*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 05:00:57 -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/t1227787397zukxwj3dhstt0cu.htm/, Retrieved Thu, 27 Nov 2008 12:03:27 +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/t1227787397zukxwj3dhstt0cu.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 «

1 1 16 1 29 1 56 1 51 1 50 1 37 1 20 1 47 1 49 1 39 1 30 1 0 1 14 1 36 1 72 1 41 1 43 1 44 1 18 1 56 1 57 1 49 1 31 1 17 1 22 1 49 1 65 1 55 1 48 1 50 1 15 1 60 1 56 1 40 1 31 1 20 0 27 0 14 0 67 0 64 0 46 0 60 0 22 0 65 0 58 0 42 0 32 0 25 0 20 0 27 0 72 0 68 0 51 0 53 0 18 0 54 0 67 0 40 0 45 0 25 1 36 1 50 1 64 1 50 1 43 1 51 1 12 1 58 1 50 1 50 1 31 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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation S[t] = + 36.1111111111111 -4.16666666666667D[t] -18.6666666666667M1[t] -10.8333333333334M2[t] + 0.833333333333295M3[t] + 32.6666666666666M4[t] + 21.5M5[t] + 13.5M6[t] + 15.8333333333333M7[t] -15.8333333333334M8[t] + 23.3333333333333M9[t] + 22.8333333333333M10[t] + 9.99999999999999M11[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) 36.1111111111111 3.362517 10.7393 0 0 D -4.16666666666667 1.906368 -2.1857 0.032822 0.016411 M1 -18.6666666666667 4.402569 -4.2399 8e-05 4e-05 M2 -10.8333333333334 4.402569 -2.4607 0.016813 0.008407 M3 0.833333333333295 4.402569 0.1893 0.85052 0.42526 M4 32.6666666666666 4.402569 7.4199 0 0 M5 21.5 4.402569 4.8835 8e-06 4e-06 M6 13.5 4.402569 3.0664 0.003267 0.001634 M7 15.8333333333333 4.402569 3.5964 0.00066 0.00033 M8 -15.8333333333334 4.402569 -3.5964 0.00066 0.00033 M9 23.3333333333333 4.402569 5.2999 2e-06 1e-06 M10 22.8333333333333 4.402569 5.1864 3e-06 1e-06 M11 9.99999999999999 4.402569 2.2714 0.026785 0.013393 Multiple Linear Regression - Regression Statistics Multiple R 0.919686902962468 R-squared 0.845823999480696 Adjusted R-squared 0.8144661688666 F-TEST (value) 26.9732944823194 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.62547272468749 Sum Squared Residuals 3430.72222222222 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 13.2777777777777 -12.2777777777777 2 16 21.1111111111111 -5.11111111111111 3 29 32.7777777777778 -3.77777777777781 4 56 64.6111111111111 -8.6111111111111 5 51 53.4444444444444 -2.44444444444441 6 50 45.4444444444444 4.5555555555556 7 37 47.7777777777778 -10.7777777777778 8 20 16.1111111111111 3.88888888888886 9 47 55.2777777777778 -8.27777777777779 10 49 54.7777777777778 -5.77777777777777 11 39 41.9444444444444 -2.94444444444443 12 30 31.9444444444444 -1.94444444444444 13 0 13.2777777777778 -13.2777777777778 14 14 21.1111111111111 -7.11111111111111 15 36 32.7777777777778 3.22222222222223 16 72 64.6111111111111 7.38888888888889 17 41 53.4444444444444 -12.4444444444445 18 43 45.4444444444444 -2.44444444444445 19 44 47.7777777777778 -3.77777777777777 20 18 16.1111111111111 1.88888888888889 21 56 55.2777777777778 0.722222222222227 22 57 54.7777777777778 2.22222222222222 23 49 41.9444444444444 7.05555555555556 24 31 31.9444444444445 -0.944444444444454 25 17 13.2777777777778 3.72222222222221 26 22 21.1111111111111 0.88888888888889 27 49 32.7777777777778 16.2222222222222 28 65 64.6111111111111 0.388888888888884 29 55 53.4444444444444 1.55555555555555 30 48 45.4444444444445 2.55555555555555 31 50 47.7777777777778 2.22222222222223 32 15 16.1111111111111 -1.11111111111111 33 60 55.2777777777778 4.72222222222222 34 56 54.7777777777778 1.22222222222222 35 40 41.9444444444444 -1.94444444444444 36 31 31.9444444444445 -0.944444444444454 37 20 17.4444444444445 2.55555555555554 38 27 25.2777777777778 1.72222222222222 39 14 36.9444444444444 -22.9444444444444 40 67 68.7777777777778 -1.77777777777777 41 64 57.6111111111111 6.38888888888888 42 46 49.6111111111111 -3.61111111111112 43 60 51.9444444444444 8.05555555555557 44 22 20.2777777777778 1.72222222222223 45 65 59.4444444444444 5.55555555555556 46 58 58.9444444444444 -0.94444444444445 47 42 46.1111111111111 -4.11111111111112 48 32 36.1111111111111 -4.11111111111113 49 25 17.4444444444445 