Multiple Regression analysis 2a

*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: Fri, 18 Dec 2009 08:36:53 -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/18/t12611506496c72urmw9uslzeu.htm/, Retrieved Fri, 18 Dec 2009 16:37:41 +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/18/t12611506496c72urmw9uslzeu.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:

» Textbox « » Textfile « » CSV «

100.5 98.60 96.33 106.29 96.90 96.33 101.09 95.10 95.05 104.53 97.00 96.84 122.74 112.70 96.92 109.84 102.90 97.44 101.99 97.40 97.78 125.12 111.40 97.69 103.5 87.40 96.67 102.8 96.80 98.29 118.72 114.10 98.20 119.01 110.30 98.71 118.61 103.90 98.54 120.43 101.60 98.20 111.83 94.60 100.80 116.79 95.90 101.33 131.71 104.70 101.88 120.57 102.80 101.85 117.83 98.10 102.04 130.8 113.90 102.22 107.46 80.90 102.63 112.09 95.70 102.65 129.47 113.20 102.54 119.72 105.90 102.37 134.81 108.80 102.68 135.8 102.30 102.76 129.27 99.00 102.82 126.94 100.70 103.31 153.45 115.50 103.23 121.86 100.70 103.60 133.47 109.90 103.95 135.34 114.60 103.93 117.1 85.40 104.25 120.65 100.50 104.38 132.49 114.80 104.36 137.6 116.50 104.32 138.69 112.90 104.58 125.53 102.00 104.68 133.09 106.00 104.92 129.08 105.30 105.46 145.94 118.80 105.23 129.07 106.10 105.58 139.69 109.30 105.34 142.09 117.20 105.28 137.29 92.50 105.70 127.03 104.20 105.67 137.25 112.50 105.71 156.87 122.40 106.19 150. 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Invoer[t] = -302.163143368612 + 1.83960425125152TIP[t] + 2.21964812237739CONS[t] + 8.06502158287286M1[t] + 15.4927591059766M2[t] + 15.0387109696017M3[t] + 9.03916944167505M4[t] + 6.51428846995665M5[t] + 6.05042410409308M6[t] + 6.96847684173931M7[t] + 1.55787249898175M8[t] + 32.3395943901429M9[t] + 10.1387969509638M10[t] -2.41633725563274M11[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) -302.163143368612 22.031303 -13.7152 0 0 TIP 1.83960425125152 0.135023 13.6244 0 0 CONS 2.21964812237739 0.160676 13.8145 0 0 M1 8.06502158287286 3.97399 2.0295 0.047175 0.023587 M2 15.4927591059766 4.301471 3.6017 0.000672 0.000336 M3 15.0387109696017 4.308838 3.4902 0.000948 0.000474 M4 9.03916944167505 4.226685 2.1386 0.036844 0.018422 M5 6.51428846995665 3.850933 1.6916 0.096278 0.048139 M6 6.05042410409308 4.064428 1.4886 0.142195 0.071098 M7 6.96847684173931 4.132873 1.6861 0.097339 0.04867 M8 1.55787249898175 3.830258 0.4067 0.685758 0.342879 M9 32.3395943901429 5.260592 6.1475 0 0 M10 10.1387969509638 4.43135 2.288 0.025937 0.012968 M11 -2.41633725563274 3.993694 -0.605 0.547598 0.273799 Multiple Linear Regression - Regression Statistics Multiple R 0.946943282382516 R-squared 0.896701580049374 Adjusted R-squared 0.872721589703693 F-TEST (value) 37.3937423294616 F-TEST (DF numerator) 13 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.31434383351252 Sum Squared Residuals 2232.77253067778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.5 101.105561016276 -0.605561016275506 2 106.29 105.405971312251 0.884028687748686 3 101.09 98.7994859269806 2.29051407301938 4 104.53 100.268362615487 4.26163738451258 5 122.74 126.802840238208 -4.06284023820815 6 109.84 109.465071233716 0.374928766284138 7 101.99 101.019980951087 0.97001904891297 8 125.12 121.164067794837 3.95593220516314 9 103.5 105.531246571137 -2.03124657113652 10 102.8 104.218559051973 -1.41855905197311 11 118.72 123.288810061014 -4.56881006101394 12 119.01 119.846671704303 -0.836671704303348 13 118.61 115.760885898362 2.84911410163768 14 120.43 118.202853281979 2.22714671802079 15 111.83 110.642660505025 1.18733949497514 16 116.79 108.211018008585 8.5789819914148 17 131.71 123.095460915188 8.61453908481223 18 120.57 119.069759028275 1.50024097172502 19 117.83 111.763404928291 6.06659507170924 20 130.8 135.818084417335 -5.01808441733522 21 107.46 106.802921747371 0.657078252629171 22 112.09 111.872660189162 0.217339810838169 23 129.47 131.266439086005 -1.79643908600546 24 119.72 119.876325126698 -0.156325126697903 25 134.81 133.964289956137 0.845710043862845 26 135.8 129.612171695896 6.18782830410384 27 129.27 123.220608417734 6.04939158226612 28 126.94 121.436021696900 5.50397830310024 29 153.45 145.959711793914 7.49028820608625 30 121.86 119.090974314807 2.76902568519279 31 133.47 137.710263006800 -4.24026300679957 32 135.34 140.901405682477 -5.56140568247663 33 117.1 118.676970836254 -1.57697083625407 34 120.65 124.542751846882 -3.892751846882 35 132.49 138.249565470735 -5.7595654707347 36 137.6 143.7044440286 -6.10444402859994 37 138.69 145.723998818785 -7.03399881878544 38 125.53 133.322014815485 -7.7920148154853 39 133.09 140.759099233487 -7.6690992334871 40 129.08 134.670444715768 -5.59044471576811 41 145.94 156.469702067799 -10.5297020677985 42 129.07 133.419740553873 -4.34974055387267 43 139.69 139.691811346153 -0.00181134615321338 44 142.09 148.68090170094 -6.59090170094006 45 137.29 