paper: 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: Sun, 07 Dec 2008 08:13:05 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8.htm/, Retrieved Sun, 07 Dec 2008 15:13:53 +0000

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/2008/Dec/07/t1228662832d9toh8ry70z14p8.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

15107 0 15024 0 12083 0 15761 0 16943 0 15070 0 13660 0 14769 0 14725 0 15998 0 15371 0 14957 0 15470 0 15102 0 11704 0 16284 0 16727 0 14969 0 14861 0 14583 0 15306 0 17904 0 16379 0 15420 0 17871 0 15913 0 13867 0 17823 0 17872 0 17422 0 16705 0 15991 0 16584 0 19124 0 17839 0 17209 0 18587 0 16258 0 15142 0 19202 0 17747 0 19090 0 18040 0 17516 0 17752 0 21073 0 17170 0 19440 0 19795 0 17575 0 16165 0 19465 1 19932 1 19961 1 17343 1 18924 1 18574 1 21351 1 18595 1 19823 1 20844 1 19640 1 17735 1 19814 1 22239 1 20682 1 17819 1 21872 1 22117 1 21866 1

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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 16449.9803921569 + 3476.12487100103D[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) 16449.9803921569 250.627133 65.6353 0 0 D 3476.12487100103 481.061173 7.226 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.659047705744802 R-squared 0.434343878447487 Adjusted R-squared 0.426025406071714 F-TEST (value) 52.2143800961008 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 5.54896795179616e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1789.83573338394 Sum Squared Residuals 217838812.769866 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15107 16449.9803921569 -1342.98039215688 2 15024 16449.9803921569 -1425.98039215687 3 12083 16449.9803921569 -4366.98039215686 4 15761 16449.9803921569 -688.980392156862 5 16943 16449.9803921569 493.019607843138 6 15070 16449.9803921569 -1379.98039215686 7 13660 16449.9803921569 -2789.98039215686 8 14769 16449.9803921569 -1680.98039215686 9 14725 16449.9803921569 -1724.98039215686 10 15998 16449.9803921569 -451.980392156862 11 15371 16449.9803921569 -1078.98039215686 12 14957 16449.9803921569 -1492.98039215686 13 15470 16449.9803921569 -979.980392156862 14 15102 16449.9803921569 -1347.98039215686 15 11704 16449.9803921569 -4745.98039215686 16 16284 16449.9803921569 -165.980392156862 17 16727 16449.9803921569 277.019607843138 18 14969 16449.9803921569 -1480.98039215686 19 14861 16449.9803921569 -1588.98039215686 20 14583 16449.9803921569 -1866.98039215686 21 15306 16449.9803921569 -1143.98039215686 22 17904 16449.9803921569 1454.01960784314 23 16379 16449.9803921569 -70.9803921568624 24 15420 16449.9803921569 -1029.98039215686 25 17871 16449.9803921569 1421.01960784314 26 15913 16449.9803921569 -536.980392156862 27 13867 16449.9803921569 -2582.98039215686 28 17823 16449.9803921569 1373.01960784314 29 17872 16449.9803921569 1422.01960784314 30 17422 16449.9803921569 972.019607843138 31 16705 16449.9803921569 255.019607843138 32 15991 16449.9803921569 -458.980392156862 33 16584 16449.9803921569 134.019607843138 34 19124 16449.9803921569 2674.01960784314 35 17839 16449.9803921569 1389.01960784314 36 17209 16449.9803921569 759.019607843138 37 18587 16449.9803921569 2137.01960784314 38 16258 16449.9803921569 -191.980392156862 39 15142 16449.9803921569 -1307.98039215686 40 19202 16449.9803921569 2752.01960784314 41 17747 16449.9803921569 1297.01960784314 42 19090 16449.9803921569 2640.01960784314 43 18040 16449.9803921569 1590.01960784314 44 17516 16449.9803921569 1066.01960784314 45 17752 16449.9803921569 1302.01960784314 46 21073 16449.9803921569 4623.01960784314 47 17170 16449.9803921569 720.019607843138 48 19440 16449.9803921569 2990.01960784314 49 19795 16449.9803921569 3345.01960784314 50 17575 16449.9803921569 1125.01960784314 51 16165 16449.9803921569 -284.980392156862 52 19465 19926.1052631579 -461.105263157895 53 19932 19926.1052631579 5.89473684210531 54 19961 19926.1052631579 34.8947368421053 55 17343 19926.1052631579 -2583.10526315789 56 18924 19926.1052631579 -1002.10526315789 57 18574 19926.1052631579 -1352.10526315789 58 21351 19926.1052631579 1424.89473684211 59 18595 19926.1052631579 -1331.10526315789 60 19823 19926.1052631579 -103.105263157895 61 20844 19926.1052631579 917.894736842105 62 19640 19926.1052631579 -286.105263157895 63 17735 19926.1052631579 -2191.10526315789 64 19814 19926.1052631579 -112.105263157895 65 22239 19926.1052631579 2312.89473684211 66 20682 19926.1052631579 755.894736842105 67 17819 19926.1052631579 -2107.10526315789 68 21872 19926.1052631579 1945.89473684211 69 22117 19926.1052631579 2190.89473684211 70 21866 19926.1052631579 1939.89473684211 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.749043125119793 0.501913749760414 0.250956874880207 6 0.609770005525733 0.780459988948535 0.390229994474267 7 