Multiple Linear Regression Model 1

*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 11:57:28 -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/t1258743777qybx3u440g518k3.htm/, Retrieved Fri, 20 Nov 2009 20:03:10 +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/t1258743777qybx3u440g518k3.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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9.3 104.1 8.7 90.2 8.2 99.2 8.3 116.5 8.5 98.4 8.6 90.6 8.5 130.5 8.2 107.4 8.1 106 7.9 196.5 8.6 107.8 8.7 90.5 8.7 123.8 8.5 114.7 8.4 115.3 8.5 197 8.7 88.4 8.7 93.8 8.6 111.3 8.5 105.9 8.3 123.6 8 171 8.2 97 8.1 99.2 8.1 126.6 8 103.4 7.9 121.3 7.9 129.6 8 110.8 8 98.9 7.9 122.8 8 120.9 7.7 133.1 7.2 203.1 7.5 110.2 7.3 119.5 7 135.1 7 113.9 7 137.4 7.2 157.1 7.3 126.4 7.1 112.2 6.8 128.8 6.4 136.8 6.1 156.5 6.5 215.2 7.7 146.7 7.9 130.8 7.5 133.1 6.9 153.4 6.6 159.9 6.9 174.6 7.7 145 8 112.9 8 137.8 7.7 150.6 7.3 162.1 7.4 226.4 8.1 112.3 8.3 126.3

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 324.706517146108 -24.8824963432473X[t] + 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) 324.706517146108 40.248805 8.0675 0 0 X -24.8824963432473 5.111377 -4.8681 9e-06 5e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.538580087796469 R-squared 0.290068510970852 Adjusted R-squared 0.277828312884143 F-TEST (value) 23.6980242407851 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 9.03944554009328e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 26.8959936560185 Sum Squared Residuals 41956.8795351861 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.1 93.2993011539099 10.8006988460901 2 90.2 108.228798959857 -18.0287989598568 3 99.2 120.670047131481 -21.4700471314805 4 116.5 118.181797497156 -1.68179749715578 5 98.4 113.205298228506 -14.8052982285063 6 90.6 110.717048594182 -20.1170485941816 7 130.5 113.205298228506 17.2947017714937 8 107.4 120.670047131481 -13.2700471314805 9 106 123.158296765805 -17.1582967658053 10 196.5 128.134796034455 68.3652039655453 11 107.8 110.717048594182 -2.91704859418163 12 90.5 108.228798959857 -17.7287989598569 13 123.8 108.228798959857 15.5712010401431 14 114.7 113.205298228506 1.49470177149366 15 115.3 115.693547862831 -0.393547862831066 16 197 113.205298228506 83.7947017714937 17 88.4 108.228798959857 -19.8287989598569 18 93.8 108.228798959857 -14.4287989598569 19 111.3 110.717048594182 0.58295140581837 20 105.9 113.205298228506 -7.30529822850634 21 123.6 118.181797497156 5.41820250284421 22 171 125.64654640013 45.35345359987 23 97 120.670047131481 -23.6700471314805 24 99.2 123.158296765805 -23.9582967658053 25 126.6 123.158296765805 3.44170323419473 26 103.4 125.64654640013 -22.2465464001300 27 121.3 128.134796034455 -6.8347960344547 28 129.6 128.134796034455 1.46520396554529 29 110.8 125.64654640013 -14.8465464001300 30 98.9 125.64654640013 -26.74654640013 31 122.8 128.134796034455 -5.3347960344547 32 120.9 125.64654640013 -4.74654640012998 33 133.1 133.111295303104 -0.0112953031041694 34 203.1 145.552543474728 57.5474565252722 35 110.2 138.087794571754 -27.8877945717536 36 119.5 143.064293840403 -23.5642938404031 37 135.1 150.529042743377 -15.4290427433773 38 113.9 150.529042743377 -36.6290427433773 39 137.4 150.529042743377 -13.1290427433773 40 157.1 145.552543474728 11.5474565252722 41 126.4 143.064293840403 -16.6642938404031 42 112.2 148.040793109053 -35.8407931090525 43 128.8 155.505542012027 -26.7055420120267 44 136.8 165.458540549326 -28.6585405493256 45 156.5 172.923289452300 -16.4232894522998 46 215.2 162.970290915001 52.2297090849991 47 146.7 133.111295303104 13.5887046968958 48 130.8 128.134796034455 2.66520396554531 49 133.1 138.087794571754 -4.98779457175363 50 153.4 153.017292377702 0.382707622298027 51 159.9 160.482041280676 -0.582041280676173 52 174.6 153.017292377702 21.582707622298 53 145 133.111295303104 11.8887046968958 54 112.9 125.64654640013 -12.7465464001300 55 137.8 125.64654640013 12.1534535998700 56 150.6 133.111295303104 17.4887046968958 57 162.1 143.064293840403 19.0357061595969 58 226.4 140.576044206078 85.8239557939217 59 112.3 123.158296765805 -10.8582967658053 60 126.3 118.181797497156 8.11820250284421 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0761457608517317 0.152291521703463 0.923854239148268 6 0.0382458663423905 