Paper Multiple Lineair 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: Wed, 22 Dec 2010 08:59:09 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293008258wo6e8ki5h0un9ii.htm/, Retrieved Wed, 22 Dec 2010 09:57:52 +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/2010/Dec/22/t1293008258wo6e8ki5h0un9ii.htm/}, year = {2010}, } @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 = {2010}, 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 «

97.06 21.454 631.923 130.678 97.73 23.899 654.294 120.877 98 24.939 671.833 137.114 97.76 23.580 586.840 134.406 97.48 24.562 600.969 120.262 97.77 24.696 625.568 130.846 97.96 23.785 558.110 120.343 98.22 23.812 630.577 98.881 98.51 21.917 628.654 115.678 98.19 19.713 603.184 120.796 98.37 19.282 656.255 94.261 98.31 18.788 600.730 89.151 98.6 21.453 670.326 119.880 98.96 24.482 678.423 131.468 99.11 27.474 641.502 155.089 99.64 27.264 625.311 149.581 100.02 27.349 628.177 122.788 99.98 30.632 589.767 143.900 100.32 29.429 582.471 112.115 100.44 30.084 636.248 109.600 100.51 26.290 599.885 117.446 101 24.379 621.694 118.456 100.88 23.335 637.406 101.901 100.55 21.346 595.994 89.940 100.82 21.106 696.308 129.143 101.5 24.514 674.201 126.102 102.15 28.353 648.861 143.048 102.39 30.805 649.605 142.258 102.54 31.348 672.392 131.011 102.85 34.556 598.396 146.471 103.47 33.855 613.177 114.073 103.56 34.787 638.104 114.642 103.69 32.529 615.632 118.226 103.49 29.998 634.465 111.338 etc...

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation vacatures[t] = -93.4251087813653 + 1.51550935451971CPI[t] -0.070592650360014werklozen[t] + 0.110563161993499inschrijvingen[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) -93.4251087813653 10.189264 -9.169 0 0 CPI 1.51550935451971 0.088781 17.0702 0 0 werklozen -0.070592650360014 0.010499 -6.7238 0 0 inschrijvingen 0.110563161993499 0.021569 5.1261 2e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.894854068958925 R-squared 0.800763804732344 Adjusted R-squared 0.793292447409807 F-TEST (value) 107.177821935630 F-TEST (DF numerator) 3 F-TEST (DF denominator) 80 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.82827247699823 Sum Squared Residuals 1172.45361265137 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21.454 23.5092826578525 -2.05528265785246 2 23.899 21.8618161934789 2.03718380652106 3 24.939 22.8280932858234 2.11090671417659 4 23.58 28.1648471301090 -4.58484713010895 5 24.562 25.1792955906708 -0.617295590670753 6 24.696 25.0524852038147 -0.356485203814674 7 23.785 28.9412260987415 -5.15622609874151 8 23.812 21.8467143545730 1.96528564542697 9 21.917 24.2790911660309 -2.36209116603087 10 19.713 26.1579852403368 -6.44498524033684 11 19.282 19.7505608733966 -0.468560873396586 12 18.788 23.0143094655784 -4.22630946557839 13 21.453 21.9383364888318 -0.485336488831806 14 24.482 23.1935370876745 1.28846291232546 15 27.474 28.6388271842430 -1.16482718424303 16 27.264 29.9760308478573 -2.71203084785726 17 27.349 27.3872870673511 -0.0382870673511174 18 30.632 32.3723398695052 -1.74033986950523 19 29.429 29.8884069231052 -0.459406923105207 20 30.084 25.9959407348235 4.08805926517655 21 26.29 29.5364655036820 -3.24646550368203 22 24.379 28.8511787693086 -4.47217876930857 23 23.335 25.7297927775073 -2.39479277750728 24 21.346 26.8306115466204 -5.48461154662042 25 21.106 24.4927755837575 -3.38677558375746 26 24.514 26.7476910907175 -2.23369109071747 27 28.353 31.3951932744199 -3.04219327441988 28 30.805 31.6190496896619 -0.814049689661886 29 31.348 28.9942774861453 2.35372251385468 30 34.556 36.3969656265055 -1.84096562650552 31 33.855 32.711126139071 1.14387386092902 32 34.787 31.150769424628 3.63623057537202 33 32.529 33.3304020521905 -0.801402052190478 34 29.998 30.9362697372452 -0.938269737245161 35 29.257 30.3164329148083 -1.05943291480830 36 28.155 29.6008804106331 -1.44588041063312 37 30.466 29.7520203732486 0.713979626751412 38 35.704 31.4444384989281 4.25956150107192 39 39.327 36.9490835842694 2.37791641573058 40 39.351 33.0172016693448 6.33379833065518 41 42.234 36.6781262648957 5.55587373510431 42 43.63 38.4646990067892 5.16530099321079 43 43.722 36.2015418705745 7.52045812942546 44 43.121 35.8279970371483 7.29300296285173 45 37.985 33.7628579041828 4.22214209581721 46 37.135 34.0608133713413 3.07418662865868 47 34.646 34.5393217808878 0.106678219112237 48 33.026 30.2356859797347 2.79031402026535 49 35.087 34.0888151870083 0.998184812991698 50 38.846 35.0218253644232 3.82417463557685 51 42.013 40.4578080406046 1.55519195939537 52 43.908 36.8669835288579 7.0410164711421 53 42.868 42.3448153070952 0.523184692904821 54 44.423 41.7424442833559 2.68055571664408 55 44.167 40.5951520839272 3.57184791607277 56 43.636 38.9094621615088 4.7265378384912 57 44.382 32.8206061237704 11.5613938762296 58 42.142 41.0708634817300 1.07113651827005 59 43.452 39.4403257010329 4.0116742989671 60 36.912 36.4323095942094 0.479690405790627 61 42.413 42.0778786353113 0.335121364688697 62 45.344 43.4945999560039 1.84940004399612 63 44.873 43.2506448242032 1.62235517579680 64 47.51 50.0999504664975 -2.58995046649748 65 49.554 48.0926682624758 1.46133173752415 66 47.369 50.8031221702656 -3.43412217026561 67 45.998 51.3172157137271 -5.31921571372708 68 48.14 43.7928108495821 4.34718915041791 69 48.441 47.086791884737 1.35420811526296 70 44.928 48.0613777457123 -3.13337774571233 71 40.454 39.5104810518695 0.943518948130518 72 38.661 41.3891412022154 -2.72814120221541 73 37.246 38.8402834128577 -1.59428341285767 74 36.843 37.5241584590142 -0.681158459014184 75 36.424 36.0890607707563 0.334939229243744 76 37.594 42.6517009637095 -5.05770096370947 77 38.144 37.3595355066562 0.784464493343774 78 38.737 40.4091208532928 -1.67212085329285 79 34.56 44.0552287584902 -9.4952287584902 80 36.08 36.5562952989398 -0.476295298939846 81 33.508 40.5617369552775 -7.05373695527747 82 35.462 40.5047318954123 -5.04273189541225 83 33.374 36.2412539816967 -2.86725398169669 84 32.11 40.0457166917273 -7.93571669172734 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.0217861543359573 0.0435723086719146 0.978213845664043 8 0.00859857924907621 0.0171971584981524 0.991401420750924 9 0.0182607195758642 0.0365214391517283 0.981739280424136 10 0.0506287716678722 0.101257543335744 0.949371228332128 11 0.0450047995839629 0.0900095991679258 0.954995200416037 12 0.0294778565948074 0.0589557131896148 0.970522143405193 13 0.0152722488825267 0.0305444977650534 0.984727751117473 14 0.00798827747275692 0.0159765549455138 0.992011722527243 15 0.00417042209350676 0.00834084418701352 0.995829577906493 16 0.00203018424270777 0.00406036848541554 0.997969815757292 17 0.00191795955117257 0.00383591910234514 0.998082040448827 18 0.00165301482624455 0.0033060296524891 0.998346985173755 19 0.00167362044554391 0.00334724089108782 0.998326379554456 20 0.00215630016112810 0.00431260032225619 0.997843699838872 21 0.00190991207717793 0.00381982415435586 0.998090087922822 22 0.00485564258354874 