*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: Tue, 24 Nov 2009 12:58:11 -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/24/t1259093370tsyt3xm9zt39617.htm/, Retrieved Tue, 24 Nov 2009 21:09:42 +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/24/t1259093370tsyt3xm9zt39617.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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101.9 93.7 95.7 107.2 98.6 99.9 106.2 106.7 93.7 95.7 107.2 98.6 81 86.7 106.7 93.7 95.7 107.2 94.7 95.3 86.7 106.7 93.7 95.7 101 99.3 95.3 86.7 106.7 93.7 109.4 101.8 99.3 95.3 86.7 106.7 102.3 96 101.8 99.3 95.3 86.7 90.7 91.7 96 101.8 99.3 95.3 96.2 95.3 91.7 96 101.8 99.3 96.1 96.6 95.3 91.7 96 101.8 106 107.2 96.6 95.3 91.7 96 103.1 108 107.2 96.6 95.3 91.7 102 98.4 108 107.2 96.6 95.3 104.7 103.1 98.4 108 107.2 96.6 86 81.1 103.1 98.4 108 107.2 92.1 96.6 81.1 103.1 98.4 108 106.9 103.7 96.6 81.1 103.1 98.4 112.6 106.6 103.7 96.6 81.1 103.1 101.7 97.6 106.6 103.7 96.6 81.1 92 87.6 97.6 106.6 103.7 96.6 97.4 99.4 87.6 97.6 106.6 103.7 97 98.5 99.4 87.6 97.6 106.6 105.4 105.2 98.5 99.4 87.6 97.6 102.7 104.6 105.2 98.5 99.4 87.6 98.1 97.5 104.6 105.2 98.5 99.4 104.5 108.9 97.5 104.6 105.2 98.5 87.4 86.8 108.9 97.5 104.6 105.2 89.9 88.9 86.8 108.9 97.5 104.6 109.8 110.3 88.9 86.8 108.9 97.5 111.7 114.8 110.3 88.9 86.8 108.9 98.6 94.6 114.8 110.3 88.9 86.8 96.9 92 94.6 114.8 110 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 ProdInd[t] = -28.7635976658427 + 0.738532559514924ProdMetal[t] + 0.37676744914924`(t-1)`[t] + 0.108262815954371`(t-2)`[t] -0.0945254194298194`(t-3)`[t] + 0.191970290650688`(t-4)`[t] -5.51808609678788M1[t] + 0.0300456353639460M2[t] -6.57335079379142M3[t] -0.65804805502582M4[t] + 4.71602434640368M5[t] -3.84131584547569M6[t] -4.7749612891723M7[t] -4.41089498950224M8[t] + 1.15567643310007M9[t] -1.37956770121052M10[t] + 0.276501934329990M11[t] -0.119210985558996t + 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) -28.7635976658427 29.722414 -0.9677 0.339293 0.169647 ProdMetal 0.738532559514924 0.209236 3.5297 0.001108 0.000554 `(t-1)` 0.37676744914924 0.137695 2.7363 0.009396 0.004698 `(t-2)` 0.108262815954371 0.145371 0.7447 0.461015 0.230507 `(t-3)` -0.0945254194298194 0.150784 -0.6269 0.534478 0.267239 `(t-4)` 0.191970290650688 0.151111 1.2704 0.211668 0.105834 M1 -5.51808609678788 3.272756 -1.6861 0.099978 0.049989 M2 0.0300456353639460 3.651686 0.0082 0.993478 0.496739 M3 -6.57335079379142 4.723429 -1.3916 0.172122 0.086061 M4 -0.65804805502582 4.834733 -0.1361 0.892454 0.446227 M5 4.71602434640368 4.234953 1.1136 0.272447 0.136224 M6 -3.84131584547569 4.740351 -0.8103 0.422788 0.211394 M7 -4.7749612891723 3.445693 -1.3858 0.173895 0.086947 M8 -4.41089498950224 4.092285 -1.0779 0.287893 0.143946 M9 1.15567643310007 4.659351 0.248 0.805444 0.402722 M10 -1.37956770121052 4.258068 -0.324 0.747723 0.373861 M11 0.276501934329990 3.70153 0.0747 0.940846 0.470423 t -0.119210985558996 0.043095 -2.7663 0.008707 0.004353 Multiple Linear Regression - Regression Statistics Multiple R 0.911618262036502 R-squared 0.831047855678453 Adjusted R-squared 0.755464001639866 F-TEST (value) 10.9950447254805 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 7.0088868042717e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.18799275887399 Sum Squared Residuals 666.492767238477 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 93.7 98.3756175004954 -4.67561750049542 2 106.7 103.919190986287 2.78080901371347 3 86.7 86.0050011018663 0.694998898133672 4 95.3 93.7724490412261 1.52755095877387 5 99.3 103.142238291748 -3.84223829174768 6 101.8 107.493612795095 -5.69361279509457 7 96 97.9198206598634 -1.91982065986336 8 91.7 88.9559469402983 2.74405305970172 9 95.3 96.7487797048248 -1.44877970482480 10 96.6 95.9394771966569 0.660522803343128 11 107.2 104.960383624940 2.23961637505989 12 108 105.391639144435 2.60836085556484 13 98.4 100.959166056141 -2.5591660561413 14 103.1 104.099359386245 -0.99935938624504 15 81.1 86.2569418317945 -5.1569418317945 16 96.6 89.8390538107912 6.76094618920879 