ws7 3

*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 11:49:38 -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/t1258743038dpare45smw01qyp.htm/, Retrieved Fri, 20 Nov 2009 19:50:51 +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/2009/Nov/20/t1258743038dpare45smw01qyp.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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2360 8.1 2214 7.4 2825 7.3 2355 7.7 2333 8 3016 8 2155 7.7 2172 6.9 2150 6.6 2533 6.9 2058 7.5 2160 7.9 2260 7.7 2498 6.5 2695 6.1 2799 6.4 2947 6.8 2930 7.1 2318 7.3 2540 7.2 2570 7 2669 7 2450 7 2842 7.3 3440 7.5 2678 7.2 2981 7.7 2260 8 2844 7.9 2546 8 2456 8 2295 7.9 2379 7.9 2479 8 2057 8.1 2280 8.1 2351 8.2 2276 8 2548 8.3 2311 8.5 2201 8.6 2725 8.7 2408 8.7 2139 8.5 1898 8.4 2537 8.5 2069 8.7 2063 8.7 2524 8.6 2437 7.9 2189 8.1 2793 8.2 2074 8.5 2622 8.6 2278 8.5 2144 8.3 2427 8.2 2139 8.7 1828 9.3 2072 9.3 1800 8.8 1758 7.4 2246 7.2 1987 7.5 1868 8.3 2514 8.8 2121 8.9

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] = + 3519.20884433017 -133.248614219740X[t] + 139.001639814284M1[t] -102.846618326212M2[t] + 178.070681575751M3[t] + 54.1918478919903M4[t] + 58.254634682221M5[t] + 434.105083146815M6[t] -0.527524565871434M7[t] -107.043783873772M8[t] -95.1437206735603M9[t] + 121.860871361363M10[t] -213.409675181739M11[t] -3.75486919097563t + 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) 3519.20884433017 557.462363 6.3129 0 0 X -133.248614219740 72.790718 -1.8306 0.072789 0.036395 M1 139.001639814284 165.753363 0.8386 0.405457 0.202729 M2 -102.846618326212 174.824432 -0.5883 0.55884 0.27942 M3 178.070681575751 174.152211 1.0225 0.311189 0.155594 M4 54.1918478919903 169.465562 0.3198 0.750392 0.375196 M5 58.254634682221 166.308104 0.3503 0.727516 0.363758 M6 434.105083146815 165.58332 2.6217 0.011396 0.005698 M7 -0.527524565871434 165.694531 -0.0032 0.997472 0.498736 M8 -107.043783873772 175.542562 -0.6098 0.544608 0.272304 M9 -95.1437206735603 177.874743 -0.5349 0.594962 0.297481 M10 121.860871361363 175.276724 0.6952 0.489937 0.244968 M11 -213.409675181739 173.103362 -1.2328 0.223074 0.111537 t -3.75486919097563 2.423851 -1.5491 0.127301 0.06365 Multiple Linear Regression - Regression Statistics Multiple R 0.66356672214392 R-squared 0.440320794736827 Adjusted R-squared 0.303040989672275 F-TEST (value) 3.20746955118256 F-TEST (DF numerator) 13 F-TEST (DF denominator) 53 p-value 0.00132221729725535 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 273.356717095901 Sum Squared Residuals 3960366.42341676 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2360 2575.14183977359 -215.141839773588 2 2214 2422.81274239593 -208.812742395935 3 2825 2713.30003452890 111.699965471104 4 2355 2532.36688596626 -177.366885966264 5 2333 2492.70021929960 -159.700219299598 6 3016 2864.79579857322 151.204201426783 7 2155 2466.38290593548 -311.382905935476 8 2172 2462.71066881239 -290.710668812391 9 2150 2510.83044708755 -360.830447087549 10 2533 2684.10558566558 -151.105585665575 11 2058 2265.13100139965 -207.131001399654 12 2160 2421.48636170252 -261.486361702521 13 2260 2583.38285516978 -323.382855169777 14 2498 2497.67806490199 0.321935098006225 15 2695 2828.13994130088 -133.139941300876 16 2799 2660.53165416022 138.468345839782 17 2947 2607.54012607158 339.459873928422 18 2930 2939.66112107927 -9.66112107927486 19 2318 2474.62392133166 -156.623921331664 20 2540 2377.67765425476 162.322345745238 21 2570 2412.47257110795 157.527428892054 22 2669 2625.72229395189 43.2777060481061 23 2450 2286.69687821782 163.303121782184 24 2842 2456.37709994266 385.622900057342 25 3440 2564.97414772202 875.025852277982 26 2678 2359.34560465647 318.654395343532 27 2981 2569.88372825759 411.116271742414 28 2260 2402.27544111693 -142.275441116927 29 2844 2415.90822013816 428.091779861843 30 2546 2774.6789379898 -228.678937989802 31 2456 2336.29146108614 119.708538913861 32 2295 2239.34519400924 55.6548059907635 33 2379 2247.49038801847 131.509611981527 34 2479 2447.41524944045 31.5847505595531 35 2057 2095.06497228440 -38.0649722843948 36 2280 2304.71977827516 -24.7197782751585 37 2351 2426.64168747649 -75.641687476493 38 2276 2207.68828298897 68.3117170110307 39 2548 2444.87612943403 103.123870565966 40 2311 2290.59270371535 20.4072962846498 41 2201 2277.57575989263 -76.5757598926314 42 2725 2636.34647774428 88.6535222557235 43 2408 2197.95900084061 210.040999159386 