*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 11:00:29 -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/t12587400749w3w2bhhgvqrqmo.htm/, Retrieved Fri, 20 Nov 2009 19:01:26 +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/t12587400749w3w2bhhgvqrqmo.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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6802.96 0 7132.68 0 7073.29 0 7264.5 0 7105.33 0 7218.71 0 7225.72 0 7354.25 0 7745.46 0 8070.26 0 8366.33 0 8667.51 0 8854.34 0 9218.1 0 9332.9 0 9358.31 0 9248.66 0 9401.2 0 9652.04 0 9957.38 0 10110.63 0 10169.26 0 10343.78 0 10750.21 0 11337.5 0 11786.96 0 12083.04 0 12007.74 0 11745.93 0 11051.51 0 11445.9 0 11924.88 0 12247.63 0 12690.91 0 12910.7 0 13202.12 0 13654.67 0 13862.82 0 13523.93 0 14211.17 0 14510.35 0 14289.23 0 14111.82 0 13086.59 0 13351.54 0 13747.69 0 12855.61 0 12926.93 0 12121.95 1 11731.65 1 11639.51 1 12163.78 1 12029.53 1 11234.18 1 9852.13 1 9709.04 1 9332.75 1 7108.6 1 6691.49 1 6143.05 1

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 10728.97875 -749.17375X[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) 10728.97875 345.726271 31.0332 0 0 X -749.17375 773.067444 -0.9691 0.336523 0.168262 Multiple Linear Regression - Regression Statistics Multiple R 0.126230200070555 R-squared 0.0159340634098522 Adjusted R-squared -0.00103259066928807 F-TEST (value) 0.939139993986336 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.336523066256252 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2395.2618673994 Sum Squared Residuals 332762205.978225 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6802.96 10728.97875 -3926.01875000001 2 7132.68 10728.97875 -3596.29875 3 7073.29 10728.97875 -3655.68875 4 7264.5 10728.97875 -3464.47875 5 7105.33 10728.97875 -3623.64875 6 7218.71 10728.97875 -3510.26875 7 7225.72 10728.97875 -3503.25875 8 7354.25 10728.97875 -3374.72875 9 7745.46 10728.97875 -2983.51875 10 8070.26 10728.97875 -2658.71875 11 8366.33 10728.97875 -2362.64875 12 8667.51 10728.97875 -2061.46875 13 8854.34 10728.97875 -1874.63875 14 9218.1 10728.97875 -1510.87875 15 9332.9 10728.97875 -1396.07875 16 9358.31 10728.97875 -1370.66875 17 9248.66 10728.97875 -1480.31875 18 9401.2 10728.97875 -1327.77875 19 9652.04 10728.97875 -1076.93875 20 9957.38 10728.97875 -771.59875 21 10110.63 10728.97875 -618.34875 22 10169.26 10728.97875 -559.71875 23 10343.78 10728.97875 -385.198749999999 24 10750.21 10728.97875 21.2312499999993 25 11337.5 10728.97875 608.52125 26 11786.96 10728.97875 1057.98125 27 12083.04 10728.97875 1354.06125 28 12007.74 10728.97875 1278.76125 29 11745.93 10728.97875 1016.95125 30 11051.51 10728.97875 322.531250000000 31 11445.9 10728.97875 716.92125 32 11924.88 10728.97875 1195.90125 33 12247.63 10728.97875 1518.65125 34 12690.91 10728.97875 1961.93125 35 12910.7 10728.97875 2181.72125 36 13202.12 10728.97875 2473.14125 37 13654.67 10728.97875 2925.69125 38 13862.82 10728.97875 3133.84125 39 13523.93 10728.97875 2794.95125 40 14211.17 10728.97875 3482.19125 41 14510.35 10728.97875 3781.37125 42 14289.23 10728.97875 3560.25125 43 14111.82 10728.97875 3382.84125 44 13086.59 10728.97875 2357.61125 45 13351.54 10728.97875 2622.56125 46 13747.69 10728.97875 3018.71125 47 12855.61 10728.97875 2126.63125 48 12926.93 10728.97875 2197.95125 49 12121.95 9979.805 2142.145 50 11731.65 9979.805 1751.845 51 11639.51 9979.805 1659.705 52 12163.78 9979.805 2183.975 53 12029.53 9979.805 2049.725 54 11234.18 9979.805 1254.375 55 9852.13 9979.805 -127.675000000001 56 9709.04 9979.805 -270.764999999999 57 9332.75 9979.805 -647.055 58 7108.6 9979.805 -2871.205 59 6691.49 9979.805 -3288.315 60 6143.05 9979.805 -3836.755 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000960587826757566 0.00192117565351513 0.999039412173242 6 0.000101864659875499 0.000203729319750998 0.999898135340125 7 1.09268826022719e-05 2.18537652045439e-05 0.999989073117398 8 2.27519999069831e-06 4.55039998139663e-06 0.99999772480001 9 6.4677403364485e-06 1.2935480672897e-05 0.999993532259664 10 2.56278152715428e-05 5.12556305430856e-05 0.999974372184729 11 8.90142765236393e-05 