final

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 03:04:18 -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/27/t1227780335gk4c65wk8du7zs8.htm/, Retrieved Thu, 27 Nov 2008 10:05:35 +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/27/t1227780335gk4c65wk8du7zs8.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 «

9492.49 0 9682.35 0 9762.12 0 10124.63 0 10540.05 0 10601.61 0 10323.73 0 10418.4 0 10092.96 0 10364.91 0 10152.09 0 10032.8 0 10204.59 0 10001.6 0 10411.75 0 10673.38 0 10539.51 0 10723.78 0 10682.06 0 10283.19 0 10377.18 0 10486.64 0 10545.38 0 10554.27 0 10532.54 0 10324.31 0 10695.25 0 10827.81 0 10872.48 0 10971.19 0 11145.65 0 11234.68 0 11333.88 0 10997.97 0 11036.89 0 11257.35 0 11533.59 0 11963.12 0 12185.15 0 12377.62 0 12512.89 0 12631.48 0 12268.53 0 12754.8 0 13407.75 1 13480.21 1 13673.28 1 13239.71 1 13557.69 1 13901.28 1 13200.58 1 13406.97 1 12538.12 1 12419.57 1 12193.88 1 12656.63 1 12812.48 1 12056.67 1 11322.38 1 11530.75 1 11114.08 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation X[t] = + 10528.8355454545 + 1985.35113636364Y[t] -118.122590909089M1[t] + 248.626227272727M2[t] + 325.064227272727M3[t] + 556.176227272727M4[t] + 474.704227272727M5[t] + 543.620227272727M6[t] + 396.864227272727M7[t] + 543.634227272727M8[t] + 281.874000000000M9[t] + 154.304000000000M10[t] + 23.0279999999997M11[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) 10528.8355454545 406.638879 25.8923 0 0 Y 1985.35113636364 256.838785 7.73 0 0 M1 -118.122590909089 533.004943 -0.2216 0.825553 0.412776 M2 248.626227272727 558.784244 0.4449 0.658362 0.329181 M3 325.064227272727 558.784244 0.5817 0.563468 0.281734 M4 556.176227272727 558.784244 0.9953 0.324566 0.162283 M5 474.704227272727 558.784244 0.8495 0.399804 0.199902 M6 543.620227272727 558.784244 0.9729 0.335499 0.167749 M7 396.864227272727 558.784244 0.7102 0.480999 0.2405 M8 543.634227272727 558.784244 0.9729 0.335487 0.167743 M9 281.874000000000 556.418174 0.5066 0.614764 0.307382 M10 154.304000000000 556.418174 0.2773 0.782728 0.391364 M11 23.0279999999997 556.418174 0.0414 0.96716 0.48358 Multiple Linear Regression - Regression Statistics Multiple R 0.749292474625416 R-squared 0.561439212530279 Adjusted R-squared 0.451799015662849 F-TEST (value) 5.12074247011007 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 1.99781687993950e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 879.774381340634 Sum Squared Residuals 37152142.1790382 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9492.49 10410.7129545454 -918.222954545446 2 9682.35 10777.4617727273 -1095.11177272727 3 9762.12 10853.8997727273 -1091.77977272727 4 10124.63 11085.0117727273 -960.381772727274 5 10540.05 11003.5397727273 -463.489772727273 6 10601.61 11072.4557727273 -470.845772727272 7 10323.73 10925.6997727273 -601.969772727273 8 10418.4 11072.4697727273 -654.069772727273 9 10092.96 10810.7095454545 -717.749545454546 10 10364.91 10683.1395454545 -318.229545454545 11 10152.09 10551.8635454545 -399.773545454545 12 10032.8 10528.8355454545 -496.035545454546 13 10204.59 10410.7129545455 -206.122954545457 14 10001.6 10777.4617727273 -775.861772727272 15 10411.75 10853.8997727273 -442.149772727273 16 10673.38 11085.0117727273 -411.631772727273 17 10539.51 11003.5397727273 -464.029772727272 18 10723.78 11072.4557727273 -348.675772727272 19 10682.06 10925.6997727273 -243.639772727273 20 10283.19 11072.4697727273 -789.279772727272 21 10377.18 10810.7095454545 -433.529545454545 22 10486.64 10683.1395454545 -196.499545454546 23 10545.38 10551.8635454545 -6.48354545454617 24 10554.27 10528.8355454545 25.4344545454548 25 10532.54 10410.7129545455 121.827045454544 26 10324.31 10777.4617727273 -453.151772727273 27 10695.25 10853.8997727273 -158.649772727273 28 10827.81 11085.0117727273 -257.201772727273 29 10872.48 11003.5397727273 -131.059772727273 30 10971.19 11072.4557727273 -101.265772727273 31 11145.65 10925.6997727273 219.950227272727 32 11234.68 11072.4697727273 162.210227272727 33 11333.88 10810.7095454545 523.170454545454 34 10997.97 10683.1395454545 314.830454545454 35 11036.89 10551.8635454545 485.026454545454 36 11257.35 10528.8355454545 728.514454545455 37 11533.59 10410.7129545455 1122.87704545454 38 11963.12 10777.4617727273 1185.65822727273 39 12185.15 10853.8997727273 1331.25022727273 40 12377.62 