134.956650797587 2.33334920241290 46 127.03 134.212633654379 -7.18263365437949 47 137.25 137.015000658066 0.234999341934333 48 156.87 158.708851099830 -1.83885109982966 49 150.89 151.676013606873 -0.786013606872927 50 139.14 135.769035130744 3.37096486925616 51 158.3 155.908808212935 2.39119178706502 52 149 155.681332997881 -6.68133299788106 53 158.36 148.991624627058 9.36837537294172 54 168.06 162.702132326385 5.35786767361468 55 153.38 150.111380370657 3.268619629343 56 173.86 158.142337147808 15.7176628521917 57 162.47 153.647723312491 8.8222766875094 58 145.17 137.682741666459 7.48725833354104 59 168.89 157.000184724180 11.8898152758198 60 166.64 157.703708040569 8.93629195943086 61 140.07 135.339250703567 4.73074929643335 62 128.84 133.717953763644 -4.87795376364418 63 123.41 127.659337703839 -4.24933770383856 64 120.3 126.372819965378 -6.07281996537845 65 129.67 140.550660357833 -10.8806603578335 66 118.1 123.752322542944 -5.65232254294396 67 113.91 119.973159397012 -6.06315939701242 68 131.09 133.593203256603 -2.50320325660291 69 119.15 127.354486735161 -8.20448673516088 70 122.3 117.510653591145 4.7893464088554 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.163983913220158 0.327967826440316 0.836016086779842 18 0.0767024925823299 0.153404985164660 0.92329750741767 19 0.0373202011541513 0.0746404023083026 0.962679798845849 20 0.0869114806127433 0.173822961225487 0.913088519387257 21 0.0532714560881957 0.106542912176391 0.946728543911804 22 0.0257062103290576 0.0514124206581152 0.974293789670942 23 0.0116497693619182 0.0232995387238364 0.988350230638082 24 0.00604776106260169 0.0120955221252034 0.993952238937398 25 0.00277868302939664 0.00555736605879327 0.997221316970603 26 0.00212958260104085 0.0042591652020817 0.99787041739896 27 0.00201049021914316 0.00402098043828633 0.997989509780857 28 0.00257613716208174 0.00515227432416349 0.997423862837918 29 0.00623113769235475 0.0124622753847095 0.993768862307645 30 0.0081913373074543 0.0163826746149086 0.991808662692546 31 0.00901540867821964 0.0180308173564393 0.99098459132178 32 0.00974512165359141 0.0194902433071828 0.990254878346409 33 0.00875252625957827 0.0175050525191565 0.991247473740422 34 0.00552345077700969 0.0110469015540194 0.99447654922299 35 0.00396083555367696 0.00792167110735391 0.996039164446323 36 0.00234855249823348 0.00469710499646696 0.997651447501767 37 0.00224527988393082 0.00449055976786164 0.99775472011607 38 0.00947998594017647 0.0189599718803529 0.990520014059824 39 0.00810374618289332 0.0162074923657866 0.991896253817107 40 0.0123933140730121 0.0247866281460243 0.987606685926988 41 0.0231084381886549 0.0462168763773097 0.976891561811345 42 0.0161851719123988 0.0323703438247975 0.983814828087601 43 0.0143690176875991 0.0287380353751983 0.9856309823124 44 0.0121718736380398 0.0243437472760796 0.98782812636196 45 0.0298391374287623 0.0596782748575245 0.970160862571238 46 0.0316270969389377 0.0632541938778755 0.968372903061062 47 0.0195118232769864 0.0390236465539728 0.980488176723014 48 0.0194317290131793 0.0388634580263587 0.98056827098682 49 0.0221588827927199 0.0443177655854398 0.97784111720728 50 0.0228509100068035 0.045701820013607 0.977149089993196 51 0.0159918598087825 0.031983719617565 0.984008140191218 52 0.0303006387120426 0.0606012774240852 0.969699361287957 53 0.424628224338441 0.849256448676882 0.575371775661559 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.189189189189189 NOK 5% type I error level 27 0.72972972972973 NOK 10% type I error level 32 0.864864864864865 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/10594f1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/10594f1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/1oqgi1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/1oqgi1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/23sqx1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/23sqx1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/38sy91261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/38sy91261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/4ik6h1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/4ik6h1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/5rtie1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/5rtie1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/69b2o1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/69b2o1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/78lj31261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/78lj31261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/88hdg1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/88hdg1261150602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/9vtgx1261150602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t12611506496c72urmw9uslzeu/9vtgx1261150602.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by