Multiple Linear Regression

*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: Mon, 22 Dec 2008 10:58:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/22/t1229969230fusp510in7ezgcv.htm/, Retrieved Mon, 22 Dec 2008 19:07:20 +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/2008/Dec/22/t1229969230fusp510in7ezgcv.htm/}, year = {2008}, } @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 = {2008}, 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 «

101.02 0 100.67 0 100.47 0 100.38 0 100.33 0 100.34 0 100.37 0 100.39 0 100.21 0 100.21 0 100.22 0 100.28 0 100.25 0 100.25 0 100.21 0 100.16 0 100.18 0 100.1 1 99.96 1 99.88 1 99.88 1 99.86 1 99.84 1 99.8 1 99.82 1 99.81 1 99.92 1 100.03 1 99.99 1 100.02 1 100.01 1 100.13 1 100.33 1 100.13 1 99.96 1 100.05 1 99.83 1 99.8 1 100.01 1 100.1 1 100.13 1 100.16 1 100.41 1 101.34 1 101.65 1 101.85 1 102.07 1 102.12 1 102.14 1 102.21 1 102.28 1 102.19 1 102.33 1 102.54 1 102.44 1 102.78 1 102.9 1 103.08 1 102.77 1 102.65 1 102.71 1 103.29 1 102.86 1 103.45 1 103.72 1 103.65 1 103.83 1 104.45 1 105.14 1 105.07 1 105.31 1 105.19 1 105.3 1 105.02 1 105.17 1 105.28 1 105.45 1 105.38 1 105.8 1 105.96 1 105.08 1 105.11 1 105.61 1 105.5 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Suiker[t] = + 99.4132576769026 -2.44007113962896dummie[t] + 0.104017120867038t + 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) 99.4132576769026 0.148892 667.6885 0 0 dummie -2.44007113962896 0.226128 -10.7907 0 0 t 0.104017120867038 0.003747 27.7604 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.958334489981909 R-squared 0.918404994688885 Adjusted R-squared 0.916390303199722 F-TEST (value) 455.853910948063 F-TEST (DF numerator) 2 F-TEST (DF denominator) 81 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.597942876975073 Sum Squared Residuals 28.9603904141434 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.02 99.5172747977695 1.50272520223053 2 100.67 99.6212919186366 1.04870808136339 3 100.47 99.7253090395037 0.744690960496348 4 100.38 99.8293261603707 0.550673839629299 5 100.33 99.9333432812377 0.396656718762264 6 100.34 100.037360402105 0.302639597895230 7 100.37 100.141377522972 0.228622477028193 8 100.39 100.245394643839 0.144605356161151 9 100.21 100.349411764706 -0.139411764705895 10 100.21 100.453428885573 -0.243428885572933 11 100.22 100.55744600644 -0.337446006439966 12 100.28 100.661463127307 -0.381463127307002 13 100.25 100.765480248174 -0.515480248174042 14 100.25 100.869497369041 -0.61949736904108 15 100.21 100.973514489908 -0.763514489908125 16 100.16 101.077531610775 -0.91753161077516 17 100.18 101.181548731642 -1.00154873164219 18 100.1 98.8454947128803 1.25450528711972 19 99.96 98.9495118337473 1.01048816625269 20 99.88 99.0535289546144 0.82647104538565 21 99.88 99.1575460754814 0.722453924518611 22 99.86 99.2615631963484 0.598436803651576 23 99.84 99.3655803172155 0.474419682784542 24 99.8 99.4695974380825 0.330402561917497 25 99.82 99.5736145589495 0.246385441050455 26 99.81 99.6776316798166 0.132368320183426 27 99.92 99.7816488006836 0.138351199316387 28 100.03 99.8856659215507 0.144334078449348 29 99.99 99.9896830424177 0.000316957582303101 30 100.02 100.093700163285 -0.0737001632847341 31 100.01 100.197717284152 -0.187717284151763 32 100.13 100.301734405019 -0.171734405018812 33 100.33 100.405751525886 -0.075751525885847 34 100.13 100.509768646753 -0.379768646752888 35 99.96 100.61378576762 -0.653785767619928 36 