11085.0117727273 1292.60822727273 41 12512.89 11003.5397727273 1509.35022727273 42 12631.48 11072.4557727273 1559.02422727273 43 12268.53 10925.6997727273 1342.83022727273 44 12754.8 11072.4697727273 1682.33022727273 45 13407.75 12796.0606818182 611.689318181819 46 13480.21 12668.4906818182 811.719318181818 47 13673.28 12537.2146818182 1136.06531818182 48 13239.71 12514.1866818182 725.523318181817 49 13557.69 12396.0640909091 1161.62590909091 50 13901.28 12762.8129090909 1138.46709090909 51 13200.58 12839.2509090909 361.329090909091 52 13406.97 13070.3629090909 336.607090909091 53 12538.12 12988.8909090909 -450.770909090908 54 12419.57 13057.8069090909 -638.236909090909 55 12193.88 12911.0509090909 -717.170909090909 56 12656.63 13057.8209090909 -401.190909090909 57 12812.48 12796.0606818182 16.4193181818184 58 12056.67 12668.4906818182 -611.820681818181 59 11322.38 12537.2146818182 -1214.83468181818 60 11530.75 12514.1866818182 -983.436681818182 61 11114.08 12396.0640909091 -1281.98409090909 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.164107488083568 0.328214976167136 0.835892511916432 17 0.0703120746348246 0.140624149269649 0.929687925365175 18 0.0279623990254028 0.0559247980508055 0.972037600974597 19 0.0127019529240941 0.0254039058481882 0.987298047075906 20 0.00538724265940145 0.0107744853188029 0.994612757340599 21 0.00243959979914325 0.0048791995982865 0.997560400200857 22 0.000854123761935179 0.00170824752387036 0.999145876238065 23 0.000399544634440845 0.00079908926888169 0.99960045536556 24 0.000248203941278806 0.000496407882557611 0.999751796058721 25 0.000322852128869755 0.000645704257739511 0.99967714787113 26 0.000403987033899377 0.000807974067798754 0.9995960129661 27 0.000516247480839078 0.00103249496167816 0.99948375251916 28 0.000496599358381801 0.000993198716763602 0.999503400641618 29 0.000335809276511981 0.000671618553023961 0.999664190723488 30 0.000216032281992265 0.000432064563984529 0.999783967718008 31 0.000214151167023984 0.000428302334047967 0.999785848832976 32 0.000471361770572493 0.000942723541144986 0.999528638229428 33 0.00136944336806041 0.00273888673612083 0.99863055663194 34 0.00125760760908808 0.00251521521817616 0.998742392390912 35 0.00117872623625586 0.00235745247251173 0.998821273763744 36 0.0014630627757243 0.0029261255514486 0.998536937224276 37 0.00426743845328222 0.00853487690656444 0.995732561546718 38 0.0303162066453643 0.0606324132907285 0.969683793354636 39 0.0601630292578994 0.120326058515799 0.9398369707421 40 0.090255165774887 0.180510331549774 0.909744834225113 41 0.0949454435795047 0.189890887159009 0.905054556420495 42 0.0884006895142927 0.176801379028585 0.911599310485707 43 0.064996141374221 0.129992282748442 0.935003858625779 44 0.0534266913434238 0.106853382686848 0.946573308656576 45 0.0254953753736084 0.0509907507472167 0.974504624626392 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.566666666666667 NOK 5% type I error level 19 0.633333333333333 NOK 10% type I error level 22 0.733333333333333 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/10agk01227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/10agk01227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/1xaws1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/1xaws1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/2970j1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/2970j1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/3yzfp1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/3yzfp1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/4xh0c1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/4xh0c1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/5i7dk1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/5i7dk1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/6cbuc1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/6cbuc1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/7bkn01227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/7bkn01227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/8sp8t1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/8sp8t1227780247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/9svmi1227780247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227780335gk4c65wk8du7zs8/9svmi1227780247.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