Multiple 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: Thu, 17 Dec 2009 05:59:13 -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/17/t1261054818knjsmd0boqilttp.htm/, Retrieved Thu, 17 Dec 2009 14:00:30 +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/17/t1261054818knjsmd0boqilttp.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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107.1 0 96.3 87.0 96.8 115.2 0 107.1 96.3 87.0 106.1 0 115.2 107.1 96.3 89.5 0 106.1 115.2 107.1 91.3 0 89.5 106.1 115.2 97.6 0 91.3 89.5 106.1 100.7 0 97.6 91.3 89.5 104.6 0 100.7 97.6 91.3 94.7 0 104.6 100.7 97.6 101.8 0 94.7 104.6 100.7 102.5 0 101.8 94.7 104.6 105.3 0 102.5 101.8 94.7 110.3 0 105.3 102.5 101.8 109.8 0 110.3 105.3 102.5 117.3 0 109.8 110.3 105.3 118.8 0 117.3 109.8 110.3 131.3 0 118.8 117.3 109.8 125.9 0 131.3 118.8 117.3 133.1 0 125.9 131.3 118.8 147.0 0 133.1 125.9 131.3 145.8 0 147.0 133.1 125.9 164.4 0 145.8 147.0 133.1 149.8 0 164.4 145.8 147.0 137.7 0 149.8 164.4 145.8 151.7 0 137.7 149.8 164.4 156.8 0 151.7 137.7 149.8 180.0 0 156.8 151.7 137.7 180.4 0 180.0 156.8 151.7 170.4 0 180.4 180.0 156.8 191.6 0 170.4 180.4 180.0 199.5 0 191.6 170.4 180.4 218.2 0 199.5 191.6 170.4 217.5 0 218.2 199.5 191.6 205.0 0 217.5 218.2 199.5 194.0 0 205.0 217.5 218.2 199.3 0 194.0 205.0 217.5 219.3 0 199.3 194.0 205.0 211.1 0 219.3 199.3 194.0 215.2 0 211.1 219.3 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 15.1707908539916 + 2.58019404216615D[t] + 1.2970254593017Y1[t] -0.140546777544707Y2[t] -0.292873046833665Y3[t] + 0.300136115162012t + 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) 15.1707908539916 6.134386 2.4731 0.01566 0.00783 D 2.58019404216615 7.636126 0.3379 0.736388 0.368194 Y1 1.2970254593017 0.112758 11.5027 0 0 Y2 -0.140546777544707 0.190226 -0.7388 0.46231 0.231155 Y3 -0.292873046833665 0.114306 -2.5622 0.012407 0.006204 t 0.300136115162012 0.175054 1.7145 0.090561 0.04528 Multiple Linear Regression - Regression Statistics Multiple R 0.978714772026642 R-squared 0.957882604983162 Adjusted R-squared 0.955074778648706 F-TEST (value) 341.147382667025 F-TEST (DF numerator) 5 F-TEST (DF denominator) 75 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18.5544241079048 Sum Squared Residuals 25819.9990482000 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 107.1 99.7967981200193 7.30320187998065 2 115.2 115.667880023444 -0.467880023443807 3 106.1 122.232297825913 -16.1322978259132 4 89.5 106.428044457514 -16.9280444575143 5 91.3 84.1042619445722 7.1957380554278 6 97.6 91.7372651199057 5.86273488009425 7 100.7 104.817370006527 -4.11737000652681 8 104.6 107.725668862692 -3.12566886269187 9 94.7 110.803409063690 -16.1034090636898 10 101.8 96.8069542541563 4.99304574584372 11 102.5 106.565179345402 -4.06517934540167 12 105.3 109.674794325161 -4.37479432516073 13 110.3 111.428820349567 -1.12882034956719 14 109.8 117.615541651329 -7.81554165132895 15 117.3 115.744386617982 1.55561338201768 16 118.8 124.378121832511 -5.57812183251111 17 131.3 125.716131828457 5.58386817154281 18 125.9 139.821718167321 -13.9217181673209 19 133.1 130.921772512694 2.17822748730556 20 147 137.658531448149 9.34146855185073 21 145.8 156.556899102185 -10.7568991021848 22 164.4 151.238318521111 13.161681478889 23 149.8 171.760848961350 -21.9608489613503 24 137.7 150.861690964576 -13.1616909645764 25 151.7 132.072363303234 19.6276366967656 26 156.8 156.507418340683 0.292581659317393 27 180 164.998493279345 15.0015067206552 28 180.4 190.572608829157 -10.1726088291569 29 170.4 186.637217350151 -16.2372173501507 30 191.6 167.116225474737 24.4837745252632 31 199.5 196.201619883808 3.29838011619154 32 218.2 206.697395911843 11.5026040881572 33 217.5 223.932679980470 -6.43267998046969 34 205 218.382976464049 -13.3829764640485 35 194 197.091951106431 -3.09195110643108 36 199.3 185.086653021367 14.2133469786332 37 219.3 197.467951709240 21.8320482907596 38 211.1 226.185302604620 -15.0853026046198 39 215.2 211.486667254395 3.71333274560472 40 240.2 212.399630391888 27.8003696081124 41 242.2 246.950720185695 -4.75072018569482 42 240.7 245.130458288825 -4.43045828882452 43 255.4 235.882136489103 19.5178635108971 44 253 254.873620928650 -1.87362092864971 45 218.2 250.434167881831 -32.2341678818310 46 203.7 201.629896490946 2.07010350905378 47 205.6 188.717086617190 16.8829133828098 48 215.6 203.711481409235 11.8885185907648 49 188.5 220.961492419167 -32.4614924191674 50 202.9 184.150312022822 18.7496879771777 51 214 204.007701955054 9.99229804494628 52 230.3 224.617806641013 5.68219335898689 53 230 240.282016637642 -10.2820166376418 54 241 234.651241821181 6.34875817881907 55 259.6 247.067185400702 12.5328145992977 56 247.8 270.033842419934 -22.2338424199343 57 270.3 249.193304537834 21.1066954621656 58 289.7 274.887526791206 14.8124732087940 59 322.7 300.643556274702 22.0564437252977 60 315 334.429281508696 -19.4292815086957 61 320.2 314.422540819686 5.77745918031379 62 329.5 312.884608964800 16.6153910351997 63 360.6 326.771361068855 33.8286389311451 64 382.2 364.578964093599 17.621035906401 65 435.4 385.800126012484 49.5998739875157 66 464 442.957854411004 21.042145588996 67 468.8 466.549772285209 2.25022771479086 68 403 453.47514667569 -50.4751466756897 69 351.6 359.380213897142 -7.78021389714243 70 252 300.855428741837 -48.8554287418372 71 188 198.466979958003 -10.4669799580030 72 146.5 144.809620328559 1.69037967144065 73 152.9 129.448349110195 23.4516508898050 74 148.1 162.626014430348 -14.5260144303478 75 165.1 167.955160408173 -2.85516040817264 76 177 189.104966363943 -12.1049663639427 77 206.1 203.856200851336 2.24379914866349 78 244.9 235.248429383224 9.65157061677632 79 228.6 278.29805283542 -49.6980528354201 80 253.4 243.48085333237 9.91914666762991 81 241.1 266.874659095047 -25.7746590950468 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0223044695175946 0.0446089390351892 0.977695530482405 10 0.0143262870721759 0.0286525741443517 0.985673712927824 11 0.00753719623978628 0.0150743924795726 0.992462803760214 12 0.00255452718312488 0.00510905436624976 0.997445472816875 13 0.0020224471564735 0.004044894312947 0.997977552843527 14 0.000763281773093224 0.00152656354618645 0.999236718226907 15 0.000920010997375257 0.00184002199475051 0.999079989002625 16 0.000361933144679982 0.000723866289359964 0.99963806685532 17 0.000680982628736677 0.00136196525747335 0.999319017371263 18 0.000286502198658711 0.000573004397317422 0.999713497801341 19 0.000226575180265881 0.000453150360531761 0.999773424819734 20 0.000192554728961195 0.00038510945792239 0.999807445271039 21 8.03599389054704e-05 0.000160719877810941 0.999919640061095 22 0.000155523949869902 0.000311047899739803 