Model 5

*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, 28 Dec 2010 21:19:23 +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/28/t1293571048fadpkl2l2gexrum.htm/, Retrieved Tue, 28 Dec 2010 22:17:38 +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/28/t1293571048fadpkl2l2gexrum.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 «

3,7 0 3,7 3,93 4,15 4,24 0 0 0 3,65 0 3,7 3,7 3,93 4,15 0 0 0 3,55 0 3,65 3,7 3,7 3,93 0 0 0 3,43 0 3,55 3,65 3,7 3,7 0 0 0 3,47 0 3,43 3,55 3,65 3,7 0 0 0 3,58 0 3,47 3,43 3,55 3,65 0 0 0 3,67 0 3,58 3,47 3,43 3,55 0 0 0 3,72 0 3,67 3,58 3,47 3,43 0 0 0 3,8 0 3,72 3,67 3,58 3,47 0 0 0 3,76 0 3,8 3,72 3,67 3,58 0 0 0 3,63 0 3,76 3,8 3,72 3,67 0 0 0 3,48 0 3,63 3,76 3,8 3,72 0 0 0 3,41 0 3,48 3,63 3,76 3,8 0 0 0 3,43 0 3,41 3,48 3,63 3,76 0 0 0 3,5 0 3,43 3,41 3,48 3,63 0 0 0 3,62 0 3,5 3,43 3,41 3,48 0 0 0 3,58 0 3,62 3,5 3,43 3,41 0 0 0 3,52 0 3,58 3,62 3,5 3,43 0 0 0 3,45 0 3,52 3,58 3,62 3,5 0 0 0 3,36 0 3,45 3,52 3,58 3,62 0 0 0 3,27 0 3,36 3,45 3,52 3,58 0 0 0 3,21 0 3,27 3,36 3,45 3,52 0 0 0 3,19 0 3,21 3,27 3,36 3,45 0 0 0 3,16 0 3,19 3,21 3,27 3,36 0 0 0 3,12 0 3,16 3,19 3,21 3,27 0 0 0 3,06 0 3,12 3,16 3,19 3,21 0 0 0 3,01 0 3,06 3,12 3,16 3,19 0 0 0 2,98 0 3,01 3,06 3,12 3,16 0 0 0 2,97 0 2,98 3,01 3,06 3,12 0 0 0 3,02 0 2,97 2,98 3,01 3,06 0 0 0 3,07 0 3,02 2,97 2,98 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.205702632696605 + 0.112732044274045X[t] + 2.01139643888264Y1[t] -1.49596877798800Y2[t] + 0.349196496876271Y3[t] + 0.0804553523247682Y4[t] + 0.0525626001017356O1[t] + 0.0942234981695306O2[t] + 0.147369524897714O3[t] + 0.0334034022619562M1[t] -0.0510167264895055M2[t] + 0.0589096577756784M3[t] -0.0626460449796405M4[t] + 0.00665173642907416M5[t] + 0.0244183263253148M6[t] -0.0680214359102388M7[t] -0.0260022922625455M8[t] -0.00904691523431646M9[t] -0.0453841394217003M10[t] -0.000101676244915038M11[t] -0.000797316568880794t + 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) 0.205702632696605 0.091178 2.2561 0.02876 0.01438 X 0.112732044274045 0.062611 1.8005 0.078197 0.039098 Y1 2.01139643888264 0.16986 11.8415 0 0 Y2 -1.49596877798800 0.339394 -4.4078 6e-05 3e-05 Y3 0.349196496876271 0.380362 0.9181 0.363273 0.181637 Y4 0.0804553523247682 0.209298 0.3844 0.702411 0.351206 O1 0.0525626001017356 0.112868 0.4657 0.643582 0.321791 O2 0.0942234981695306 0.11617 0.8111 0.421405 0.210703 O3 0.147369524897714 0.115537 1.2755 0.208394 0.104197 M1 0.0334034022619562 0.058982 0.5663 0.573864 0.286932 M2 -0.0510167264895055 0.059973 -0.8507 0.399271 0.199636 M3 0.0589096577756784 0.061149 0.9634 0.340289 0.170144 M4 -0.0626460449796405 0.061892 -1.0122 0.316635 0.158318 M5 0.00665173642907416 0.064005 0.1039 0.917671 0.458835 M6 0.0244183263253148 0.061523 0.3969 0.693239 0.346619 M7 -0.0680214359102388 0.059775 -1.138 0.260904 0.130452 M8 -0.0260022922625455 0.060628 -0.4289 0.669966 0.334983 M9 -0.00904691523431646 0.061209 -0.1478 0.88313 0.441565 M10 -0.0453841394217003 0.061481 -0.7382 0.464078 0.232039 M11 -0.000101676244915038 0.061539 -0.0017 0.998689 0.499344 t -0.000797316568880794 0.001463 -0.5452 0.588222 0.294111 Multiple Linear Regression - Regression Statistics Multiple R 0.995945684344728 R-squared 0.99190780616489 Adjusted R-squared 0.988464319426545 F-TEST (value) 288.053325462098 F-TEST (DF numerator) 20 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.096133652182226 Sum Squared Residuals 0.434358916848981 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.7 3.59161440065619 0.108385599343815 2 3.65 3.76640556325106 -0.116405563251061 3 3.55 3.67694943721024 -0.126949437210241 4 3.43 3.40975048186248 0.0202495181375195 5 3.47 3.36902042699138 0.100979573008615 6 3.58 3.60701939392874 -0.0270193939287438 7 3.67 3.62524805742425 0.0447519425757492 8 3.72 3.6872522160199 0.0327477839801003 9 3.8 3.71097273715384 0.0890272628461581 10 3.76 3.80022924608338 -0.0402292460833767 11 3.63 3.66928183944998 -0.0392818394499789 12 3.48 3.49890190055713 -0.0189019005571296 13 3.41 3.41674302986718 -0.00674302986717874 14 3.43 3.36650939183635 0.0634906081636548 15 3.5 3.5577455324358 -0.0577455324358003 16 3.62 3.50975883064357 0.110241169356429 17 3.58 3.71626110896495 -0.136261108964954 18 3.52 3.49931113320628 0.0206888667937171 19 3.45 3.39276447347630 0.0572355265237039 20 3.36 3.37863345891653 -0.0186334589165254 21 3.27 3.29431365043003 -0.0243136504300277 22 3.21 3.18151954427242 0.0284804557275791 23 3.19 3.20289853518469 -0.0128985351846879 24 3.16 3.21306442633426 -0.0530644263342555 25 3.12 3.18705522289881 -0.0670552228988065 26 3.06 3.05444973228579 