ws 8 trend

*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: Sat, 27 Nov 2010 19:21:25 +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/27/t1290885821uy1zqxmn3oxy4ku.htm/, Retrieved Sat, 27 Nov 2010 20:23:51 +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/27/t1290885821uy1zqxmn3oxy4ku.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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94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3 91 93.2 103.1 94.1 91.8 102.7 82.6 89.1 104.5 105.1 95.1 88.7 86.3 91.8 111.5 99.7 97.5 111.7 86.2 95.4

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation productie[t] = + 101.158333333333 -0.0403240740740826M1[t] + 2.07907407407409M2[t] + 12.7984722222222M3[t] + 5.78453703703704M4[t] + 5.0539351851852M5[t] + 15.2066666666667M6[t] -10.0906018518518M7[t] + 0.195462962962974M8[t] + 14.5918055555556M9[t] + 15.4545370370371M10[t] + 8.33726851851853M11[t] -0.102731481481481t + 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) 101.158333333333 3.323364 30.4385 0 0 M1 -0.0403240740740826 4.030084 -0.01 0.992053 0.496026 M2 2.07907407407409 4.028181 0.5161 0.607831 0.303915 M3 12.7984722222222 4.026699 3.1784 0.002431 0.001216 M4 5.78453703703704 4.025641 1.4369 0.156404 0.078202 M5 5.0539351851852 4.025006 1.2556 0.214557 0.107278 M6 15.2066666666667 4.024794 3.7782 0.00039 0.000195 M7 -10.0906018518518 4.025006 -2.507 0.015161 0.00758 M8 0.195462962962974 4.025641 0.0486 0.96145 0.480725 M9 14.5918055555556 4.205585 3.4696 0.001021 0.00051 M10 15.4545370370371 4.204571 3.6757 0.00054 0.00027 M11 8.33726851851853 4.203963 1.9832 0.052348 0.026174 t -0.102731481481481 0.041285 -2.4884 0.015891 0.007945 Multiple Linear Regression - Regression Statistics Multiple R 0.79604363761988 R-squared 0.63368547299509 Adjusted R-squared 0.553762303466746 F-TEST (value) 7.92868296808923 F-TEST (DF numerator) 12 F-TEST (DF denominator) 55 p-value 2.51533024409056e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.64672889852633 Sum Squared Residuals 2429.84527777778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94.6 101.015277777778 -6.41527777777786 2 95.9 103.031944444444 -7.13194444444444 3 104.7 113.648611111111 -8.9486111111111 4 102.8 106.531944444444 -3.73194444444445 5 98.1 105.698611111111 -7.59861111111111 6 113.9 115.748611111111 -1.84861111111112 7 80.9 90.348611111111 -9.44861111111107 8 95.7 100.531944444444 -4.83194444444445 9 113.2 114.825555555556 -1.62555555555555 10 105.9 115.585555555556 -9.68555555555554 11 108.8 108.365555555556 0.434444444444428 12 102.3 99.9255555555555 2.37444444444445 13 99 99.7825 -0.782499999999983 14 100.7 101.799166666667 -1.09916666666667 15 115.5 112.415833333333 3.08416666666667 16 100.7 105.299166666667 -4.59916666666666 17 109.9 104.465833333333 5.43416666666668 18 114.6 114.515833333333 0.084166666666662 19 85.4 89.1158333333333 -3.71583333333334 20 100.5 99.2991666666667 1.20083333333333 21 114.8 113.592777777778 1.20722222222222 22 116.5 114.352777777778 2.14722222222222 23 112.9 107.132777777778 5.76722222222223 24 102 98.6927777777778 3.30722222222223 25 106 98.5497222222222 7.4502777777778 26 105.3 100.566388888889 4.7336111111111 27 118.8 111.183055555556 7.61694444444444 28 106.1 104.066388888889 2.03361111111111 29 109.3 103.233055555556 6.06694444444444 30 117.2 113.283055555556 3.91694444444445 31 92.5 87.8830555555556 4.61694444444444 32 104.2 98.0663888888889 6.13361111111111 33 112.5 112.36 0.14 34 122.4 113.12 9.28 35 113.3 105.9 7.4 36 100 97.46 2.54000000000001 37 110.7 97.3169444444444 13.3830555555556 38 112.8 99.3336111111111 13.4663888888889 39 109.8 109.950277777778 -0.150277777777779 40 117.3 102.833611111111 14.4663888888889 41 109.1 102.000277777778 7.09972222222222 42 115.9 112.050277777778 3.84972222222223 43 96 86.6502777777778 9.34972222222221 44 99.8 96.8336111111111 2.96638888888889 45 116.8 111.127222222222 5.67277777777777 46 115.7 111.887222222222 3.81277777777778 47 99.4 104.667222222222 -5.26722222222222 48 94.3 96.2272222222222 -1.92722222222222 49 91 96.0841666666666 -5.08416666666665 50 93.2 98.1008333333333 -4.90083333333334 51 103.1 108.7175 -5.61750000000001 52 94.1 101.600833333333 -7.50083333333334 53 91.8 100.7675 -8.9675 54 102.7 110.8175 -8.1175 55 82.6 85.4175 -2.81750000000002 56 