0.99984447605013 23 0.000265152080315990 0.000530304160631981 0.999734847919684 24 0.000236423380414597 0.000472846760829193 0.999763576619585 25 0.000148189929612245 0.00029637985922449 0.999851810070388 26 6.41929908395996e-05 0.000128385981679199 0.99993580700916 27 0.00016881281120564 0.00033762562241128 0.999831187188794 28 8.80625258630217e-05 0.000176125051726043 0.999911937474137 29 6.7636498666953e-05 0.000135272997333906 0.999932363501333 30 9.79538793228484e-05 0.000195907758645697 0.999902046120677 31 4.78995994222496e-05 9.57991988444991e-05 0.999952100400578 32 6.88650748277579e-05 0.000137730149655516 0.999931134925172 33 3.65537540240901e-05 7.31075080481801e-05 0.999963446245976 34 3.89079716364752e-05 7.78159432729505e-05 0.999961092028363 35 3.42426127995193e-05 6.84852255990387e-05 0.9999657573872 36 1.53029244667751e-05 3.06058489335501e-05 0.999984697075533 37 1.21800553574767e-05 2.43601107149534e-05 0.999987819944643 38 1.23070600818785e-05 2.46141201637571e-05 0.999987692939918 39 5.83025304774715e-06 1.16605060954943e-05 0.999994169746952 40 8.96665696003511e-06 1.79333139200702e-05 0.99999103334304 41 4.32367556395301e-06 8.64735112790601e-06 0.999995676324436 42 2.11257081655950e-06 4.22514163311901e-06 0.999997887429183 43 1.52600449770315e-06 3.05200899540631e-06 0.999998473995502 44 7.038922921419e-07 1.4077845842838e-06 0.999999296107708 45 2.18288626453530e-05 4.36577252907060e-05 0.999978171137355 46 2.40806152129645e-05 4.8161230425929e-05 0.999975919384787 47 1.83930006808305e-05 3.67860013616611e-05 0.99998160699932 48 9.5895617048996e-06 1.91791234097992e-05 0.999990410438295 49 0.000182510005595050 0.000365020011190100 0.999817489994405 50 0.000113421289773695 0.000226842579547391 0.999886578710226 51 6.13296360600287e-05 0.000122659272120057 0.99993867036394 52 3.38716547974193e-05 6.77433095948387e-05 0.999966128345203 53 1.97693057429495e-05 3.9538611485899e-05 0.999980230694257 54 9.76853525306571e-06 1.95370705061314e-05 0.999990231464747 55 4.5704691866225e-06 9.140938373245e-06 0.999995429530813 56 3.72365684482919e-05 7.44731368965839e-05 0.999962763431552 57 3.21014241942270e-05 6.42028483884541e-05 0.999967898575806 58 1.83229603378106e-05 3.66459206756212e-05 0.999981677039662 59 2.11520315738060e-05 4.23040631476119e-05 0.999978847968426 60 0.000200018144877199 0.000400036289754399 0.999799981855123 61 0.00054423414478525 0.0010884682895705 0.999455765855215 62 0.000423189596154421 0.000846379192308843 0.999576810403846 63 0.000395740136484996 0.000791480272969992 0.999604259863515 64 0.000254685533350103 0.000509371066700205 0.99974531446665 65 0.00299341158889442 0.00598682317778884 0.997006588411106 66 0.0145077659072498 0.0290155318144996 0.98549223409275 67 0.0852218769917232 0.170443753983446 0.914778123008277 68 0.165872193872886 0.331744387745771 0.834127806127114 69 0.368567655229419 0.737135310458839 0.63143234477058 70 0.502371032855588 0.995257934288824 0.497628967144412 71 0.367151491591172 0.734302983182343 0.632848508408828 72 0.229209002948359 0.458418005896717 0.770790997051641 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 54 0.84375 NOK 5% type I error level 58 0.90625 NOK 10% type I error level 58 0.90625 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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