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: Tue, 30 Nov 2010 12:30:57 +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/Nov/30/t1291120169hamfajbutf2xhs7.htm/, Retrieved Tue, 30 Nov 2010 13:29:30 +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/2010/Nov/30/t1291120169hamfajbutf2xhs7.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:

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9700 9081 9084 9743 8587 9731 9563 9998 9437 10039 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Births[t] = + 9330.6231884058 + 107.616287094544M1[t] -635.535541752933M2[t] -287.830227743271M3[t] + 8.73844030365812M4[t] -879.770531400966M5[t] + 75.7204968944103M6[t] -309.621808143547M7[t] -141.297446514838M8[t] -196.973084886128M9[t] + 367.351276742581M10[t] + 234.508971704624M11[t] + 11.0089717046239t + 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) 9330.6231884058 136.438321 68.3871 0 0 M1 107.616287094544 162.991916 0.6603 0.511536 0.255768 M2 -635.535541752933 162.923919 -3.9008 0.000239 0.000119 M3 -287.830227743271 162.871013 -1.7672 0.082111 0.041056 M4 8.73844030365812 169.414367 0.0516 0.959029 0.479514 M5 -879.770531400966 169.305323 -5.1964 2e-06 1e-06 M6 75.7204968944103 169.210762 0.4475 0.656079 0.32804 M7 -309.621808143547 169.130707 -1.8307 0.071957 0.035979 M8 -141.297446514838 169.065179 -0.8358 0.406501 0.20325 M9 -196.973084886128 169.014195 -1.1654 0.248312 0.124156 M10 367.351276742581 168.977769 2.174 0.033534 0.016767 M11 234.508971704624 168.95591 1.388 0.170108 0.085054 t 11.0089717046239 1.56919 7.0157 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.846476842937978 R-squared 0.716523045630245 Adjusted R-squared 0.661656538332874 F-TEST (value) 13.0593887040549 F-TEST (DF numerator) 12 F-TEST (DF denominator) 62 p-value 8.08797473439427e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 292.627597935097 Sum Squared Residuals 5309116.48654243 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9700 9449.248447205 250.751552795006 2 9081 8717.10559006211 363.894409937889 3 9084 9075.8198757764 8.18012422360365 4 9743 9383.39751552795 359.602484472051 5 8587 8505.89751552795 81.1024844720503 6 9731 9472.39751552795 258.602484472051 7 9563 9098.06418219462 464.935817805384 8 9998 9277.39751552795 720.60248447205 9 9437 9232.73084886128 204.269151138717 10 10039 9808.06418219462 230.935817805384 11 9918 9686.23084886128 231.769151138717 12 9252 9462.73084886128 -210.730848861283 13 9737 9581.35610766045 155.643892339550 14 9035 8849.2132505176 185.786749482402 15 9133 9207.92753623188 -74.9275362318834 16 9487 9515.50517598344 -28.5051759834363 17 8700 8638.00517598344 61.9948240165638 18 9627 9604.50517598344 22.4948240165637 19 8947 9230.1718426501 -283.171842650103 20 9283 9409.50517598344 -126.505175983436 21 8829 9364.83850931677 -535.83850931677 22 9947 9940.1718426501 6.82815734989703 23 9628 9818.33850931677 -190.338509316770 24 9318 9594.83850931677 -276.838509316770 25 9605 9713.46376811594 -108.463768115937 26 8640 8981.32091097309 -341.320910973085 27 9214 9340.03519668737 -126.035196687370 28 9567 9647.61283643892 -80.6128364389232 29 8547 8770.11283643892 -223.112836438923 30 9185 9736.61283643892 -551.612836438923 31 9470 9362.27950310559 107.720496894410 32 9123 9541.61283643892 -418.612836438923 33 9278 9496.94616977226 -218.946169772257 34 10170 10072.2795031056 97.7204968944101 35 9434 9950.44616977226 -516.446169772257 36 9655 9726.94616977226 -71.9461697722564 37 9429 9845.57142857142 -416.571428571424 38 8739 9113.42857142857 -374.428571428571 39 9552 9472.14285714286 79.8571428571428 40 9687 9779.7204968944 -92.7204968944101 41 9019 8902.22049689441 116.77950310559 42 9672 9868.7204968944 -196.72049689441 43 9206 9494.38716356108 -288.387163561077 44 9069 9673.72049689441 -604.72049689441 45 9788 9629.05383022774 158.946169772256 46 10312 10204.3871635611 107.612836438923 47 10105 10082.5538302277 22.4461697722565 48 9863 9859.05383022774 3.94616977225661 49 9656 9977.67908902691 -321.679089026911 50 9295 9245.53623188406 49.4637681159416 51 9946 9604.25051759834 341.749482401656 52 9701 9911.8281573499 -210.828157349897 53 9049 9034.3281573499 14.6718426501029 54 10190 10000.8281573499 189.171842650103 55 9706 9626.49482401656 79.5051759834362 56 9765 9805.8281573499 -40.8281573498971 57 9893 9761.16149068323 131.838509316770 58 9994 10336.4948240166 -342.494824016564 59 10433 10214.6614906832 218.338509316770 60 10073 9991.16149068323 81.8385093167697 61 10112 10109.7867494824 2.21325051760193 62 9266 9377.64389233955 -111.643892339545 