w7

*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: Wed, 18 Nov 2009 14:12:39 -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/18/t1258578995t9ycn2u3sxdqafl.htm/, Retrieved Wed, 18 Nov 2009 22:16:47 +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/18/t1258578995t9ycn2u3sxdqafl.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:

W7

Dataseries X:

» Textbox « » Textfile « » CSV «

13132.1 12002.4 17665.9 15525.5 16913 14247.9 17318.8 15000.7 16224.2 14931.4 15469.6 13333.7 16557.5 14711.2 19414.8 17197.3 17335 14985.2 16525.2 14734.4 18160.4 15937.8 15553.8 13028.1 15262.2 13836.8 18581 16677.5 17564.1 15130 18948.6 17504 17187.8 16979.9 17564.8 16012.5 17668.4 16247.7 20811.7 19268.2 17257.8 15533 18984.2 16803.3 20532.6 17396.1 17082.3 15155.4 16894.9 15692.4 20274.9 18063.7 20078.6 17568.6 19900.9 18154.3 17012.2 15467.4 19642.9 16956.1 19024 16854 21691 19396.4 18835.9 16457.6 19873.4 17284.5 21468.2 18395.3 19406.8 16938.7 18385.3 16414.3 20739.3 18173.4 22268.3 19919.7 21569 19623.8 17514.8 17228.1 21124.7 18730.3 21251 19039.1 21393 19413.3 22145.2 20013.6 20310.5 17917.2 23466.9 21270.3 21264.6 18766.1 18388.1 16790.8 22635.4 19960.6 22014.3 19586.7 18422.7 17179 16120.2 14964.9 16037.7 13918.5 16410.7 14401.3 17749.8 15994.6 16349.8 14521.1 15662.3 13746.5 17782.3 15956 16398.9 14332.2