89.1 95.6008333333333 -6.50083333333334 57 104.5 109.894444444444 -5.39444444444445 58 105.1 110.654444444444 -5.55444444444445 59 95.1 103.434444444444 -8.33444444444445 60 88.7 94.9944444444444 -6.29444444444443 61 86.3 94.8513888888889 -8.55138888888888 62 91.8 96.8680555555556 -5.06805555555557 63 111.5 107.484722222222 4.01527777777777 64 99.7 100.368055555556 -0.668055555555555 65 97.5 99.5347222222222 -2.03472222222222 66 111.7 109.584722222222 2.11527777777778 67 86.2 84.1847222222222 2.01527777777777 68 95.4 94.3680555555556 1.03194444444445 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.246156974963074 0.492313949926148 0.753843025036926 17 0.223315345094332 0.446630690188664 0.776684654905668 18 0.167327997386593 0.334655994773186 0.832672002613407 19 0.117296894984265 0.234593789968530 0.882703105015735 20 0.0679045431288118 0.135809086257624 0.932095456871188 21 0.0435210625427960 0.0870421250855921 0.956478937457204 22 0.0437094332153011 0.0874188664306023 0.956290566784699 23 0.0226307869823082 0.0452615739646163 0.977369213017692 24 0.0183640279185951 0.0367280558371901 0.981635972081405 25 0.009872613973709 0.019745227947418 0.99012738602629 26 0.00486155688802709 0.00972311377605418 0.995138443111973 27 0.00227489376593495 0.0045497875318699 0.997725106234065 28 0.00167591498890271 0.00335182997780542 0.998324085011097 29 0.000789761478647406 0.00157952295729481 0.999210238521353 30 0.00056124931493781 0.00112249862987562 0.999438750685062 31 0.000383494803218873 0.000766989606437745 0.999616505196781 32 0.000163099066302521 0.000326198132605041 0.999836900933697 33 0.000589221028919101 0.00117844205783820 0.99941077897108 34 0.000488056999900019 0.000976113999800038 0.9995119430001 35 0.000397380803126273 0.000794761606252546 0.999602619196874 36 0.000726807194596451 0.00145361438919290 0.999273192805403 37 0.00127384751458687 0.00254769502917375 0.998726152485413 38 0.0027896550842161 0.0055793101684322 0.997210344915784 39 0.0106830297544493 0.0213660595088987 0.98931697024555 40 0.0385183653290377 0.0770367306580755 0.961481634670962 41 0.0561133275122573 0.112226655024515 0.943886672487743 42 0.0606449961700173 0.121289992340035 0.939355003829983 43 0.0785489347941831 0.157097869588366 0.921451065205817 44 0.103292985320278 0.206585970640556 0.896707014679722 45 0.207731610381116 0.415463220762231 0.792268389618884 46 0.431738398045603 0.863476796091206 0.568261601954397 47 0.703315706624768 0.593368586750463 0.296684293375232 48 0.85485968409887 0.290280631802260 0.145140315901130 49 0.974799008730163 0.050401982539674 0.025200991269837 50 0.997610979476901 0.00477804104619738 0.00238902052309869 51 0.994658288968683 0.010683422062633 0.0053417110313165 52 0.979238939861171 0.0415221202776577 0.0207610601388288 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.378378378378378 NOK 5% type I error level 20 0.540540540540541 NOK 10% type I error level 24 0.648648648648649 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/10z6sk1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/10z6sk1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/1b5dq1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/1b5dq1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/2b5dq1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/2b5dq1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/3lecb1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/3lecb1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/4lecb1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/4lecb1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/5lecb1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/5lecb1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/6e6ce1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/6e6ce1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/77xth1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/77xth1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/87xth1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/87xth1290885677.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/97xth1290885677.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t1290885821uy1zqxmn3oxy4ku/97xth1290885677.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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