Q3 - Bob Leysen - Seatbelt Law

*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: Sat, 22 Nov 2008 08:53:14 -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/Nov/22/t12273692446xdv89pemxjl4ob.htm/, Retrieved Sat, 22 Nov 2008 15:54:13 +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/Nov/22/t12273692446xdv89pemxjl4ob.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:

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 1 19202 1 17747 1 19090 1 18040 1 17516 1 17752 1 21073 1 17170 1 19440 1 19795 1 17575 1 16165 1 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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation x[t] = + 16027.2578125 + 3356.35546875y[t] + 799.623697916657M1[t] -560.709635416667M2[t] -3256.10221354167M3[t] + 352.731119791668M4[t] + 871.231119791667M5[t] + 160.231119791667M6[t] -1300.76888020833M7[t] -429.602213541666M8[t] -195.768880208333M9[t] + 1847.23111979167M10[t] -298.999999999999M11[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) 16027.2578125 569.126074 28.1612 0 0 y 3356.35546875 301.072936 11.148 0 0 M1 799.623697916657 753.417937 1.0613 0.293017 0.146509 M2 -560.709635416667 753.417937 -0.7442 0.4598 0.2299 M3 -3256.10221354167 753.752062 -4.3199 6.3e-05 3.2e-05 M4 352.731119791668 753.752062 0.468 0.641593 0.320796 M5 871.231119791667 753.752062 1.1559 0.252561 0.12628 M6 160.231119791667 753.752062 0.2126 0.832415 0.416207 M7 -1300.76888020833 753.752062 -1.7257 0.089815 0.044907 M8 -429.602213541666 753.752062 -0.57 0.57095 0.285475 M9 -195.768880208333 753.752062 -0.2597 0.796011 0.398006 M10 1847.23111979167 753.752062 2.4507 0.017349 0.008674 M11 -298.999999999999 786.640073 -0.3801 0.705286 0.352643 Multiple Linear Regression - Regression Statistics Multiple R 0.878081445256386 R-squared 0.771027024503544 Adjusted R-squared 0.722822187556922 F-TEST (value) 15.9948061925261 F-TEST (DF numerator) 12 F-TEST (DF denominator) 57 p-value 4.18554080283684e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1243.78716549242 Sum Squared Residuals 88179371.2434897 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15107 16826.8815104167 -1719.88151041669 2 15024 15466.5481770833 -442.548177083334 3 12083 12771.1555989583 -688.155598958322 4 15761 16379.9889322917 -618.988932291667 5 16943 16898.4889322917 44.5110677083356 6 15070 16187.4889322917 -1117.48893229167 7 13660 14726.4889322917 -1066.48893229167 8 14769 15597.6555989583 -828.655598958333 9 14725 15831.4889322917 -1106.48893229167 10 15998 17874.4889322917 -1876.48893229167 11 15371 15728.2578125 -357.2578125 12 14957 16027.2578125 -1070.2578125 13 15470 16826.8815104167 -1356.88151041666 14 15102 15466.5481770833 -364.548177083333 15 11704 12771.1555989583 -1067.15559895834 16 16284 16379.9889322917 -95.9889322916668 17 16727 16898.4889322917 -171.488932291667 18 14969 16187.4889322917 -1218.48893229167 19 14861 14726.4889322917 134.511067708333 20 14583 15597.6555989583 -1014.65559895833 21 15306 15831.4889322917 -525.488932291667 22 17904 17874.4889322917 29.5110677083338 23 16379 15728.2578125 650.7421875 24 15420 16027.2578125 -607.2578125 25 17871 16826.8815104167 1044.11848958334 26 15913 15466.5481770833 446.451822916667 27 13867 12771.1555989583 1095.84440104166 28 17823 16379.9889322917 1443.01106770833 29 17872 16898.4889322917 973.511067708333 30 17422 16187.4889322917 1234.51106770833 31 16705 14726.4889322917 1978.51106770833 32 15991 15597.6555989583 393.344401041666 33 16584 15831.4889322917 752.511067708333 34 19124 17874.4889322917 1249.51106770833 35 17839 15728.2578125 2110.7421875 36 17209 16027.2578125 1181.7421875 37 18587 16826.8815104167 1760.11848958334 38 16258 15466.5481770833 791.451822916667 39 15142 16127.5110677083 -985.511067708335 40 19202 19736.3444010417 -534.344401041667 41 17747 20254.8444010417 -2507.84440104167 42 19090 19543.8444010417 -453.844401041667 43 18040 18082.8444010417 -42.8444010416669 44 17516 18954.0110677083 -1438.01106770833 45 17752 19187.8444010417 -1435.84440104167 46 21073 21230.8444010417 -157.844401041667 47 17170 19084.61328125 -1914.61328125 48 19440 19383.61328125 56.3867187500005 49 19795 20183.2369791667 -388.236979166663 50 17575 18822.9036458333 -1247.90364583333 51 16165 16127.5110677083 37.4889322916646 52 19465 19736.3444010417 -271.344401041667 53 19932 20254.8444010417 -322.844401041667 54 19961 19543.8444010417 417.155598958333 55 17343 18082.8444010417 -739.844401041667 56 18924 18954.0110677083 -30.0110677083335 57 18574 19187.8444010417 -613.844401041666 58 21351 21230.8444010417 120.155598958333 59 18595 19084.61328125 -489.61328125 60 19823 19383.61328125 439.38671875 61 20844 20183.2369791667 660.763020833337 62 19640 18822.9036458333 817.096354166667 63 17735 16127.5110677083 1607.48893229166 64 19814 19736.3444010417 77.6555989583333 65 22239 20254.8444010417 1984.15559895833 66 20682 19543.8444010417 1138.15559895833 67 17819 18082.8444010417 -263.844401041667 68 21872 18954.0110677083 2917.98893229167 69 22117 19187.8444010417 2929.15559895833 70 21866 21230.8444010417 635.155598958333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0203095634006087 0.0406191268012173 0.979690436599391 17 0.00473552690004265 0.0094710538000853 0.995264473099957 18 0.00108958788177853 0.00217917576355707 0.998910412118222 19 0.00600376402641667 0.0120075280528333 0.993996235973583 20 0.00230588026155717 0.00461176052311433 0.997694119738443 21 0.00132317058859954 0.00264634117719908 0.9986768294114 22 0.0140794657613572 0.0281589315227144 0.985920534238643 23 0.0111398622168204 0.0222797244336409 0.98886013778318 24 0.00714284880012891 0.0142856976002578 0.992857151199871 25 0.0803499604024563 0.160699920804913 0.919650039597544 26 0.0608027222509663 0.121605444501933 0.939197277749034 27 0.098637821924109 0.197275643848218 0.901362178075891 28 0.120599703326735 0.24119940665347 0.879400296673265 29 0.0966747979405797 0.193349595881159 0.90332520205942 30 0.155044580000527 0.310089160001054 0.844955419999473 31 0.224155374246377 0.448310748492754 0.775844625753623 32 0.212465811553603 0.424931623107206 0.787534188446397 33 0.205032262324711 0.410064524649422 0.79496773767529 34 0.224996552745533 0.449993105491066 0.775003447254467 35 0.259653075798187 0.519306151596374 0.740346924201813 36 0.254653232276472 0.509306464552945 0.745346767723527 37 0.281791473229174 0.563582946458348 0.718208526770826 38 0.224412009860683 0.448824019721366 0.775587990139317 39 0.198670681918009 0.397341363836018 0.801329318081991 40 0.144923660177800 0.289847320355600 0.8550763398222 41 0.255607061089546 0.511214122179092 0.744392938910454 42 0.227481059004016 0.454962118008032 0.772518940995984 43 0.171706822985333 0.343413645970665 0.828293177014667 44 0.238853651230438 0.477707302460877 0.761146348769562 45 0.311755594407930 0.623511188815859 0.68824440559207 46 0.248492544667542 0.496985089335084 0.751507455332458 47 0.240090941561628 0.480181883123255 0.759909058438372 48 0.182420005873389 0.364840011746779 0.81757999412661 49 0.135985498806891 0.271970997613781 0.86401450119311 50 0.13262062316562 0.26524124633124 0.86737937683438 51 0.113158803738113 0.226317607476226 0.886841196261887 52 0.0655477140959039 0.131095428191808 0.934452285904096 53 0.0717121327679631 0.143424265535926 0.928287867232037 54 0.0402793117494645 0.080558623498929 0.959720688250535 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.102564102564103 NOK 5% type I error level 9 0.230769230769231 NOK 10% type I error level 10 0.256410256410256 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/10nkno1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/10nkno1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/1zuhb1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/1zuhb1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/2fdm41227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/2fdm41227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/32ent1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/32ent1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/48z791227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/48z791227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/5g5rr1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/5g5rr1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/63pce1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/63pce1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/7isrq1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/7isrq1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/82p1t1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/82p1t1227369189.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/91p5v1227369189.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t12273692446xdv89pemxjl4ob/91p5v1227369189.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