*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 08:16:02 -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/t1258557400qphyxrw7uia0zx6.htm/, Retrieved Wed, 18 Nov 2009 16:16:52 +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/t1258557400qphyxrw7uia0zx6.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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7.2 97.78 7.5 8.3 8.9 7.4 97.69 7.2 7.5 8.8 8.8 96.67 7.4 7.2 8.3 9.3 98.29 8.8 7.4 7.5 9.3 98.2 9.3 8.8 7.2 8.7 98.71 9.3 9.3 7.4 8.2 98.54 8.7 9.3 8.8 8.3 98.2 8.2 8.7 9.3 8.5 96.92 8.3 8.2 9.3 8.6 99.06 8.5 8.3 8.7 8.5 99.65 8.6 8.5 8.2 8.2 99.82 8.5 8.6 8.3 8.1 99.99 8.2 8.5 8.5 7.9 100.33 8.1 8.2 8.6 8.6 99.31 7.9 8.1 8.5 8.7 101.1 8.6 7.9 8.2 8.7 101.1 8.7 8.6 8.1 8.5 100.93 8.7 8.7 7.9 8.4 100.85 8.5 8.7 8.6 8.5 100.93 8.4 8.5 8.7 8.7 99.6 8.5 8.4 8.7 8.7 101.88 8.7 8.5 8.5 8.6 101.81 8.7 8.7 8.4 8.5 102.38 8.6 8.7 8.5 8.3 102.74 8.5 8.6 8.7 8 102.82 8.3 8.5 8.7 8.2 101.72 8 8.3 8.6 8.1 103.47 8.2 8 8.5 8.1 102.98 8.1 8.2 8.3 8 102.68 8.1 8.1 8 7.9 102.9 8 8.1 8.2 7.9 103.03 7.9 8 8.1 8 101.29 7.9 7.9 8.1 8 103.69 8 7.9 8 7.9 103.68 8 8 7.9 8 104.2 7.9 8 7.9 7.7 104.08 8 7.9 8 7.2 104.16 7.7 8 8 7.5 103.05 7.2 7.7 7.9 7.3 104.66 7.5 7.2 8 7 104.46 7.3 7.5 7.7 7 104.95 7 7.3 7.2 7 105.85 7 7 7.5 7.2 106.23 7 7 7.3 7.3 104.86 7.2 7 7 7.1 107.44 7.3 7.2 7 6.8 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -3.92515432159056 + 0.0512518252555557X[t] + 1.51663485005415Y1[t] -0.91199180830936Y2[t] + 0.278978156355666Y4[t] -0.142293504458614M1[t] -0.115723656701091M2[t] + 0.67913254261951M3[t] -0.426949905291582M4[t] + 0.0675867935318781M5[t] + 0.106854305203872M6[t] + 0.0377162842122586M7[t] + 0.195531686682516M8[t] + 0.128227851596769M9[t] -0.0737310721580348M10[t] + 0.000156731586821268M11[t] -0.0170088270929127t + 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) -3.92515432159056 3.178199 -1.235 0.22421 0.112105 X 0.0512518252555557 0.029458 1.7399 0.089772 0.044886 Y1 1.51663485005415 0.099137 15.2984 0 0 Y2 -0.91199180830936 0.110388 -8.2617 0 0 Y4 0.278978156355666 0.068177 4.092 0.000208 0.000104 M1 -0.142293504458614 0.101081 -1.4077 0.167134 0.083567 M2 -0.115723656701091 0.103819 -1.1147 0.271814 0.135907 M3 0.67913254261951 0.109894 6.1799 0 0 M4 -0.426949905291582 0.130658 -3.2677 0.002267 0.001133 M5 0.0675867935318781 0.103988 0.6499 0.519538 0.259769 M6 0.106854305203872 0.108452 0.9853 0.330566 0.165283 M7 0.0377162842122586 0.099379 0.3795 0.706361 0.35318 M8 0.195531686682516 0.102214 1.913 0.063115 0.031558 M9 0.128227851596769 0.126295 1.0153 0.316217 0.158109 M10 -0.0737310721580348 0.108096 -0.6821 0.499213 0.249607 M11 0.000156731586821268 0.104228 0.0015 0.998808 0.499404 t -0.0170088270929127 0.006222 -2.7335 0.009373 0.004686 Multiple Linear Regression - Regression Statistics Multiple R 0.986192861958267 R-squared 0.972576360977437 Adjusted R-squared 0.961325637275873 F-TEST (value) 86.4456711208912 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.146278312041805 Sum Squared Residuals 0.83449643837819 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.2 7.21508177835 -0.0150817783500001 2 7.4 7.4667353107373 -0.0667353107373064 3 8.8 8.62974125553013 0.170258744469869 4 9.3 9.30738584076954 -0.0073858407695366 5 9.3 9.17813649471436 0.121863505285645 6 8.7 8.82633333729022 -0.126333337290224 7 8.2 8.21206218777769 -0.0120621877776920 8 8.3 8.26380988070452 0.0361901192954789 9 8.5 8.72155427135885 -0.221554271358849 10 8.6 8.65700632192451 -0.0570063219245135 11 8.5 8.57389992064295 -0.073899920642945 12 8.2 8.35048232205587 -0.150482322055872 13 8.1 7.89189715788361 0.208102842116388 14 7.9 8.06871567225807 -0.168715672258072 15 8.6 8.55426057790963 0.0457394220903664 16 8.7 8.68325937990615 0.0167406200938494 17 8.7 8.64615865519 0.0538413448100059 18 8.5 8.51270971737356 -0.0127097173735626 19 8.4 8.31442046270673 0.0855795372932725 20 8.5 8.51795987639654 -0.0179598763965408 21 8.7 8.60834495246434 0.0916550475356572 22 8.7 