*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 10:17:46 -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/t12587376636vbfyqaamgwxfg8.htm/, Retrieved Fri, 20 Nov 2009 18:21:16 +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/t12587376636vbfyqaamgwxfg8.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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2253 14.9 2218 18.6 1855 19.1 2187 18.8 1852 18.2 1570 18 1851 19 1954 20.7 1828 21.2 2251 20.7 2277 19.6 2085 18.6 2282 18.7 2266 23.8 1878 24.9 2267 24.8 2069 23.8 1746 22.3 2299 21.7 2360 20.7 2214 19.7 2825 18.4 2355 17.4 2333 17 3016 18 2155 23.8 2172 25.5 2150 25.6 2533 23.7 2058 22 2160 21.3 2260 20.7 2498 20.4 2695 20.3 2799 20.4 2946 19.8 2930 19.5 2318 23.1 2540 23.5 2570 23.5 2669 22.9 2450 21.9 2842 21.5 3440 20.5 2678 20.2 2981 19.4 2260 19.2 2844 18.8 2546 18.8 2456 22.6 2295 23.3 2379 23 2479 21.4 2057 19.9 2280 18.8 2351 18.6 2276 18.4 2548 18.6 2311 19.9 2201 19.2

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 wngb[t] = + 1681.39441980409 + 25.6802893156051`<25`[t] + 239.567189564934M1[t] -205.134354973182M2[t] -371.241281120370M3[t] -214.467917951953M4[t] -184.300659681619M5[t] -507.106189838661M6[t] -196.569557234498M7[t] -13.2281651345211M8[t] -189.659561461919M9[t] + 175.472311646427M10[t] -88.4135078262194M11[t] + 8.90827154945574t + 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) 1681.39441980409 447.084896 3.7608 0.000477 0.000239 `<25` 25.6802893156051 23.578262 1.0892 0.28176 0.14088 M1 239.567189564934 179.370945 1.3356 0.188252 0.094126 M2 -205.134354973182 201.199127 -1.0196 0.31327 0.156635 M3 -371.241281120370 211.048258 -1.759 0.085222 0.042611 M4 -214.467917951953 209.10242 -1.0257 0.310416 0.155208 M5 -184.300659681619 196.018195 -0.9402 0.352015 0.176007 M6 -507.106189838661 185.82202 -2.729 0.008969 0.004485 M7 -196.569557234498 183.297603 -1.0724 0.289132 0.144566 M8 -13.2281651345211 181.885959 -0.0727 0.942338 0.471169 M9 -189.659561461919 180.497621 -1.0508 0.298858 0.149429 M10 175.472311646427 178.65198 0.9822 0.331138 0.165569 M11 -88.4135078262194 178.147494 -0.4963 0.62205 0.311025 t 8.90827154945574 2.194401 4.0595 0.000189 9.5e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.705597659500331 R-squared 0.497868057092345 Adjusted R-squared 0.355961203661921 F-TEST (value) 3.50841446383311 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.000797409462064635 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 280.638436043883 Sum Squared Residuals 3622864.86211721 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2253 2312.50619172101 -59.506191721009 2 2218 1971.72998920007 246.270010799925 3 1855 1827.37147926014 27.628520739855 4 2187 1985.34902718334 201.650972816664 5 1852 2009.01638341376 -157.016383413763 6 1570 1689.98306694306 -119.983066943056 7 1851 2035.10826041228 -184.108260412278 8 1954 2271.01441589824 -317.01441589824 9 1828 2116.3314357781 -288.331435778101 10 2251 2477.5314357781 -226.531435778101 11 2277 2194.30556960774 82.6944303922565 12 2085 2265.94705966781 -180.947059667814 13 2282 2516.99054971376 -234.990549713764 14 2266 2212.16675223469 53.83324776531 15 1878 2083.21641588412 -205.216415884124 16 2267 2246.33002167044 20.6699783295644 17 2069 2259.72526217462 -190.725262174620 18 1746 1907.30756959363 -161.307569593627 19 2299 2211.34430015788 87.6556998421186 20 2360 2377.91367449171 -17.9136744917094 21 2214 2184.71026039816 29.2897396018383 22 2825 2525.36602894568 299.633971054322 23 2355 2244.70819170688 110.291808293119 24 2333 2331.75785535631 1.24214464368536 25 3016 2605.91360578631 410.08639421369 26 2155 2319.06601082816 -164.066010828159 27 2172 2205.52384806696 -33.5238480669555 28 2150 2373.77351171639 -223.773511716389 29 2533 2364.05649183653 168.943508163471 30 2058 2006.50274139241 51.4972586075861 31 2160 2307.97144302511 -147.971443025108 32 2260 2484.81293308518 -224.812933085178 33 2498 2309.58572151255 188.414278487446 34 2695 2681.05783723880 13.9421627612039 35 2799 2428.64831824717 370.351681752835 36 2946 2510.56192403348 435.438075966522 37 2930 2751.33329835319 178.666701646814 