*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 08:39:13 -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/t1258731595r4rmfuq6lu29899.htm/, Retrieved Fri, 20 Nov 2009 16:40:07 +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/t1258731595r4rmfuq6lu29899.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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114 106.3 113.8 107.2 113.6 107.8 113.7 109.2 114.2 109.7 114.8 108.7 115.2 109.3 115.3 110.4 114.9 111.1 115.1 110.1 116 109.5 116 109 116 108.5 115.9 108.8 115.6 109.8 116.6 110.7 116.9 110.6 117.9 111.2 117.9 112 117.7 111.1 117.4 111.6 117.3 110.2 119 111.5 119.1 110.6 119 110.6 118.5 110.3 117 111.7 117.5 113.8 118.2 113.9 118.2 114.3 118.3 113.8 118.2 114.3 117.9 116.4 117.8 115.6 118.6 115.2 118.9 113.6 120.8 115.5 121.8 115.6 121.3 115.3 121.9 117.3 122 118.7 121.9 118.3 122 120.6 122.2 119.3 123 121.8 123.1 120.8 124.9 121.6 125.4 121.6 124.7 121.1 124.4 122.4 124 121.9 125 125.1 125.1 124.5 125.4 123.5 125.7 124.9 126.4 125.2 125.7 125.7 125.4 124.5 126.4 124.7 126.2 122.9

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation CPItot[t] = + 43.8313314423289 + 0.657891554789366CPIlandbouw[t] + 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) 43.8313314423289 2.970446 14.7558 0 0 CPIlandbouw 0.657891554789366 0.025756 25.543 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.958311670964566 R-squared 0.9183612587069 Adjusted R-squared 0.916953694201846 F-TEST (value) 652.447014264558 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.14427941767553 Sum Squared Residuals 75.9437723715197 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114 113.765203716438 0.234796283561588 2 113.8 114.357306115749 -0.557306115748934 3 113.6 114.752041048623 -1.15204104862256 4 113.7 115.673089225328 -1.97308922532767 5 114.2 116.002035002722 -1.80203500272235 6 114.8 115.344143447933 -0.544143447932988 7 115.2 115.738878380807 -0.538878380806598 8 115.3 116.462559091075 -1.16255909107491 9 114.9 116.923083179427 -2.02308317942745 10 115.1 116.265191624638 -1.16519162463810 11 116 115.870456691764 0.129543308235524 12 116 115.541510914370 0.458489085630207 13 116 115.212565136975 0.78743486302489 14 115.9 115.409932603412 0.490067396588088 15 115.6 116.067824158201 -0.467824158201289 16 116.6 116.659926557512 -0.0599265575117226 17 116.9 116.594137402033 0.305862597967231 18 117.9 116.988872334906 0.911127665093606 19 117.9 117.515185578738 0.384814421262115 20 117.7 116.923083179427 0.776916820572545 21 117.4 117.252028956822 0.147971043177865 22 117.3 116.330980780117 0.969019219882963 23 119 117.186239801343 1.81376019865679 24 119.1 116.594137402033 2.50586259796722 25 119 116.594137402033 2.40586259796723 26 118.5 116.396769935596 2.10323006440403 27 117 117.317818112301 -0.317818112301083 28 117.5 118.699390377359 -1.19939037735875 29 118.2 118.765179532838 -0.565179532837687 30 118.2 119.028336154753 -0.828336154753427 31 118.3 118.699390377359 -0.39939037735875 32 118.2 119.028336154753 -0.828336154753427 33 117.9 120.409908419811 -2.5099084198111 34 117.8 119.883595175980 -2.08359517597961 35 118.6 119.620438554064 -1.02043855406387 36 118.9 118.567812066401 0.332187933599133 37 120.8 119.817806020501 0.982193979499326 38 121.8 119.883595175980 1.91640482402039 39 121.3 119.686227709543 1.6137722904572 40 121.9 121.002010819122 0.897989180878478 41 122 121.923058995827 0.0769410041733562 42 121.9 121.659902373911 0.240097626089112 43 122 123.173052949926 -1.17305294992643 44 122.2 122.317793928700 -0.117793928700257 45 123 123.962522815674 -0.962522815673674 46 123.1 123.304631260884 -0.204631260884314 47 124.9 123.830944504716 1.06905549528421 48 125.4 123.830944504716 1.56905549528421 49 124.7 123.501998727321 1.19800127267889 50 124.4 124.357257748547 0.0427422514527056 51 124 124.028311971153 -0.0283119711526162 52 125 126.133564946479 -1.13356494647858 53 125.1 125.738830013605 -0.63883001360497 54 125.4 125.080938458816 0.319061541184407 55 125.7 126.001986635521 -0.301986635520711 56 126.4 126.199354101958 0.200645898042484 57 125.7 126.528299879352 -0.828299879352202 58 125.4 125.738830013605 -0.338830013604959 59 126.4 125.870408324563 0.529591675437166 60 126.2 124.686203525942 1.51379647405802 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0195362644833442 0.0390725289666883 0.980463735516656 