*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 09:39:27 -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/t1258735261q6lw5v2ls5ce22b.htm/, Retrieved Fri, 20 Nov 2009 17:41:13 +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/t1258735261q6lw5v2ls5ce22b.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] = + 1588.51070846361 + 47.8206258852458`<25`[t] + 157.074438119669M1[t] -376.136315775409M2[t] -552.818466554426M3[t] -384.479991448196M4[t] -320.164477939016M5[t] -607.936139394426M6[t] -280.520714075738M7[t] -83.4001763809834M8[t] -245.166813650820M9[t] + 139.943499291803M10[t] -111.048788048852M11[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) 1588.51070846361 514.803878 3.0857 0.003399 0.0017 `<25` 47.8206258852458 26.44797 1.8081 0.076992 0.038496 M1 157.074438119669 205.479452 0.7644 0.448433 0.224216 M2 -376.136315775409 226.837256 -1.6582 0.103942 0.051971 M3 -552.818466554426 237.806522 -2.3247 0.024461 0.01223 M4 -384.479991448196 236.205694 -1.6277 0.110268 0.055134 M5 -320.164477939016 222.686254 -1.4377 0.157134 0.078567 M6 -607.936139394426 212.326307 -2.8632 0.006247 0.003123 M7 -280.520714075738 209.988888 -1.3359 0.18802 0.09401 M8 -83.4001763809834 208.761452 -0.3995 0.691335 0.345667 M9 -245.166813650820 207.511926 -1.1815 0.243364 0.121682 M10 139.943499291803 205.734609 0.6802 0.499706 0.249853 M11 -111.048788048852 205.299631 -0.5409 0.591124 0.295562 Multiple Linear Regression - Regression Statistics Multiple R 0.563892530113455 R-squared 0.317974785517753 Adjusted R-squared 0.143840688203137 F-TEST (value) 1.82603401873244 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0710475831667519 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 323.570134071516 Sum Squared Residuals 4920788.68816377 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2253 2458.11247227345 -205.112472273453 2 2218 2101.83803415377 116.161965846229 3 1855 1949.06619631738 -94.0661963173776 4 2187 2103.05848365803 83.9415163419666 5 1852 2138.68162163607 -286.681621636066 6 1570 1841.34583500361 -271.345835003607 7 1851 2216.58188620754 -365.581886207541 8 1954 2494.99748790721 -540.997487907213 9 1828 2357.14116358 -529.14116358 10 2251 2718.34116358 -467.34116358 11 2277 2414.74618776557 -137.746187765574 12 2085 2477.97434992918 -392.97434992918 13 2282 2639.83085063737 -357.830850637374 14 2266 2350.50528875705 -84.505288757049 15 1878 2226.42582645180 -348.425826451803 16 2267 2389.98223896951 -122.982238969508 17 2069 2406.47712659344 -337.477126593442 18 1746 2046.97452631016 -300.974526310164 19 2299 2345.69757609770 -46.6975760977046 20 2360 2494.99748790721 -134.997487907213 21 2214 2285.41022475213 -71.4102247521312 22 2825 2608.35372404393 216.646275956065 23 2355 2309.54081081803 45.459189181967 24 2333 2401.46134851279 -68.461348512787 25 3016 2606.35641251770 409.643587482298 26 2155 2350.50528875705 -195.505288757049 27 2172 2255.11820198295 -83.1182019829507 28 2150 2428.23873967770 -278.238739677705 29 2533 2401.69506400492 131.304935995082 30 2058 2032.62833854459 25.3716614554101 31 2160 2326.56932574361 -166.569325743606 32 2260 2494.99748790721 -234.997487907213 33 2498 2318.88466287180 179.115337128197 34 2695 2699.2129132259 -4.21291322590157 35 2799 2453.00268847377 345.99731152623 36 2946 2535.35910099148 410.640899008525 37 2930 2678.08735134557 251.912648654429 38 2318 2317.03085063738 0.969149362623003 39 2540 2159.47695021246 380.523049787541 40 2570 2327.81542531869 242.184574681312 41 2669 2363.43856329672 305.561436703279 42 2450 2027.84627595607 422.153724043935 43 2842 2336.13345092066 505.866549079345 44 3440 2485.43336273016 954.566637269836 45 2678 2309.32053769475 368.679462305246 46 2981 2656.17434992918 324.82565007082 47 2260 2395.61793741148 -135.617937411475 48 2844 2487.53847510623 356.46152489377 49 2546 2644.6129132259 -98.612913225899 50 2456 2293.12053769475 162.879462305246 51 2295 2149.91282503541 145.08717496459 52 2379 2303.90511237607 75.0948876239345 53 2479 2291.70762446885 187.292375531148 54 2057 1932.20502418557 124.794975814426 55 2280 2207.01776103049 72.982238969508 56 2351 2394.57417354820 -43.574173548197 57 2276 2223.24341110131 52.7565888986883 58 2548 2617.91784922098 -69.917849220984 59 2311 2429.09237553115 -118.092375531147 60 2201 2506.66672546033 -305.666725460328 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000214716387293338 0.000429432774586676 0.999785283612707 17 0.00232717015166516 0.00465434030333032 0.997672829848335 18 0.00104114817018728 0.00208229634037456 0.998958851829813 19 0.0200808937748702 0.0401617875497405 0.97991910622513 20 0.0492190508313813 0.0984381016627626 0.950780949168619 21 0.0785731497578985 0.157146299515797 0.921426850242101 22 0.183792130570613 0.367584261141226 0.816207869429387 23 0.120007924842556 0.240015849685111 0.879992075157444 24 0.0947686163042809 0.189537232608562 0.905231383695719 25 0.293217902021464 0.586435804042928 0.706782097978536 26 0.231232874242615 0.462465748485229 0.768767125757385 27 0.21115829706949 0.42231659413898 0.78884170293051 28 0.213708796075801 0.427417592151602 0.7862912039242 29 0.261336184548452 0.522672369096903 0.738663815451548 30 0.263148096164772 0.526296192329543 0.736851903835228 31 0.292776625508004 0.585553251016008 0.707223374491996 32 0.574536640985605 0.85092671802879 0.425463359014395 33 0.60896468451887 0.78207063096226 0.39103531548113 34 0.633568184690416 0.732863630619169 0.366431815309584 35 0.653803488475933 0.692393023048135 0.346196511524067 36 0.711078757004813 0.577842485990374 0.288921242995187 37 0.662528499908923 0.674943000182153 0.337471500091077 38 0.597257865018128 0.805484269963743 0.402742134981872 39 0.583799739661498 0.832400520677004 0.416200260338502 40 0.492164964223603 0.984329928447207 0.507835035776397 41 0.434644889643904 0.869289779287808 0.565355110356096 42 0.380174161688851 0.760348323377702 0.619825838311149 43 0.355233040364110 0.710466080728221 0.64476695963589 44 0.528980449001085 0.94203910199783 0.471019550998915 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.103448275862069 NOK 5% type I error level 4 0.137931034482759 NOK 10% type I error level 5 0.172413793103448 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/10tglf1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/10tglf1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/1hrl11258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/1hrl11258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/21xzq1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/21xzq1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/3a3mq1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/3a3mq1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/47u061258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/47u061258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/58qcr1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/58qcr1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/6gki61258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/6gki61258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/7srhb1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/7srhb1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/8bqdx1258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/8bqdx1258735161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/9csq31258735161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735261q6lw5v2ls5ce22b/9csq31258735161.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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