44 2139 2114.33759518569 24.6624048143146 45 1898 2135.80765061690 -237.807650616896 46 2537 2335.73251203887 201.267487961130 47 2069 1970.05737346084 98.9426265391564 48 2063 2179.71217945161 -116.712179451607 49 2524 2328.28381149689 195.716188503111 50 2437 2175.95471411924 261.045285880764 51 2189 2426.46742198627 -237.467421986275 52 2793 2285.50885768956 507.491142310435 53 2074 2245.8421910229 -171.842191022898 54 2622 2604.61290887454 17.3870911254571 55 2278 2179.55029339285 98.4497066071456 56 2144 2095.92888773793 48.0711122620743 57 2427 2117.39894316914 309.601056830864 58 2139 2264.02435890321 -125.024358903214 59 1828 1845.04977463729 -17.0497746372923 60 2072 2054.70458062806 17.2954193719441 61 1800 2256.57565836123 -456.575658361234 62 1758 2197.52059093740 -439.520590937398 63 2246 2501.33274449233 -255.332744492333 64 1987 2333.72445735168 -346.724457351675 65 1868 2227.43348357514 -359.433483575138 66 2514 2532.90475573889 -18.9047557388873 67 2121 2081.19241741325 39.8075825867489 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.496805399670399 0.993610799340798 0.503194600329601 18 0.396938280151417 0.793876560302835 0.603061719848583 19 0.347406952016174 0.694813904032348 0.652593047983826 20 0.356898439012097 0.713796878024193 0.643101560987903 21 0.279395816323647 0.558791632647294 0.720604183676353 22 0.196764398023303 0.393528796046605 0.803235601976697 23 0.140470297772478 0.280940595544956 0.859529702227522 24 0.185806726685232 0.371613453370464 0.814193273314768 25 0.64523224804515 0.709535503909699 0.354767751954849 26 0.622388023510538 0.755223952978924 0.377611976489462 27 0.63359433692049 0.73281132615902 0.36640566307951 28 0.810211883362749 0.379576233274502 0.189788116637251 29 0.863500868806761 0.272998262386479 0.136499131193239 30 0.93765800036458 0.124683999270841 0.0623419996354203 31 0.905578628222724 0.188842743554552 0.0944213717772758 32 0.866206190139715 0.26758761972057 0.133793809860285 33 0.809807202523997 0.380385594952006 0.190192797476003 34 0.749229959342771 0.501540081314457 0.250770040657229 35 0.696488556831307 0.607022886337387 0.303511443168693 36 0.651114852137796 0.697770295724408 0.348885147862204 37 0.656796720443134 0.686406559113732 0.343203279556866 38 0.578705350755326 0.842589298489347 0.421294649244674 39 0.497987866616541 0.995975733233081 0.502012133383459 40 0.483815505691916 0.967631011383833 0.516184494308084 41 0.446840617124146 0.893681234248292 0.553159382875854 42 0.361213236702167 0.722426473404334 0.638786763297833 43 0.288954383756259 0.577908767512519 0.71104561624374 44 0.230811508699243 0.461623017398486 0.769188491300757 45 0.572664771527221 0.854670456945558 0.427335228472779 46 0.469254170338386 0.938508340676772 0.530745829661614 47 0.348538669483704 0.697077338967407 0.651461330516296 48 0.295471601088233 0.590943202176465 0.704528398911767 49 0.324296862045532 0.648593724091065 0.675703137954468 50 0.300360990531469 0.600721981062939 0.69963900946853 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/20/t1258743038dpare45smw01qyp/108kt21258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/108kt21258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/18cwh1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/18cwh1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/2j9wm1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/2j9wm1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/3jsje1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/3jsje1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/480td1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/480td1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/5oujf1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/5oujf1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/6mqeu1258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/6mqeu1258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/7n0t11258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/7n0t11258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/839e81258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/839e81258742973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/92mi71258742973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743038dpare45smw01qyp/92mi71258742973.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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