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Uitvoer[t] = + 1802.9519805538 + 1.03159197521405Invoer[t] -809.987975349867M1[t] -62.3425252148233M2[t] + 127.884444649563M3[t] -615.930184109128M4[t] -1408.21741538185M5[t] -124.076219418276M6[t] -384.682428485835M7[t] -421.530632432152M8[t] -235.327519690847M9[t] -137.553692129983M10[t] + 125.972029215426M11[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) 1802.9519805538 473.01649 3.8116 0.000401 2e-04 Invoer 1.03159197521405 0.028185 36.6013 0 0 M1 -809.987975349867 243.004078 -3.3332 0.00168 0.00084 M2 -62.3425252148233 248.914356 -0.2505 0.803325 0.401663 M3 127.884444649563 246.61465 0.5186 0.606501 0.30325 M4 -615.930184109128 247.747753 -2.4861 0.016526 0.008263 M5 -1408.21741538185 242.32901 -5.8112 1e-06 0 M6 -124.076219418276 242.244292 -0.5122 0.610913 0.305457 M7 -384.682428485835 242.811854 -1.5843 0.119836 0.059918 M8 -421.530632432152 253.132515 -1.6653 0.102515 0.051257 M9 -235.327519690847 242.918236 -0.9688 0.337628 0.168814 M10 -137.553692129983 242.545677 -0.5671 0.573328 0.286664 M11 125.972029215426 249.653885 0.5046 0.616207 0.308103 Multiple Linear Regression - Regression Statistics Multiple R 0.988229087160556 R-squared 0.976596728710185 Adjusted R-squared 0.970621425402148 F-TEST (value) 163.438854626258 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 382.966501248118 Sum Squared Residuals 6893177.03067656 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13132.1 13374.5435285132 -242.443528513171 2 17665.9 17756.5906665248 -90.6906665247915 3 16913 16628.8557288557 284.144271144301 4 17318.8 16661.6235390381 657.17646096185 5 16224.2 15797.8469838831 426.353016116904 6 15469.6 15433.8136810472 35.7863189528278 7 16557.5 16594.2254178370 -36.7254178369717 8 19414.8 19122.0180234703 292.781976529684 9 17335 17026.2365278406 308.763472159387 10 16525.2 16865.2870880178 -340.08708801779 11 18160.4 18370.2305923358 -209.830592335791 12 15553.8 15242.6353928400 311.164607159966 13 15262.2 15266.8958478458 -4.69584784577068 14 18581 18944.9846219714 -363.98462197138 15 17564.1 17538.8230101920 25.2769898079814 16 18948.6 19244.0077305915 -295.407730591493 17 17187.8 17911.0631451091 -723.263145109091 18 17564.8 18197.2422642506 -632.442264250582 19 17668.4 18179.2664877534 -510.866487753367 20 20811.7 21258.3418449411 -446.641844941102 21 17257.8 17591.3426118629 -333.542611862872 22 18984.2 18999.5477255381 -15.3477255381472 23 20532.6 19874.6011697904 657.99883020955 24 17082.3 17437.1410017129 -354.841001712891 25 16894.9 17181.1179170530 -286.21791705297 26 20274.9 20374.9774180131 -100.077418013102 27 20078.6 20054.463200949 24.1367990509895 28 19900.9 19914.8519920732 -13.9519920731896 29 17012.2 16350.7802825978 661.41971740217 30 19642.9 19170.6524520626 472.24754793744 31 19024 18804.7207023256 219.279297674351 32 21691 21390.5919361635 300.408063836454 33 18835.9 18545.1525521458 290.747447854217 34 19873.4 19495.9497840112 377.450215988850 35 21468.2 20905.3678714243 562.832128575668 36 19406.8 19276.7789711121 130.021028887884 37 18385.3 17925.82416396 459.475836040002 38 20739.3 20488.1430576941 251.156942305913 39 22268.3 22479.8390938748 -211.539093874776 40 21569 21430.7763996502 138.223600349756 41 17514.8 18167.1042733572 -652.304273357216 42 21124.7 21000.9029344873 123.797065512663 43 21251 21058.8523273659 192.147672634122 44 21393 21408.0258405447 -15.0258405446610 45 22145.2 22213.4936160070 -68.2936160069619 46 20310.5 20148.6380267291 161.861973270915 47 23466.9 23871.1948001647 -404.294800164737 48 21264.6 21161.9101466183 102.689853381722 49 18388.1 18314.2185426281 73.88145737191 50 22635.4 22331.8042357966 303.595764203360 51 22014.3 22136.3189661285 -122.018966128496 52 18422.7 18908.7403386469 -486.040338646923 53 16120.2 15832.4053150528 287.794684947233 54 16037.7 16037.0886681523 0.61133184765021 55 16410.7 16274.5350647181 136.164935281864 56 17749.8 17881.3223548804 -131.522354880374 57 16349.8 16547.4746921438 -197.674692143770 58 15662.3 15846.1773757038 -183.877375703828 59 17782.3 18389.0055662847 -606.70556628469 60 16398.9 16587.9344877167 -189.034487716680 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.371876697028055 0.74375339405611 0.628123302971945 17 0.458816162016218 0.917632324032436 0.541183837983782 18 0.369672050152674 0.739344100305348 0.630327949847326 19 0.282422952612030 0.564845905224061 0.71757704738797 20 0.208815921803813 0.417631843607627 0.791184078196187 21 0.21349546281487 0.42699092562974 0.78650453718513 22 0.402548141611796 0.805096283223592 0.597451858388204 23 0.781336670735136 0.437326658529728 0.218663329264864 24 0.722744645167773 0.554510709664454 0.277255354832227 25 0.716135047250386 0.567729905499228 0.283864952749614 26 0.700718902160943 0.598562195678113 0.299281097839057 27 0.64388927708609 0.712221445827819 0.356110722913909 28 0.554344585858597 0.891310828282806 0.445655414141403 29 0.751992132415124 0.496015735169753 0.248007867584876 30 0.858550023771955 0.282899952456091 0.141449976228046 31 0.837026835669996 0.325946328660008 0.162973164330004 32 0.81272066513215 0.3745586697357 0.18727933486785 33 0.799690537039796 0.400618925920409 0.200309462960204 34 0.792860882444976 0.414278235110047 0.207139117555024 35 0.964341834384814 0.0713163312303713 0.0356581656151857 36 0.942665043852343 0.114669912295315 0.0573349561476574 37 0.944411517107794 0.111176965784411 0.0555884828922057 38 0.91019227091938 0.17961545816124 0.08980772908062 39 0.855339086104105 0.289321827791789 0.144660913895895 40 0.862450639918233 0.275098720163535 0.137549360081767 41 0.99980129881764 0.000397402364718438 0.000198701182359219 42 0.999030853944092 0.00193829211181541 0.000969146055907706 43 0.997419419807682 0.00516116038463536 0.00258058019231768 44 0.986063723807241 0.0278725523855174 0.0139362761927587 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/18/t1258578995t9ycn2u3sxdqafl/108xhn1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/108xhn1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/199fn1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/199fn1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/28p3o1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/28p3o1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/32zjy1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/32zjy1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/4ml4o1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/4ml4o1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/5eh661258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/5eh661258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/6uhzj1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/6uhzj1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/7pezo1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/7pezo1258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/8qgr21258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/8qgr21258578755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/9glel1258578755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258578995t9ycn2u3sxdqafl/9glel1258578755.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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