Model 1

*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: Sat, 19 Dec 2009 04:59:10 -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/Dec/19/t1261224020qhlj62wy2xp3ijv.htm/, Retrieved Sat, 19 Dec 2009 13:00:32 +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/Dec/19/t1261224020qhlj62wy2xp3ijv.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:

» Textbox « » Textfile « » CSV «

3016 0 2155 0 2172 0 2150 0 2533 0 2058 0 2160 0 2260 0 2498 0 2695 0 2799 0 2946 0 2930 0 2318 0 2540 0 2570 0 2669 0 2450 0 2842 0 3440 0 2678 0 2981 0 2260 0 2844 0 2546 0 2456 0 2295 0 2379 0 2479 0 2057 0 2280 0 2351 0 2276 0 2548 0 2311 1 2201 1 2725 1 2408 1 2139 1 1898 1 2537 1 2068 1 2063 1 2520 1 2434 1 2190 1 2794 1 2070 1 2615 1 2265 1 2139 1 2428 1 2137 1 1823 1 2063 1 1806 1 1758 1 2243 1 1993 1 1932 1 2465 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation y[t] = + 2518.55882352941 -295.410675381264`x `[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) 2518.55882352941 51.874454 48.551 0 0 `x ` -295.410675381264 77.971624 -3.7887 0.000358 0.000179 Multiple Linear Regression - Regression Statistics Multiple R 0.442361267607549 R-squared 0.195683491079357 Adjusted R-squared 0.182051007877313 F-TEST (value) 14.3542073868102 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.000357622887561249 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 302.477448095073 Sum Squared Residuals 5398063.78976035 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3016 2518.55882352941 497.441176470592 2 2155 2518.55882352941 -363.558823529412 3 2172 2518.55882352941 -346.558823529412 4 2150 2518.55882352941 -368.558823529412 5 2533 2518.55882352941 14.4411764705882 6 2058 2518.55882352941 -460.558823529412 7 2160 2518.55882352941 -358.558823529412 8 2260 2518.55882352941 -258.558823529412 9 2498 2518.55882352941 -20.5588235294118 10 2695 2518.55882352941 176.441176470588 11 2799 2518.55882352941 280.441176470588 12 2946 2518.55882352941 427.441176470588 13 2930 2518.55882352941 411.441176470588 14 2318 2518.55882352941 -200.558823529412 15 2540 2518.55882352941 21.4411764705882 16 2570 2518.55882352941 51.4411764705882 17 2669 2518.55882352941 150.441176470588 18 2450 2518.55882352941 -68.5588235294118 19 2842 2518.55882352941 323.441176470588 20 3440 2518.55882352941 921.441176470588 21 2678 2518.55882352941 159.441176470588 22 2981 2518.55882352941 462.441176470588 23 2260 2518.55882352941 -258.558823529412 24 2844 2518.55882352941 325.441176470588 25 2546 2518.55882352941 27.4411764705882 26 2456 2518.55882352941 -62.5588235294118 27 2295 2518.55882352941 -223.558823529412 28 2379 2518.55882352941 -139.558823529412 29 2479 2518.55882352941 -39.5588235294118 30 2057 2518.55882352941 -461.558823529412 31 2280 2518.55882352941 -238.558823529412 32 2351 2518.55882352941 -167.558823529412 33 2276 2518.55882352941 -242.558823529412 34 2548 2518.55882352941 29.4411764705882 35 2311 2223.14814814815 87.8518518518519 36 2201 2223.14814814815 -22.1481481481481 37 2725 2223.14814814815 501.851851851852 38 2408 2223.14814814815 184.851851851852 39 2139 2223.14814814815 -84.1481481481481 40 1898 2223.14814814815 -325.148148148148 41 2537 2223.14814814815 313.851851851852 42 2068 2223.14814814815 -155.148148148148 43 2063 2223.14814814815 -160.148148148148 44 2520 2223.14814814815 296.851851851852 45 2434 2223.14814814815 210.851851851852 46 2190 2223.14814814815 -33.1481481481481 47 2794 2223.14814814815 570.851851851852 48 2070 2223.14814814815 -153.148148148148 49 2615 2223.14814814815 391.851851851852 50 2265 2223.14814814815 41.8518518518519 51 2139 2223.14814814815 -84.1481481481481 52 2428 2223.14814814815 204.851851851852 53 2137 2223.14814814815 -86.1481481481481 54 1823 2223.14814814815 -400.148148148148 55 2063 2223.14814814815 -160.148148148148 56 1806 2223.14814814815 -417.148148148148 57 1758 2223.14814814815 -465.148148148148 58 2243 2223.14814814815 19.8518518518519 59 1993 2223.14814814815 -230.148148148148 60 1932 2223.14814814815 -291.148148148148 61 2465 2223.14814814815 241.851851851852 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.897254086706306 0.205491826587389 0.102745913293694 6 0.887993518090925 0.224012963818149 0.112006481909075 7 0.843686091836314 