100.05 100.717802888487 -0.667802888486963 37 99.83 100.821820009354 -0.991820009354 38 99.8 100.925837130221 -1.12583713022104 39 100.01 101.029854251088 -1.01985425108807 40 100.1 101.133871371955 -1.03387137195512 41 100.13 101.237888492822 -1.10788849282216 42 100.16 101.341905613689 -1.18190561368919 43 100.41 101.445922734556 -1.03592273455623 44 101.34 101.549939855423 -0.209939855423264 45 101.65 101.653956976290 -0.00395697629030009 46 101.85 101.757974097157 0.0920259028426502 47 102.07 101.861991218024 0.208008781975611 48 102.12 101.966008338891 0.153991661108584 49 102.14 102.070025459758 0.0699745402415413 50 102.21 102.174042580625 0.0359574193744960 51 102.28 102.278059701493 0.00194029850746505 52 102.19 102.382076822360 -0.192076822359577 53 102.33 102.486093943227 -0.156093943226615 54 102.54 102.590111064094 -0.050111064093645 55 102.44 102.694128184961 -0.254128184960692 56 102.78 102.798145305828 -0.0181453058277269 57 102.9 102.902162426695 -0.00216242669476072 58 103.08 103.006179547562 0.0738204524381934 59 102.77 103.110196668429 -0.340196668428847 60 102.65 103.214213789296 -0.564213789295876 61 102.71 103.318230910163 -0.608230910162926 62 103.29 103.42224803103 -0.132248031029952 63 102.86 103.526265151897 -0.666265151896997 64 103.45 103.630282272764 -0.180282272764033 65 103.72 103.734299393631 -0.0142993936310746 66 103.65 103.838316514498 -0.188316514498106 67 103.83 103.942333635365 -0.112333635365152 68 104.45 104.046350756232 0.403649243767814 69 105.14 104.150367877099 0.989632122900773 70 105.07 104.254384997966 0.815615002033727 71 105.31 104.358402118833 0.951597881166698 72 105.19 104.462419239700 0.727580760299655 73 105.3 104.566436360567 0.733563639432616 74 105.02 104.670453481434 0.349546518565576 75 105.17 104.774470602301 0.395529397698544 76 105.28 104.878487723168 0.401512276831505 77 105.45 104.982504844036 0.467495155964468 78 105.38 105.086521964903 0.293478035097423 79 105.8 105.190539085770 0.609460914230386 80 105.96 105.294556206637 0.665443793363344 81 105.08 105.398573327504 -0.318573327503690 82 105.11 105.502590448371 -0.392590448370727 83 105.61 105.606607569238 0.00339243076223475 84 105.5 105.710624690105 -0.210624690104803 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0287637850952882 0.0575275701905764 0.971236214904712 7 0.0166795319629144 0.0333590639258287 0.983320468037086 8 0.00949006321659528 0.0189801264331906 0.990509936783405 9 0.00281317335631635 0.00562634671263269 0.997186826643684 10 0.000873297255353614 0.00174659451070723 0.999126702744646 11 0.000305468498901160 0.000610936997802319 0.999694531501099 12 0.000158405036329251 0.000316810072658501 0.99984159496367 13 6.50095542355928e-05 0.000130019108471186 0.999934990445764 14 2.64655722464999e-05 5.29311444929998e-05 0.999973534427753 15 8.80187542900342e-06 1.76037508580068e-05 0.999991198124571 16 2.47318068398553e-06 4.94636136797106e-06 0.999997526819316 17 7.74513637983662e-07 1.54902727596732e-06 0.999999225486362 18 3.52265030854663e-07 7.04530061709326e-07 0.99999964773497 19 1.73666481155338e-07 3.47332962310675e-07 0.999999826333519 20 8.54368005517826e-08 1.70873601103565e-07 0.9999999145632 21 3.71205994572043e-08 7.42411989144086e-08 0.9999999628794 22 1.56305650478694e-08 3.12611300957388e-08 0.999999984369435 23 6.37572388801722e-09 1.27514477760344e-08 0.999999993624276 24 2.41752392539292e-09 4.83504785078584e-09 0.999999997582476 25 