*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: Thu, 19 Nov 2009 02:58:07 -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/19/t12586247617rng5o4w12ni3kr.htm/, Retrieved Thu, 19 Nov 2009 10:59:34 +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/19/t12586247617rng5o4w12ni3kr.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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3499 1 4164 3186 4145 1 3499 3902 3796 1 4145 4164 3711 1 3796 3499 3949 1 3711 4145 3740 1 3949 3796 3243 1 3740 3711 4407 1 3243 3949 4814 1 4407 3740 3908 1 4814 3243 5250 1 3908 4407 3937 1 5250 4814 4004 1 3937 3908 5560 1 4004 5250 3922 1 5560 3937 3759 1 3922 4004 4138 1 3759 5560 4634 1 4138 3922 3996 1 4634 3759 4308 1 3996 4138 4143 0 4308 4634 4429 0 4143 3996 5219 0 4429 4308 4929 0 5219 4143 5755 0 4929 4429 5592 0 5755 5219 4163 0 5592 4929 4962 0 4163 5755 5208 0 4962 5592 4755 0 5208 4163 4491 0 4755 4962 5732 0 4491 5208 5731 0 5732 4755 5040 0 5731 4491 6102 0 5040 5732 4904 0 6102 5731 5369 0 4904 5040 5578 0 5369 6102 4619 0 5578 4904 4731 0 4619 5369 5011 0 4731 5578 5299 0 5011 4619 4146 0 5299 4731 4625 0 4146 5011 4736 0 4625 5299 4219 0 4736 4146 5116 0 4219 4625 4205 0 5116 4736 4121 0 4205 4219 5103 1 4121 5116 4300 1 5103 4205 4578 1 4300 4121 3809 1 4578 5103 5526 1 3809 4300 4247 1 5526 4578 3830 1 4247 3809 4394 1 3830 5526

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 873.552019317555 -166.509677507681X[t] + 0.336497568874567Y1[t] + 0.386622532227597Y3[t] + 679.099448674866M1[t] + 947.051054819987M2[t] -37.8228504198242M3[t] + 453.097384497474M4[t] + 216.144363232371M5[t] + 960.49461647157M6[t] -0.897572909612111M7[t] + 785.141475967574M8[t] + 607.148401348361M9[t] + 435.889731644429M10[t] + 1304.84169438637M11[t] -1.33397701611762t + 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) 873.552019317555 990.045926 0.8823 0.38274 0.19137 X -166.509677507681 175.658568 -0.9479 0.348726 0.174363 Y1 0.336497568874567 0.137851 2.441 0.019051 0.009526 Y3 0.386622532227597 0.144651 2.6728 0.010747 0.005373 M1 679.099448674866 322.012688 2.1089 0.041104 0.020552 M2 947.051054819987 317.234669 2.9853 0.00476 0.00238 M3 -37.8228504198242 294.395804 -0.1285 0.8984 0.4492 M4 453.097384497474 326.272894 1.3887 0.17242 0.08621 M5 216.144363232371 329.525212 0.6559 0.515536 0.257768 M6 960.49461647157 318.898282 3.0119 0.004433 0.002216 M7 -0.897572909612111 302.394262 -0.003 0.997646 0.498823 M8 785.141475967574 338.21386 2.3214 0.025312 0.012656 M9 607.148401348361 310.311105 1.9566 0.057231 0.028616 M10 435.889731644429 336.880868 1.2939 0.202942 0.101471 M11 1304.84169438637 336.307055 3.8799 0.000371 0.000186 t -1.33397701611762 4.302301 -0.3101 0.758085 0.379042 Multiple Linear Regression - Regression Statistics Multiple R 0.833103487567017 R-squared 0.694061420996326 Adjusted R-squared 0.582132672580348 F-TEST (value) 6.20092184375079 F-TEST (DF numerator) 15 F-TEST (DF denominator) 41 p-value 1.60352825517851e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 432.709464877265 Sum Squared Residuals 7676736.72076912 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3499 4017.76307793944 -518.763077939445 2 4145 4337.43155684182 -192.431556841816 3 3796 3669.89620752249 126.103792477511 4 3711 3784.9408299551 -73.9408299550955 5 3949 3767.80969413856 181.190305861437 6 3740 4455.98112800636 -715.981128006359 7 3243 3390.06405447493 -147.064054474930 8 4407 4099.54599727551 307.454002724493 9 4814 4231.09800657461 582.901993425395 10 3908 4003.30847186939 -95.3084718693893 11 5250 5016.08828770778 233.911712292218 12 3937 4318.84772435159 -381.847724351590 13 4004 4204.51187387983 -200.511873879830 14 5560 5012.52227837286 547.477721627136 15 3922 4042.26922847093 -120.269228470927 16 3759 4006.57617821482 -247.576178214815 17 4138 4315.02473635318 -177.024736353180 18 4634 4552.28588339092 81.7141166090809 19 3996 3693.44303840231 302.556961597694 20 4308 4409.99260103566 -101.992601035661 21 4143 4693.92724438177 -550.927244381765 22 4429 4219.14732323621 