0.076491732684781 0.96175413365761 7 0.0998760539726721 0.199752107945344 0.900123946027328 8 0.0476177957545426 0.0952355915090851 0.952382204245457 9 0.0215112707080981 0.0430225414161961 0.978488729291902 10 0.645484078209412 0.709031843581175 0.354515921790588 11 0.541315700162692 0.917368599674617 0.458684299837308 12 0.462534786550147 0.925069573100295 0.537465213449853 13 0.415915845247842 0.831831690495685 0.584084154752158 14 0.324477403025408 0.648954806050815 0.675522596974592 15 0.243749535405579 0.487499070811159 0.75625046459442 16 0.862910162745044 0.274179674509911 0.137089837254956 17 0.835337068902393 0.329325862195214 0.164662931097607 18 0.791164711253773 0.417670577492453 0.208835288746227 19 0.726805516540008 0.546388966919984 0.273194483459992 20 0.66117295253037 0.67765409493926 0.33882704746963 21 0.585616985724484 0.828766028551033 0.414383014275516 22 0.658823108485679 0.682353783028642 0.341176891514321 23 0.665863518161085 0.668272963677829 0.334136481838915 24 0.67142025258207 0.657159494835861 0.328579747417930 25 0.599668878454249 0.800662243091502 0.400331121545751 26 0.590152175047225 0.81969564990555 0.409847824952775 27 0.523756129905387 0.952487740189225 0.476243870094613 28 0.447427957911483 0.894855915822966 0.552572042088517 29 0.399622855987817 0.799245711975634 0.600377144012183 30 0.40438372499743 0.80876744999486 0.59561627500257 31 0.33720306257487 0.67440612514974 0.66279693742513 32 0.275034966179203 0.550069932358405 0.724965033820797 33 0.2154725320634 0.4309450641268 0.7845274679366 34 0.405208673772772 0.810417347545544 0.594791326227228 35 0.436297819351078 0.872595638702156 0.563702180648922 36 0.429999914048146 0.859999828096291 0.570000085951854 37 0.379256486324301 0.758512972648603 0.620743513675698 38 0.434286888904927 0.868573777809854 0.565713111095073 39 0.374458089492761 0.748916178985522 0.625541910507239 40 0.313827578325869 0.627655156651738 0.686172421674131 41 0.275281032283036 0.550562064566072 0.724718967716964 42 0.338043338092707 0.676086676185413 0.661956661907293 43 0.352172981460353 0.704345962920705 0.647827018539647 44 0.415738440171975 0.83147688034395 0.584261559828025 45 0.511048846410842 0.977902307178317 0.488951153589158 46 0.615690147855147 0.768619704289707 0.384309852144853 47 0.529550413960387 0.940899172079226 0.470449586039613 48 0.435698478328397 0.871396956656794 0.564301521671603 49 0.372874998559511 0.745749997119021 0.62712500144049 50 0.322541897455308 0.645083794910616 0.677458102544692 51 0.405291321196081 0.810582642392162 0.594708678803919 52 0.452208834478191 0.904417668956383 0.547791165521809 53 0.349826965953364 0.699653931906729 0.650173034046636 54 0.295635036584525 0.591270073169051 0.704364963415475 55 0.176448137227505 0.352896274455009 0.823551862772495 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 0.0196078431372549 OK 10% type I error level 3 0.0588235294117647 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/10o9gi1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/10o9gi1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/1z63z1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/1z63z1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/2i0mk1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/2i0mk1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/30o7f1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/30o7f1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/4g8bv1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/4g8bv1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/5d5wo1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/5d5wo1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/6ol3a1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/6ol3a1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/7emjo1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/7emjo1258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/8hfn81258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/8hfn81258743441.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/9rn5w1258743441.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743777qybx3u440g518k3/9rn5w1258743441.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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