*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: Wed, 18 Nov 2009 10:51:37 -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/18/t1258566979xie77iujj9rgwwg.htm/, Retrieved Wed, 18 Nov 2009 18:56:31 +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/18/t1258566979xie77iujj9rgwwg.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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2863,36 99.9 2882,6 2767,63 2803,47 3030,29 2897,06 99.7 2863,36 2882,6 2767,63 2803,47 3012,61 99.5 2897,06 2863,36 2882,6 2767,63 3142,95 99.2 3012,61 2897,06 2863,36 2882,6 3032,93 99 3142,95 3012,61 2897,06 2863,36 3045,78 99 3032,93 3142,95 3012,61 2897,06 3110,52 99.3 3045,78 3032,93 3142,95 3012,61 3013,24 99.5 3110,52 3045,78 3032,93 3142,95 2987,1 99.7 3013,24 3110,52 3045,78 3032,93 2995,55 100 2987,1 3013,24 3110,52 3045,78 2833,18 100.4 2995,55 2987,1 3013,24 3110,52 2848,96 100.6 2833,18 2995,55 2987,1 3013,24 2794,83 100.7 2848,96 2833,18 2995,55 2987,1 2845,26 100.7 2794,83 2848,96 2833,18 2995,55 2915,02 100.6 2845,26 2794,83 2848,96 2833,18 2892,63 100.5 2915,02 2845,26 2794,83 2848,96 2604,42 100.6 2892,63 2915,02 2845,26 2794,83 2641,65 100.5 2604,42 2892,63 2915,02 2845,26 2659,81 100.4 2641,65 2604,42 2892,63 2915,02 2638,53 100.3 2659,81 2641,65 2604,42 2892,63 2720,25 100.4 2638,53 2659,81 2641,65 2604,42 2745,88 100.4 2720,25 2638,53 2659,81 2641,65 2735,7 100.4 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = -1092.78270532526 + 12.5769027972356Gzhind[t] + 1.54924150207039Y1[t] -0.763672660876186Y2[t] + 0.338991410113042Y3[t] -0.141990563713654Y4[t] -199.107040330143M1[t] -101.49182241988M2[t] -148.393128332664M3[t] -88.7734804200535M4[t] -218.605515568737M5[t] -65.3177530628458M6[t] -88.7273869252064M7[t] -167.702931515144M8[t] -49.6419680958159M9[t] -175.368415813703M10[t] -187.01519336378M11[t] -0.282093134491550t + 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) -1092.78270532526 2068.766542 -0.5282 0.600412 0.300206 Gzhind 12.5769027972356 20.326503 0.6187 0.539777 0.269888 Y1 1.54924150207039 0.161362 9.601 0 0 Y2 -0.763672660876186 0.29179 -2.6172 0.012656 0.006328 Y3 0.338991410113042 0.281892 1.2026 0.236587 0.118293 Y4 -0.141990563713654 0.162602 -0.8732 0.388018 0.194009 M1 -199.107040330143 70.766654 -2.8136 0.007716 0.003858 M2 -101.49182241988 67.987818 -1.4928 0.143748 0.071874 M3 -148.393128332664 63.416287 -2.34 0.024638 0.012319 M4 -88.7734804200535 63.122114 -1.4064 0.167738 0.083869 M5 -218.605515568737 64.554411 -3.3864 0.001658 0.000829 M6 -65.3177530628458 62.930493 -1.0379 0.30586 0.15293 M7 -88.7273869252064 65.533452 -1.3539 0.183758 0.091879 M8 -167.702931515144 68.114055 -2.4621 0.018462 0.009231 M9 -49.6419680958159 66.70994 -0.7441 0.461365 0.230682 M10 -175.368415813703 68.657013 -2.5543 0.014773 0.007386 M11 -187.01519336378 66.91962 -2.7946 0.008099 0.00405 t -0.282093134491550 1.512973 -0.1864 0.853084 0.426542 Multiple Linear Regression - Regression Statistics Multiple R 0.982311884103085 R-squared 0.964936637650153 Adjusted R-squared 0.949250396598906 F-TEST (value) 61.5148418603080 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 91.233745581805 Sum Squared Residuals 316296.660649649 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2863.36 2836.62060129505 26.7393987049488 2 2897.06 2833.88834071369 63.1716592863088 3 3012.61 2895.15484594619 117.455154053808 4 3142.95 3081.15156693711 61.7984330628865 5 3032.93 3076.36372847676 -43.4337284767624 6 3045.78 2993.77012861319 52.0098713868124 7 3110.52 3105.55562266373 4.96437733626984 8 3013.24 3063.49518163545 -50.255181635452 9 2987.1 3003.61689253293 -16.5168925329319 10 2995.55 2935.29605125264 60.253948747363 11 2833.18 2919.28188226414 -86.1018822641426 12 2848.96 2855.47859295501 -6.51859295501351 13 2794.83 2812.36782137017 -17.5378213701703 14 2845.26 2757.54789352681 87.7121064731903 15 2915.02 2856.97694656422 58.0430534357808 16 2892.63 2964.02966983424 -71.3996698342379 17 2604.42 2771.99319580253 -167.573195802529 18 2641.65 2510.82036910092 130.829630899079 19 2659.81 2746.15203114046 -86.342031140456 20 2638.53 2570.81785006235 67.7121499376502 21 2720.25 2696.56200650776 23.6879934922381 22 2745.88 2714.27821074861 31.6017892513861 23 2735.7 2669.85678407106 65.8432159289373 24 2811.7 2851.96961274128 -40.2696127412817 25 2799.43 2775.18190209623 24.2480979037658 26 2555.28 2789.6342509918 -234.354250991803 27 2304.98 2402.04030414991 -97.0603041499127 28 2214.95 2245.10268366841 -30.1526836684080 29 2065.81 2084.37739112909 -18.5673911290882 30 1940.49 2024.90007871979 -84.4100787197871 31 2042 1928.48776933105 113.512230668955 32 1995.37 2069.45408700856 -74.0840870085609 33 1946.81 2021.19624452210 -74.3862445220955 34 1765.9 1912.8026291099 -146.902629109899 35 1635.25 1629.97917168123 5.27082831877324 36 1833.42 1742.61948785501 90.8005121449927 37 1910.43 1895.58550176952 14.8444982304792 38 1959.67 1946.06005940324 13.6099405967615 39 1969.6 2007.11063631402 -37.5106363140189 40 2061.41 2045.96944670699 15.4405532930057 41 2093.48 2056.26515492878 37.2148450712213 42 2120.88 2182.70140106225 -61.8214010622488 43 2174.56 2200.39229265401 -25.8322926540103 44 2196.72 2174.92015732187 21.7998426781308 45 2350.44 2283.22485643721 67.2151435627893 46 2440.25 2385.20310888885 55.0468911111502 47 2408.64 2393.65216198357 14.9878380164321 48 2472.81 2516.82230644870 -44.0123064486975 49 2407.6 2455.89417346902 -48.2941734690235 50 2454.62 2384.75945536446 69.8605446355425 51 2448.05 2488.97726702566 -40.9272670256572 52 2497.84 2473.52663285325 24.3133671467537 53 2645.64 2453.28052966284 192.359470337158 54 2756.76 2793.36802250386 -36.6080225038557 55 2849.27 2855.57228421076 -6.30228421075798 56 2921.44 2886.61272397177 34.8272760282317 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.236460848077182 0.472921696154363 0.763539151922818 22 0.159515815174221 0.319031630348441 0.84048418482578 23 0.0961097489874067 0.192219497974813 0.903890251012593 24 0.0714564424518679 0.142912884903736 0.928543557548132 25 0.156505586205749 0.313011172411498 0.843494413794251 26 0.753166492208777 0.493667015582445 0.246833507791223 27 0.844413484758106 0.311173030483788 0.155586515241894 28 0.784196083139915 0.43160783372017 0.215803916860085 29 0.69559463668264 0.60881072663472 0.30440536331736 30 0.748340810175116 0.503318379649767 0.251659189824884 31 0.954450224288869 0.0910995514222625 0.0455497757111313 32 0.94979020541583 0.100419589168340 0.0502097945841699 33 0.997085926399475 0.00582814720104935 0.00291407360052468 34 0.994753609664952 0.0104927806700966 0.00524639033504832 35 0.980125444430683 0.0397491111386346 0.0198745555693173 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0666666666666667 NOK 5% type I error level 3 0.2 NOK 10% type I error level 4 0.266666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/10vzqw1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/10vzqw1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/1pjg21258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/1pjg21258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/2vqcj1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/2vqcj1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/3zt9v1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/3zt9v1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/4616d1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/4616d1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/5ke8g1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/5ke8g1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/6hdns1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/6hdns1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/7tvml1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/7tvml1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/89byj1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/89byj1258566690.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/932su1258566690.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566979xie77iujj9rgwwg/932su1258566690.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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