0.0696413300137317 0.965179334993134 22 0.0157730294774870 0.0315460589549739 0.984226970522513 23 0.00866032903329128 0.0173206580665826 0.991339670966709 24 0.0618712431454521 0.123742486290904 0.938128756854548 25 0.0660615882920617 0.132123176584123 0.933938411707938 26 0.0839843593911774 0.167968718782355 0.916015640608823 27 0.161759277948584 0.323518555897168 0.838240722051416 28 0.483453699831766 0.966907399663533 0.516546300168234 29 0.381526812375684 0.763053624751368 0.618473187624316 30 0.367802762265659 0.735605524531318 0.632197237734341 31 0.266823536731103 0.533647073462206 0.733176463268897 32 0.423011514030045 0.84602302806009 0.576988485969955 33 0.304326357066636 0.608652714133272 0.695673642933364 34 0.221128255572386 0.442256511144772 0.778871744427614 35 0.129905005527964 0.259810011055928 0.870094994472036 36 0.0826144141416389 0.165228828283278 0.917385585858361 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 2 0.125 NOK 10% type I error level 3 0.1875 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/103j4r1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/103j4r1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/1ac0s1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/1ac0s1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/20slx1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/20slx1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/316m01258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/316m01258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/4mdct1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/4mdct1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/5xhyx1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/5xhyx1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/6cd0i1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/6cd0i1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/7erh51258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/7erh51258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/84nsv1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/84nsv1258741239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/9unem1258741239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741279lrjiepk5ury32jh/9unem1258741239.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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