209.852676763795 23 5219 5303.62984371517 -84.6298437151687 24 4929 4199.49453390603 729.505466093969 25 5755 4890.24975480825 864.750245191752 26 5592 5740.24617628745 -148.246176287446 27 4163 4587.06865595896 -424.068655958959 28 4962 4915.15009955838 46.8499004416228 29 5208 4882.70518605484 325.294813945162 30 4755 5156.01626566783 -401.016265667827 31 4491 4349.7681038202 141.231896179802 32 5732 5140.74696042637 591.25303957363 33 5731 5203.87338466528 527.126615334724 34 5040 4928.87589186827 111.124108131734 35 6102 6043.77261999622 58.2273800037847 36 4904 5094.57074420629 -190.570744206286 37 5369 5102.05595858403 266.944041415966 38 5578 5935.73808646542 -357.738086465419 39 4619 4556.68440249561 62.3155975043864 40 4731 4903.34896933192 -172.348969331916 41 5011 4783.55380800021 227.446191999785 42 5299 5250.01839510191 48.9816048980898 43 4146 4427.50525214998 -281.505252149977 44 4625 4932.4829361224 -307.482936122397 45 4736 5025.68550925953 -289.685509259532 46 4219 4444.66831302614 -225.66831302614 47 5116 5323.50924858083 -207.509248580835 48 4205 4362.08699753609 -157.086997536093 49 4121 4533.41933478844 -412.419334788443 50 5103 4952.06190203246 150.938097967544 51 4300 3944.08150555201 355.918494447989 52 4578 4130.9839229398 447.016077060204 53 3809 4365.90657545320 -556.906575453204 54 5526 4539.69832783298 986.301672167016 55 4247 4262.21955115259 -15.2195511525879 56 3830 4319.23150514006 -489.231505140064 57 4394 4663.41585511882 -269.415855118822 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.621602954128869 0.756794091742262 0.378397045871131 20 0.580428545505122 0.839142908989756 0.419571454494878 21 0.500603544021645 0.99879291195671 0.499396455978355 22 0.573321967180018 0.853356065639964 0.426678032819982 23 0.496134046405957 0.992268092811913 0.503865953594043 24 0.531490210143521 0.937019579712958 0.468509789856479 25 0.791149277117747 0.417701445764507 0.208850722882253 26 0.71539293826548 0.569214123469039 0.284607061734520 27 0.749480354035719 0.501039291928562 0.250519645964281 28 0.706568948212076 0.586862103575847 0.293431051787924 29 0.614112323699829 0.771775352600342 0.385887676300171 30 0.872465051534345 0.25506989693131 0.127534948465655 31 0.945083765179527 0.109832469640945 0.0549162348204727 32 0.912328458686217 0.175343082627566 0.0876715413137831 33 0.87350292551273 0.252994148974541 0.126497074487271 34 0.789913681411412 0.420172637177175 0.210086318588588 35 0.676558185595168 0.646883628809663 0.323441814404832 36 0.606270443219057 0.787459113561886 0.393729556780943 37 0.449004135321866 0.898008270643733 0.550995864678134 38 0.339577760929035 0.679155521858069 0.660422239070965 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/102rqd1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/102rqd1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/1iekg1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/1iekg1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/256xl1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/256xl1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/3qv851258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/3qv851258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/4hlu81258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/4hlu81258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/55c681258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/55c681258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/652lb1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/652lb1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/76j4d1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/76j4d1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/8dc1w1258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/8dc1w1258624682.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/9vnz11258624682.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586247617rng5o4w12ni3kr/9vnz11258624682.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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