0.00971128516709749 0.995144357416451 23 0.0044250132609175 0.008850026521835 0.995574986739083 24 0.0070593563424588 0.0141187126849176 0.992940643657541 25 0.0205342357827068 0.0410684715654136 0.979465764217293 26 0.0184435917931524 0.0368871835863049 0.981556408206848 27 0.0168765128002258 0.0337530256004515 0.983123487199774 28 0.0139770645205185 0.0279541290410370 0.986022935479481 29 0.0144497064011747 0.0288994128023494 0.985550293598825 30 0.0145433797078865 0.0290867594157730 0.985456620292114 31 0.0171384286281142 0.0342768572562284 0.982861571371886 32 0.0226900348359498 0.0453800696718995 0.97730996516405 33 0.0190893615389393 0.0381787230778786 0.98091063846106 34 0.0176510932582252 0.0353021865164503 0.982348906741775 35 0.0184609690912615 0.0369219381825229 0.981539030908739 36 0.0268492941427418 0.0536985882854835 0.973150705857258 37 0.0251210187055261 0.0502420374110523 0.974878981294474 38 0.0260198192262569 0.0520396384525139 0.973980180773743 39 0.0237341164255907 0.0474682328511813 0.97626588357441 40 0.036920694067568 0.073841388135136 0.963079305932432 41 0.0615508423104574 0.123101684620915 0.938449157689543 42 0.0720758050759574 0.144151610151915 0.927924194924043 43 0.120005109362113 0.240010218724226 0.879994890637887 44 0.161714932366445 0.323429864732889 0.838285067633555 45 0.128412245226515 0.256824490453029 0.871587754773485 46 0.100320018866002 0.200640037732004 0.899679981133998 47 0.107377340188799 0.214754680377599 0.892622659811201 48 0.092399079182806 0.184798158365612 0.907600920817194 49 0.0961735501258627 0.192347100251725 0.903826449874137 50 0.0721288572645183 0.144257714529037 0.927871142735482 51 0.0597835559459649 0.119567111891930 0.940216444054035 52 0.0585228879175541 0.117045775835108 0.941477112082446 53 0.0535028794045484 0.107005758809097 0.946497120595452 54 0.0394560986217307 0.0789121972434614 0.96054390137827 55 0.0275512095112604 0.0551024190225207 0.97244879048874 56 0.019722640318659 0.039445280637318 0.98027735968134 57 0.094868958044554 0.189737916089108 0.905131041955446 58 0.0763231701760833 0.152646340352167 0.923676829823917 59 0.0719092099430885 0.143818419886177 0.928090790056912 60 0.0737807064060344 0.147561412812069 0.926219293593966 61 0.0752561874135611 0.150512374827122 0.924743812586439 62 0.106440719023066 0.212881438046133 0.893559280976933 63 0.183510252679975 0.36702050535995 0.816489747320025 64 0.287978889075005 0.575957778150011 0.712021110924995 65 0.517996279150978 0.964007441698043 0.482003720849022 66 0.517491401989034 0.965017196021933 0.482508598010966 67 0.719612142228904 0.560775715542193 0.280387857771096 68 0.771660142346853 0.456679715306294 0.228339857653147 69 0.765578741253129 0.468842517493742 0.234421258746871 70 0.76315995471705 0.473680090565901 0.236840045282951 71 0.87739349374357 0.245213012512858 0.122606506256429 72 0.99179736923511 0.0164052615297804 0.00820263076489021 73 0.987783097107842 0.024433805784317 0.0122169028921585 74 0.97379488094488 0.0524102381102395 0.0262051190551198 75 0.987227523911235 0.0255449521775306 0.0127724760887653 76 0.980830224521037 0.0383395509579255 0.0191697754789627 77 0.949228545752224 0.101542908495551 0.0507714542477757 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.126760563380282 NOK 5% type I error level 32 0.450704225352113 NOK 10% type I error level 41 0.577464788732394 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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