0.00555026771421356 27 3.01 3.09064876281587 -0.0806487628158677 28 2.98 2.94110252778202 0.0388974722179787 29 2.97 2.99988953444921 -0.0298895344492094 30 3.02 3.01933676074408 0.00066323925591695 31 3.07 3.02713052914113 0.0428694708588661 32 3.18 3.08821811372617 0.0917818862738275 33 3.29 3.38021865915782 -0.0902186591578243 34 3.43 3.42126375356002 0.00873624643997809 35 3.61 3.62522221830545 -0.0152222183054457 36 3.74 3.82440401147415 -0.0844040114741488 37 3.87 3.90695485250253 -0.0369548525025316 38 3.88 3.96286212186169 -0.082862121861689 39 4.09 3.95750672082075 0.132493279179247 40 4.19 4.29844200627816 -0.108442006278163 41 4.2 4.26787983239977 -0.0678798323997652 42 4.29 4.22950201018442 0.0604979898155849 43 4.37 4.35414619677537 0.0158538032246329 44 4.47 4.43318004914711 0.0368199508528883 45 4.61 4.5630324894978 0.0469675105022051 46 4.65 4.69307327384563 -0.043073273845633 47 4.69 4.64993472696413 0.0400652730358689 48 4.82 4.72678923787111 0.0932107621288938 49 4.86 4.98626971869992 -0.126269718699923 50 4.87 4.80421826376449 0.0657817362355111 51 5.01 4.922236303417 0.0877636965829957 52 5.03 5.09094615343376 -0.0609461534337646 53 5.13 4.99694909719469 0.133050902805313 54 5.18 5.23483070193647 -0.0548307019364752 55 5.21 5.11081424654037 0.0991857534596337 56 5.26 5.17410828462038 0.0858917153796186 57 5.25 5.27146246376051 -0.0214624637605111 58 5.2 5.15391418223855 0.0460858177614525 59 5.16 5.13266268009576 0.0273373199042435 60 5.19 5.12684042376336 0.0631595762366399 61 5.39 5.26136277537537 0.128637224624625 62 5.58 5.51555492700063 0.0644450729993707 63 5.76 5.71491324330033 0.0450867566996655 64 5.89 5.89 6.41847686111419e-17 65 5.98 5.98 3.98986399474666e-17 66 6.02 6.02 -2.25514051876985e-17 67 5.62 5.87989649664259 -0.259896496642586 68 4.87 5.09860787756991 -0.228607877569909 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 24 0.722199599977873 0.555600800044253 0.277800400022127 25 0.672612610699746 0.654774778600509 0.327387389300254 26 0.526845938416421 0.946308123167158 0.473154061583579 27 0.523468447108091 0.953063105783818 0.476531552891909 28 0.402812683726883 0.805625367453767 0.597187316273117 29 0.313324121409157 0.626648242818314 0.686675878590843 30 0.268501348873945 0.537002697747889 0.731498651126055 31 0.184881988971798 0.369763977943596 0.815118011028202 32 0.151995099170575 0.303990198341151 0.848004900829425 33 0.112939768553448 0.225879537106897 0.887060231446552 34 0.0889220829617836 0.177844165923567 0.911077917038216 35 0.0658921415548964 0.131784283109793 0.934107858445104 36 0.0561902923749116 0.112380584749823 0.943809707625088 37 0.0352394903699986 0.0704789807399972 0.964760509630001 38 0.0186953079967073 0.0373906159934145 0.981304692003293 39 0.0366011658335975 0.073202331667195 0.963398834166402 40 0.0227797043040914 0.0455594086081827 0.977220295695909 41 0.0410389629598873 0.0820779259197746 0.958961037040113 42 0.0279482747820002 0.0558965495640003 0.972051725218 43 0.0133344017474749 0.0266688034949497 0.986665598252525 44 0.020266381418708 0.040532762837416 0.979733618581292 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 4 0.190476190476190 NOK 10% type I error level 8 0.380952380952381 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/10e1141293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/10e1141293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/180ls1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/180ls1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/280ls1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/280ls1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/3ia3v1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/3ia3v1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/4ia3v1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/4ia3v1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/5ia3v1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/5ia3v1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/6b1ky1293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/6b1ky1293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/7ma111293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/7ma111293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/8ma111293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/8ma111293571155.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/9ma111293571155.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293571048fadpkl2l2gexrum/9ma111293571155.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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