8.66256352110805 0.0374364788919462 23 8.6 8.50555869269467 0.0944413073053296 24 8.5 8.39384100504075 0.106158994959246 25 8.3 8.24832065767788 0.0516793423221192 26 8 8.04985403518304 -0.0498540351830433 27 8.2 8.47083449063968 -0.270834490639679 28 8.1 7.98646060670097 0.113539393299032 29 8.1 8.04901760611787 0.0509823938821255 30 8 8.06340647704453 -0.0634064770445249 31 7.9 7.89266717678194 0.00733282321806117 32 7.9 7.95177436963246 -0.0517743696324604 33 8 7.86948271234007 0.130517287659930 34 8 7.89728501147554 0.102714988524465 35 7.9 7.83455447340842 0.0654455265915794 36 8 7.69237637885616 0.307623621143839 37 7.7 7.79768430974588 -0.0976843097458844 38 7.2 7.26515584058376 -0.0651558405837586 39 7.5 7.47349598860795 0.0265040113920546 40 7.3 7.37180432707188 -0.0718043270718777 41 7 7.17846387434098 -0.178463874340975 42 7 6.81375478176307 0.186245218236926 43 7 7.13102556580805 -0.131025565808055 44 7.2 7.23551220351138 -0.0355122035113778 45 7.3 7.30061806383674 -0.000618063836738301 46 7.1 7.1831451454919 -0.0831451454918977 47 6.8 6.88598691325396 -0.085986913253964 48 6.4 6.66330029404721 -0.263300294047212 49 6.1 6.24701609634262 -0.147016096342623 50 6.5 6.14953914123782 0.35046085876218 51 7.7 7.67166768731261 0.0283323126873895 52 7.9 7.95108984555147 -0.0510898455514668 53 7.5 7.5482233696368 -0.0482233696368014 54 6.9 6.88379568652862 0.016204313471385 55 6.6 6.54982460692559 0.050175393074413 56 6.9 6.8309436697551 0.0690563302449001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.118597039480865 0.237194078961729 0.881402960519135 21 0.158641943368255 0.31728388673651 0.841358056631745 22 0.0851916858433506 0.170383371686701 0.91480831415665 23 0.0375534320130533 0.0751068640261066 0.962446567986947 24 0.061372348351353 0.122744696702706 0.938627651648647 25 0.0318857321119679 0.0637714642239359 0.968114267888032 26 0.0168945958769079 0.0337891917538158 0.983105404123092 27 0.282956097438329 0.565912194876659 0.71704390256167 28 0.19484789535671 0.38969579071342 0.80515210464329 29 0.156432084127582 0.312864168255163 0.843567915872418 30 0.180903613276643 0.361807226553286 0.819096386723357 31 0.140196853854047 0.280393707708094 0.859803146145953 32 0.123483867633537 0.246967735267075 0.876516132366463 33 0.09553246363269 0.19106492726538 0.90446753636731 34 0.0662522865713956 0.132504573142791 0.933747713428604 35 0.0335735392149152 0.0671470784298303 0.966426460785085 36 0.178837748676553 0.357675497353106 0.821162251323447 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 0.0588235294117647 NOK 10% type I error level 4 0.235294117647059 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/1023ko1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/1023ko1258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/1guz31258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/1guz31258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/2ziui1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/2ziui1258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/3isy61258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/3isy61258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/4ehgy1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/4ehgy1258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/5yj701258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/5yj701258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/6ooo91258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/6ooo91258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/7m09e1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/7m09e1258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/8p92j1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/8p92j1258557357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/9e7fm1258557357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557400qphyxrw7uia0zx6/9e7fm1258557357.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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