38 2318 2407.98906690070 -89.9890669007047 39 2540 2261.06252802921 278.937471970786 40 2570 2426.74416274709 143.255837252913 41 2669 2450.41151897751 218.588481022486 42 2450 2110.83397105432 339.166028945678 43 2842 2420.0067594817 421.993240518302 44 3440 2586.57613381553 853.423866184474 45 2678 2411.3489222429 266.651077757098 46 2981 2764.84483544822 216.155164551780 47 2260 2504.73122966191 -244.731229661908 48 2844 2591.78089331134 252.219106688658 49 2546 2840.25635442573 -294.256354425731 50 2456 2502.04818083637 -46.0481808363711 51 2295 2362.82572875956 -67.8257287595621 52 2379 2520.80327668275 -141.803276682753 53 2479 2518.79034359757 -39.7903435975746 54 2057 2166.37265101658 -109.372651016581 55 2280 2457.56923692303 -177.569236923033 56 2351 2644.68284270935 -293.682842709345 57 2276 2472.02366006828 -196.023660068282 58 2548 2851.19986258921 -303.199862589206 59 2311 2629.6066907763 -318.606690776301 60 2201 2708.95226763105 -507.952267631052 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0122714684640087 0.0245429369280174 0.987728531535991 18 0.00467562608789218 0.00935125217578435 0.995324373912108 19 0.00730777224341425 0.0146155444868285 0.992692227756586 20 0.00238398147275534 0.00476796294551067 0.997616018527245 21 0.000893201919487399 0.00178640383897480 0.999106798080513 22 0.000255973840357365 0.00051194768071473 0.999744026159643 23 0.00202214512579558 0.00404429025159117 0.997977854874204 24 0.000900160310208833 0.00180032062041767 0.999099839689791 25 0.00200439462415734 0.00400878924831469 0.997995605375843 26 0.0116893276450563 0.0233786552901126 0.988310672354944 27 0.00682241270253931 0.0136448254050786 0.99317758729746 28 0.0149191901277135 0.029838380255427 0.985080809872287 29 0.0134278246762566 0.0268556493525132 0.986572175323743 30 0.00864828109283326 0.0172965621856665 0.991351718907167 31 0.0179797122940492 0.0359594245880984 0.98202028770595 32 0.148337422636536 0.296674845273073 0.851662577363464 33 0.153466859354944 0.306933718709888 0.846533140645056 34 0.313335911794254 0.626671823588508 0.686664088205746 35 0.249999985497752 0.499999970995503 0.750000014502249 36 0.268308160898679 0.536616321797358 0.73169183910132 37 0.188521712988226 0.377043425976452 0.811478287011774 38 0.316994731609651 0.633989463219302 0.683005268390349 39 0.227374726726845 0.454749453453691 0.772625273273154 40 0.166681883524300 0.333363767048600 0.8333181164757 41 0.156795255271400 0.313590510542799 0.8432047447286 42 0.124309853424319 0.248619706848638 0.875690146575681 43 0.104058699430530 0.208117398861060 0.89594130056947 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.259259259259259 NOK 5% type I error level 15 0.555555555555556 NOK 10% type I error level 15 0.555555555555556 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/10nbc1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/10nbc1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/10nrmz1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/10nrmz1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/2a9zh1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/2a9zh1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/3f52i1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/3f52i1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/4zav51258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/4zav51258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/56pxt1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/56pxt1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/68zgw1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/68zgw1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/78h6q1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/78h6q1258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/8z6561258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/8z6561258737461.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/94xkn1258737461.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587376636vbfyqaamgwxfg8/94xkn1258737461.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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