0.555945760583136 0.888108478833728 0.444054239416864 8 0.434797082979682 0.869594165959365 0.565202917020318 9 0.328877336434981 0.657754672869962 0.671122663565019 10 0.275900770473606 0.551801540947211 0.724099229526394 11 0.20088038365893 0.40176076731786 0.79911961634107 12 0.141367688263981 0.282735376527962 0.85863231173602 13 0.0987489344374507 0.197497868874901 0.90125106556255 14 0.066218547483475 0.13243709496695 0.933781452516525 15 0.353165513487906 0.706331026975813 0.646834486512094 16 0.337267658594837 0.674535317189675 0.662732341405163 17 0.347319262639274 0.694638525278548 0.652680737360726 18 0.303091502023378 0.606183004046757 0.696908497976622 19 0.270364025107857 0.540728050215714 0.729635974892143 20 0.259010438531052 0.518020877062104 0.740989561468948 21 0.230237195253711 0.460474390507422 0.769762804746289 22 0.360177891628103 0.720355783256206 0.639822108371897 23 0.335670823624235 0.671341647248469 0.664329176375765 24 0.309072038228503 0.618144076457005 0.690927961771497 25 0.392058505969058 0.784117011938116 0.607941494030942 26 0.358720714476163 0.717441428952325 0.641279285523837 27 0.502207191241917 0.995585617516166 0.497792808758083 28 0.559982632234421 0.880034735531158 0.440017367765579 29 0.601604929413235 0.79679014117353 0.398395070586765 30 0.59819740300092 0.80360519399816 0.40180259699908 31 0.568601874075318 0.862796251849363 0.431398125924681 32 0.552916948841261 0.894166102317478 0.447083051158739 33 0.52848154587982 0.94303690824036 0.47151845412018 34 0.654460765297753 0.691078469404493 0.345539234702247 35 0.645615484245801 0.708769031508397 0.354384515754199 36 0.613498179609957 0.773003640780086 0.386501820390043 37 0.639684464475891 0.720631071048218 0.360315535524109 38 0.621865759738755 0.756268480522491 0.378134240261245 39 0.710425319181834 0.579149361636333 0.289574680818166 40 0.759673880149818 0.480652239700365 0.240326119850182 41 0.732603785157558 0.534792429684884 0.267396214842442 42 0.754387998850854 0.491224002298292 0.245612001149146 43 0.723489782352954 0.553020435294092 0.276510217647046 44 0.685481499390326 0.629037001219348 0.314518500609674 45 0.645957027134559 0.708085945730882 0.354042972865441 46 0.83833963134593 0.323320737308140 0.161660368654070 47 0.803304084728967 0.393391830542067 0.196695915271033 48 0.817841012690925 0.364317974618151 0.182158987309075 49 0.880213166301082 0.239573667397835 0.119786833698918 50 0.851781109809713 0.296437780380574 0.148218890190287 51 0.798846383192676 0.402307233614648 0.201153616807324 52 0.738624975709731 0.522750048580538 0.261375024290269 53 0.665034566798108 0.669930866403785 0.334965433201892 54 0.582670450749881 0.834659098500237 0.417329549250119 55 0.677846131356011 0.644307737287978 0.322153868643989 56 0.628202761427215 0.74359447714557 0.371797238572785 57 0.610064167917571 0.779871664164858 0.389935832082429 58 0.562063291231242 0.875873417537516 0.437936708768758 59 0.545256370040816 0.909487259918368 0.454743629959184 60 0.449270033117103 0.898540066234206 0.550729966882897 61 0.352532039197368 0.705064078394737 0.647467960802632 62 0.265381550563408 0.530763101126815 0.734618449436592 63 0.417882695242099 0.835765390484199 0.5821173047579 64 0.332309535461592 0.664619070923183 0.667690464538408 65 0.262674967998746 0.525349935997492 0.737325032001254 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/10glx51228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/10glx51228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/1zrwl1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/1zrwl1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/2wu3e1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/2wu3e1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/3734b1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/3734b1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/46pe71228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/46pe71228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/5wwfa1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/5wwfa1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/6ivgz1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/6ivgz1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/7xf5d1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/7xf5d1228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/8yt371228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/8yt371228662773.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/9g8zr1228662773.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228662832d9toh8ry70z14p8/9g8zr1228662773.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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') }

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