0.000178028553047279 0.999910985723476 12 0.000289220754086141 0.000578441508172283 0.999710779245914 13 0.000696414428953668 0.00139282885790734 0.999303585571046 14 0.00197328030622231 0.00394656061244462 0.998026719693778 15 0.00408799934897584 0.00817599869795168 0.995912000651024 16 0.00666998816818566 0.0133399763363713 0.993330011831814 17 0.00886639639222972 0.0177327927844594 0.99113360360777 18 0.0125717859091867 0.0251435718183734 0.987428214090813 19 0.019766749055624 0.039533498111248 0.980233250944376 20 0.0339033236501712 0.0678066473003425 0.966096676349829 21 0.0558062259298641 0.111612451859728 0.944193774070136 22 0.085499764121259 0.170999528242518 0.914500235878741 23 0.128380969620708 0.256761939241416 0.871619030379292 24 0.195815395336483 0.391630790672965 0.804184604663517 25 0.301110145926606 0.602220291853211 0.698889854073395 26 0.42772991388162 0.85545982776324 0.57227008611838 27 0.545829480886135 0.90834103822773 0.454170519113865 28 0.622677697930094 0.754644604139812 0.377322302069906 29 0.666539047120085 0.66692190575983 0.333460952879915 30 0.700354580536259 0.599290838927482 0.299645419463741 31 0.732383532438455 0.535232935123091 0.267616467561545 32 0.761695356833441 0.476609286333118 0.238304643166559 33 0.786736917937634 0.426526164124732 0.213263082062366 34 0.809164786410899 0.381670427178202 0.190835213589101 35 0.82501441455207 0.349971170895862 0.174985585447931 36 0.83683781179663 0.32632437640674 0.16316218820337 37 0.849820926228696 0.300358147542607 0.150179073771304 38 0.858127115715658 0.283745768568684 0.141872884284342 39 0.849950668409654 0.300098663180691 0.150049331590346 40 0.852864459559895 0.29427108088021 0.147135540440105 41 0.859141491115126 0.281717017769749 0.140858508884874 42 0.852139951008959 0.295720097982082 0.147860048991041 43 0.835292630504561 0.329414738990878 0.164707369495439 44 0.789913753846099 0.420172492307802 0.210086246153901 45 0.738533116879262 0.522933766241477 0.261466883120738 46 0.689700000686946 0.620599998626107 0.310299999313054 47 0.612328247231261 0.775343505537478 0.387671752768739 48 0.527239655565247 0.945520688869507 0.472760344434753 49 0.496008307959512 0.992016615919025 0.503991692040488 50 0.45152850079882 0.90305700159764 0.54847149920118 51 0.413508365455171 0.827016730910342 0.586491634544829 52 0.450320487177232 0.900640974354464 0.549679512822768 53 0.545024455405107 0.909951089189787 0.454975544594893 54 0.62758139581144 0.74483720837712 0.37241860418856 55 0.582992188906159 0.834015622187683 0.417007811093841 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.215686274509804 NOK 5% type I error level 15 0.294117647058824 NOK 10% type I error level 16 0.313725490196078 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/10fhfn1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/10fhfn1258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/17d4d1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/17d4d1258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/20er31258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/20er31258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/36j311258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/36j311258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/4icb01258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/4icb01258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/5b92e1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/5b92e1258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/6tx781258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/6tx781258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/7ngbs1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/7ngbs1258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/8rsro1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/8rsro1258740020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/9k8of1258740020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587400749w3w2bhhgvqrqmo/9k8of1258740020.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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