0.312627816327373 0.156313908163686 8 0.771129775201643 0.457740449596713 0.228870224798357 9 0.699938548038638 0.600122903922723 0.300061451961362 10 0.700887973938562 0.598224052122877 0.299112026061438 11 0.739248825574779 0.521502348850442 0.260751174425221 12 0.829691622840067 0.340616754319865 0.170308377159933 13 0.871922955149679 0.256154089700642 0.128077044850321 14 0.835821101256253 0.328357797487495 0.164178898743747 15 0.775863103059754 0.448273793880491 0.224136896940246 16 0.708580193796215 0.58283961240757 0.291419806203785 17 0.651656650522278 0.696686698955445 0.348343349477722 18 0.573456982883892 0.853086034232216 0.426543017116108 19 0.581417463044614 0.837165073910773 0.418582536955386 20 0.955747848229733 0.0885043035405338 0.0442521517702669 21 0.941511946221662 0.116976107556677 0.0584880537783384 22 0.968168839309013 0.0636623213819742 0.0318311606909871 23 0.962489256652405 0.0750214866951902 0.0375107433475951 24 0.970589318933028 0.0588213621339433 0.0294106810669717 25 0.95920772992364 0.0815845401527175 0.0407922700763588 26 0.942985359852004 0.114029280295991 0.0570146401479956 27 0.927929578584883 0.144140842830233 0.0720704214151166 28 0.903573264986565 0.192853470026871 0.0964267350134353 29 0.874702341033397 0.250595317933206 0.125297658966603 30 0.895437848463279 0.209124303073441 0.104562151536721 31 0.870589431947548 0.258821136104904 0.129410568052452 32 0.832927162880504 0.334145674238992 0.167072837119496 33 0.807253102386602 0.385493795226797 0.192746897613398 34 0.750462841156289 0.499074317687422 0.249537158843711 35 0.688952704147422 0.622094591705156 0.311047295852578 36 0.619335152526785 0.761329694946431 0.380664847473216 37 0.717507490413716 0.564985019172568 0.282492509586284 38 0.671213774655317 0.657572450689366 0.328786225344683 39 0.613700147509133 0.772599704981735 0.386299852490867 40 0.633169207040851 0.733661585918299 0.366830792959149 41 0.636777251381430 0.726445497237139 0.363222748618570 42 0.579389223021719 0.841221553956562 0.420610776978281 43 0.518361957814101 0.963276084371798 0.481638042185899 44 0.516536391247462 0.966927217505076 0.483463608752538 45 0.478548469648146 0.957096939296292 0.521451530351854 46 0.393090259388541 0.786180518777082 0.606909740611459 47 0.677399197894446 0.645201604211108 0.322600802105554 48 0.599760500858742 0.800478998282516 0.400239499141258 49 0.760240632649376 0.479518734701247 0.239759367350624 50 0.706025818289305 0.587948363421391 0.293974181710695 51 0.613529515008318 0.772940969983365 0.386470484991682 52 0.685309219909137 0.629381560181725 0.314690780090863 53 0.589266859673125 0.821466280653749 0.410733140326875 54 0.541313631442861 0.917372737114278 0.458686368557139 55 0.399706310488953 0.799412620977906 0.600293689511047 56 0.350222195711579 0.700444391423159 0.649777804288421 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 5 0.0961538461538462 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/10esie1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/10esie1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/1p5hs1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/1p5hs1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/2limo1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/2limo1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/3djvc1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/3djvc1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/4ygvo1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/4ygvo1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/5kx8v1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/5kx8v1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/65xrg1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/65xrg1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/7c8ge1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/7c8ge1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/8j7ls1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/8j7ls1261223944.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/9j1rd1261223944.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261224020qhlj62wy2xp3ijv/9j1rd1261223944.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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