6 0.05173656159903 0.10347312319806 0.94826343840097 7 0.0780953902916442 0.156190780583288 0.921904609708356 8 0.054324934150738 0.108649868301476 0.945675065849262 9 0.0327926149790801 0.0655852299581603 0.96720738502092 10 0.0200115439912606 0.0400230879825211 0.97998845600874 11 0.0613836176387709 0.122767235277542 0.93861638236123 12 0.112983961965066 0.225967923930131 0.887016038034934 13 0.168687431726963 0.337374863453926 0.831312568273037 14 0.174746682484302 0.349493364968603 0.825253317515698 15 0.136875352395110 0.273750704790221 0.86312464760489 16 0.139693489968808 0.279386979937615 0.860306510031192 17 0.152642189481908 0.305284378963816 0.847357810518092 18 0.222506338546302 0.445012677092604 0.777493661453698 19 0.199930170013143 0.399860340026285 0.800069829986857 20 0.193266342640506 0.386532685281011 0.806733657359494 21 0.148097906712475 0.296195813424950 0.851902093287525 22 0.148247853671253 0.296495707342505 0.851752146328747 23 0.225318968528242 0.450637937056484 0.774681031471758 24 0.462878467130596 0.925756934261193 0.537121532869403 25 0.673169083882918 0.653661832234163 0.326830916117082 26 0.811458807549348 0.377082384901305 0.188541192450652 27 0.770216398962216 0.459567202075568 0.229783601037784 28 0.801745150708722 0.396509698582557 0.198254849291278 29 0.763521311112327 0.472957377775347 0.236478688887673 30 0.734611203992040 0.530777592015919 0.265388796007960 31 0.676132412889455 0.64773517422109 0.323867587110545 32 0.641963648166018 0.716072703667964 0.358036351833982 33 0.858644473531241 0.282711052937517 0.141355526468759 34 0.961322784664548 0.077354430670905 0.0386772153354525 35 0.981519819718279 0.0369603605634424 0.0184801802817212 36 0.981212658421122 0.0375746831577553 0.0187873415788776 37 0.977813596298548 0.0443728074029046 0.0221864037014523 38 0.98456818501459 0.0308636299708178 0.0154318149854089 39 0.98486446438786 0.0302710712242785 0.0151355356121393 40 0.9781150105321 0.043769978935799 0.0218849894678995 41 0.96488780459413 0.0702243908117421 0.0351121954058710 42 0.944949107704325 0.110101784591351 0.0550508922956753 43 0.968383956677465 0.0632320866450709 0.0316160433225355 44 0.96666462470963 0.0666707505807414 0.0333353752903707 45 0.985334014098924 0.0293319718021527 0.0146659859010764 46 0.993358398773344 0.0132832024533128 0.00664160122665641 47 0.987640449581557 0.0247191008368867 0.0123595504184434 48 0.98544762472759 0.0291047505448200 0.0145523752724100 49 0.973831279987641 0.0523374400247172 0.0261687200123586 50 0.956247444088852 0.087505111822296 0.043752555911148 51 0.978109890576902 0.0437802188461959 0.0218901094230979 52 0.975992846601483 0.0480143067970333 0.0240071533985167 53 0.974473846932908 0.0510523061341836 0.0255261530670918 54 0.9604692676788 0.0790614646424012 0.0395307323212006 55 0.902038481895843 0.195923036208313 0.0979615181041567 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 14 0.274509803921569 NOK 10% type I error level 23 0.450980392156863 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/10lr4x1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/10lr4x1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/1b4ki1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/1b4ki1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/2u2k51258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/2u2k51258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/3uyzs1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/3uyzs1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/4qowa1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/4qowa1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/54ifv1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/54ifv1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/6m7pt1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/6m7pt1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/7yik81258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/7yik81258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/8xmrw1258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/8xmrw1258731548.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/9n2791258731548.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731595r4rmfuq6lu29899/9n2791258731548.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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