17 103.7 107.195126356733 -3.4951263567329 18 106.6 110.063152898296 -3.46315289829622 19 97.6 97.1330927706594 0.466907229340577 20 87.6 89.4416464085342 -1.84164640853419 21 99.4 95.2239081791498 4.17609182085024 22 98.5 97.0447103936469 1.45528960635309 23 105.2 104.941174646023 0.258825353976953 24 104.6 101.943226334606 2.65677366539401 25 97.5 95.7583021830602 1.74169781693984 26 108.9 102.367731160251 6.53226883974914 27 86.8 87.8856161038741 -1.08561610387412 28 88.9 88.991623035111 -0.0916230351109470 29 110.3 104.901306950830 5.39869304916957 30 114.8 110.195616044585 4.6043839554148 31 94.6 99.0392140640964 -4.43921406409636 32 92 89.2853378605804 2.71466213941961 33 93.8 93.919635272921 -0.119635272921060 34 93.8 95.8383698835275 -2.03836988352756 35 107.6 105.533029005985 2.06697099401513 36 101 102.429819290444 -1.42981929044368 37 95.4 92.0835013497213 3.31649865027871 38 96.5 103.722994840857 -7.22299484085696 39 89.2 82.3568362017204 6.84316379827958 40 87.1 91.7261591636401 -4.6261591636401 41 110.5 106.997092070422 3.50290792957818 42 110.8 105.004326444968 5.79567355503178 43 104.2 100.668362021947 3.53163797805325 44 88.9 94.1453737045506 -5.24537370455059 45 89.8 92.4076768431044 -2.60767684310438 46 90 90.0774425261687 -0.0774425261686619 47 93.9 98.465412723052 -4.56541272305198 48 91.3 95.1353152305152 -3.83531523051518 49 87.8 85.6234129105818 2.17658708941816 50 99.7 100.790723626361 -1.09072362636061 51 73.5 74.7956047607446 -1.29560476074463 52 79.2 82.7707149492316 -3.5707149492316 53 96.9 98.4642363302672 -1.56423633026717 54 95.2 96.4432918170558 -1.24329181705579 55 95.6 93.239510483434 2.36048951656589 56 89.7 88.0716950860366 1.62830491396345 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.139977468113135 0.279954936226271 0.860022531886865 22 0.0536311838861539 0.107262367772308 0.946368816113846 23 0.0560240939835708 0.112048187967142 0.94397590601643 24 0.070853040187893 0.141706080375786 0.929146959812107 25 0.0366194957169194 0.0732389914338388 0.96338050428308 26 0.0595845327433744 0.119169065486749 0.940415467256626 27 0.0582904022099199 0.116580804419840 0.94170959779008 28 0.160675269384022 0.321350538768045 0.839324730615978 29 0.24548872343958 0.49097744687916 0.75451127656042 30 0.292875574557891 0.585751149115782 0.707124425442109 31 0.224751868008774 0.449503736017548 0.775248131991226 32 0.310004456858406 0.620008913716812 0.689995543141594 33 0.445809408433609 0.891618816867218 0.554190591566391 34 0.368518475099896 0.737036950199792 0.631481524900104 35 0.320591608530602 0.641183217061205 0.679408391469398 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 1 0.0666666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/107nf61259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/107nf61259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/1bzbn1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/1bzbn1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/2cfs91259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/2cfs91259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/30pac1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/30pac1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/4j1s71259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/4j1s71259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/5l2wn1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/5l2wn1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/60e8b1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/60e8b1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/78owo1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/78owo1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/860pt1259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/860pt1259092686.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/90fa21259092686.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259093370tsyt3xm9zt39617/90fa21259092686.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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