9.674021671726e-10 1.9348043343452e-09 0.999999999032598 26 3.83843495423785e-10 7.67686990847571e-10 0.999999999616156 27 4.57980842292158e-10 9.15961684584315e-10 0.99999999954202 28 2.33124801924041e-09 4.66249603848083e-09 0.999999997668752 29 4.09817397264892e-09 8.19634794529783e-09 0.999999995901826 30 7.75305006024299e-09 1.55061001204860e-08 0.99999999224695 31 9.61847391001265e-09 1.92369478200253e-08 0.999999990381526 32 3.19208078652028e-08 6.38416157304057e-08 0.999999968079192 33 5.65384121859292e-07 1.13076824371858e-06 0.999999434615878 34 5.22441373224131e-07 1.04488274644826e-06 0.999999477558627 35 2.12444803416276e-07 4.24889606832553e-07 0.999999787555197 36 1.03900684513796e-07 2.07801369027592e-07 0.999999896099315 37 4.9236337479875e-08 9.847267495975e-08 0.999999950763663 38 3.08506150731304e-08 6.17012301462607e-08 0.999999969149385 39 2.08173434928627e-08 4.16346869857254e-08 0.999999979182656 40 2.29461019837440e-08 4.58922039674879e-08 0.999999977053898 41 3.91790071806859e-08 7.83580143613717e-08 0.999999960820993 42 1.29199100701209e-07 2.58398201402418e-07 0.9999998708009 43 1.71722933850841e-06 3.43445867701683e-06 0.999998282770661 44 0.00436852076728915 0.00873704153457831 0.99563147923271 45 0.106842554939729 0.213685109879459 0.893157445060271 46 0.367558358093725 0.73511671618745 0.632441641906275 47 0.639973046174159 0.720053907651682 0.360026953825841 48 0.776040730984945 0.44791853803011 0.223959269015055 49 0.831812829637285 0.336374340725429 0.168187170362715 50 0.857820611502719 0.284358776994562 0.142179388497281 51 0.86711171863821 0.265776562723578 0.132888281361789 52 0.851567789651852 0.296864420696295 0.148432210348148 53 0.833986567110161 0.332026865779677 0.166013432889839 54 0.820484119009788 0.359031761980423 0.179515880990212 55 0.788843995450144 0.422312009099712 0.211156004549856 56 0.767671152351559 0.464657695296882 0.232328847648441 57 0.742041570797795 0.515916858404411 0.257958429202205 58 0.720062153426443 0.559875693147113 0.279937846573557 59 0.672220065700781 0.655559868598438 0.327779934299219 60 0.660116551571286 0.679766896857428 0.339883448428714 61 0.688846192989867 0.622307614020267 0.311153807010133 62 0.65863753933849 0.68272492132302 0.34136246066151 63 0.789270352164121 0.421459295671758 0.210729647835879 64 0.823506037040025 0.35298792591995 0.176493962959975 65 0.849425466537057 0.301149066925885 0.150574533462943 66 0.936612368103822 0.126775263792355 0.0633876318961777 67 0.993128931481532 0.0137421370369355 0.00687106851846774 68 0.9979323365699 0.00413532686019816 0.00206766343009908 69 0.997229221130378 0.00554155773924447 0.00277077886962223 70 0.995568321539522 0.00886335692095556 0.00443167846047778 71 0.992933847284498 0.0141323054310042 0.00706615271550211 72 0.986340819085302 0.0273183618293961 0.0136591809146981 73 0.974215373862854 0.0515692522742928 0.0257846261371464 74 0.959169105120563 0.0816617897588734 0.0408308948794367 75 0.931623035984738 0.136753928030524 0.0683769640152621 76 0.885163829449365 0.22967234110127 0.114836170550635 77 0.79401261492615 0.411974770147699 0.205987385073849 78 0.687059003146804 0.625881993706392 0.312940996853196 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.534246575342466 NOK 5% type I error level 44 0.602739726027397 NOK 10% type I error level 47 0.643835616438356 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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