63 9820 9736.35817805383 83.6418219461689 64 10097 10043.9358178054 53.064182194616 65 9115 9166.43581780538 -51.4358178053838 66 10411 10132.9358178054 278.064182194616 67 9678 9758.60248447205 -80.6024844720507 68 10408 9937.93581780538 470.064182194616 69 10153 9893.26915113872 259.730848861283 70 10368 10468.6024844721 -100.602484472051 71 10581 10346.7691511387 234.230848861283 72 10597 10123.2691511387 473.730848861283 73 10680 10241.8944099379 438.105590062115 74 9738 9509.75155279503 228.248447204968 75 9556 9868.46583850932 -312.465838509318 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0857950324780317 0.171590064956063 0.914204967521968 17 0.0524991423628487 0.104998284725697 0.947500857637151 18 0.0231043965667466 0.0462087931334933 0.976895603433253 19 0.231495210219771 0.462990420439541 0.76850478978023 20 0.455792536295226 0.911585072590453 0.544207463704774 21 0.504624854768493 0.990750290463014 0.495375145231507 22 0.437456128742004 0.874912257484009 0.562543871257996 23 0.340133745453152 0.680267490906304 0.659866254546848 24 0.310692455296403 0.621384910592806 0.689307544703597 25 0.267120809636914 0.534241619273828 0.732879190363086 26 0.206731687361517 0.413463374723033 0.793268312638483 27 0.239844142132692 0.479688284265384 0.760155857867308 28 0.205581894907057 0.411163789814114 0.794418105092943 29 0.153234914208051 0.306469828416101 0.84676508579195 30 0.172957135267870 0.345914270535741 0.82704286473213 31 0.267970244265079 0.535940488530159 0.73202975573492 32 0.26137013599641 0.52274027199282 0.73862986400359 33 0.268430672150749 0.536861344301499 0.73156932784925 34 0.342786019861389 0.685572039722779 0.65721398013861 35 0.358549701599177 0.717099403198355 0.641450298400823 36 0.431739198587508 0.863478397175016 0.568260801412492 37 0.380196293293068 0.760392586586136 0.619803706706932 38 0.328943489855802 0.657886979711604 0.671056510144198 39 0.450397184889652 0.900794369779304 0.549602815110348 40 0.403099124692339 0.806198249384678 0.596900875307661 41 0.485034456755899 0.970068913511797 0.514965543244101 42 0.454268641179832 0.908537282359664 0.545731358820168 43 0.380707848314169 0.761415696628338 0.619292151685831 44 0.606071238392927 0.787857523214147 0.393928761607073 45 0.674969372556427 0.650061254887147 0.325030627443573 46 0.752228437215017 0.495543125569966 0.247771562784983 47 0.728763241613885 0.542473516772231 0.271236758386115 48 0.704101815787326 0.591796368425348 0.295898184212674 49 0.747094349261184 0.505811301477633 0.252905650738816 50 0.699397735368026 0.601204529263948 0.300602264631974 51 0.916220847455845 0.167558305088310 0.0837791525441551 52 0.872681982634765 0.254636034730471 0.127318017365235 53 0.837583282163457 0.324833435673085 0.162416717836543 54 0.79132209024671 0.417355819506579 0.208677909753290 55 0.789487349144987 0.421025301710025 0.210512650855013 56 0.77593043117339 0.44813913765322 0.22406956882661 57 0.673067406242274 0.653865187515451 0.326932593757726 58 0.539475400383979 0.921049199232042 0.460524599616021 59 0.416134471360841 0.832268942721682 0.583865528639159 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 1 0.0227272727272727 OK 10% type I error level 1 0.0227272727272727 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/10t04j1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/10t04j1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/14hpp1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/14hpp1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/2x8pa1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/2x8pa1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/3x8pa1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/3x8pa1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/4x8pa1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/4x8pa1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/5x8pa1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/5x8pa1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/6qz6d1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/6qz6d1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/71r5x1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/71r5x1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/81r5x1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/81r5x1291120244.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/9t04j1291120244.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291120169hamfajbutf2xhs7/9t04j1291120244.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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