7.55555555555554 50 20 25.2777777777778 -5.27777777777778 51 27 36.9444444444444 -9.94444444444444 52 72 68.7777777777778 3.22222222222223 53 68 57.6111111111111 10.3888888888889 54 51 49.6111111111111 1.38888888888888 55 53 51.9444444444444 1.05555555555557 56 18 20.2777777777778 -2.27777777777777 57 54 59.4444444444444 -5.44444444444444 58 67 58.9444444444445 8.05555555555555 59 40 46.1111111111111 -6.11111111111112 60 45 36.1111111111111 8.88888888888887 61 25 13.2777777777778 11.7222222222222 62 36 21.1111111111111 14.8888888888889 63 50 32.7777777777778 17.2222222222222 64 64 64.6111111111111 -0.611111111111116 65 50 53.4444444444444 -3.44444444444445 66 43 45.4444444444444 -2.44444444444445 67 51 47.7777777777778 3.22222222222223 68 12 16.1111111111111 -4.11111111111111 69 58 55.2777777777778 2.72222222222223 70 50 54.7777777777778 -4.77777777777778 71 50 41.9444444444444 8.05555555555556 72 31 31.9444444444445 -0.944444444444454 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.585843765965163 0.828312468069675 0.414156234034837 17 0.609086761944486 0.781826476111028 0.390913238055514 18 0.525804714915339 0.948390570169322 0.474195285084661 19 0.46737420241504 0.93474840483008 0.53262579758496 20 0.348387497426054 0.696774994852108 0.651612502573946 21 0.325709354623427 0.651418709246854 0.674290645376573 22 0.285561367007984 0.571122734015968 0.714438632992016 23 0.288431551300227 0.576863102600454 0.711568448699773 24 0.207897401154502 0.415794802309004 0.792102598845498 25 0.387348905975275 0.77469781195055 0.612651094024725 26 0.346954095993499 0.693908191986997 0.653045904006501 27 0.615347098791005 0.769305802417989 0.384652901208995 28 0.527614284259135 0.94477143148173 0.472385715740865 29 0.507322842445463 0.985354315109075 0.492677157554537 30 0.422873728135389 0.845747456270777 0.577126271864611 31 0.40534010732583 0.81068021465166 0.59465989267417 32 0.334486702583959 0.668973405167918 0.665513297416041 33 0.301566327131478 0.603132654262956 0.698433672868522 34 0.236467491557371 0.472934983114742 0.763532508442629 35 0.185709578264434 0.371419156528867 0.814290421735566 36 0.139082013677096 0.278164027354191 0.860917986322904 37 0.102498781911024 0.204997563822048 0.897501218088976 38 0.0720036942742354 0.144007388548471 0.927996305725765 39 0.619482390602686 0.761035218794628 0.380517609397314 40 0.540240830763829 0.919518338472343 0.459759169236171 41 0.531131296041225 0.93773740791755 0.468868703958775 42 0.452785437907027 0.905570875814054 0.547214562092973 43 0.452597242990236 0.905194485980472 0.547402757009764 44 0.387016083770360 0.774032167540719 0.61298391622964 45 0.351919819804844 0.703839639609688 0.648080180195156 46 0.271435693463261 0.542871386926522 0.728564306536739 47 0.212659435614162 0.425318871228324 0.787340564385838 48 0.168867753923642 0.337735507847284 0.831132246076358 49 0.140375840886598 0.280751681773196 0.859624159113402 50 0.187289321663656 0.374578643327311 0.812710678336344 51 0.611756353646239 0.776487292707522 0.388243646353761 52 0.499630094609877 0.999260189219754 0.500369905390123 53 0.560507964149146 0.878984071701708 0.439492035850854 54 0.437786744608080 0.875573489216161 0.56221325539192 55 0.305100216463809 0.610200432927618 0.694899783536191 56 0.184566553324979 0.369133106649957 0.815433446675021 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/Nov/27/t1227787397zukxwj3dhstt0cu/10jl2s1227787252.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/10jl2s1227787252.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/1r7751227787251.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/1r7751227787251.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/2rof51227787251.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/2rof51227787251.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/3zxxu1227787251.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/3zxxu1227787251.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/4turn1227787251.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/4turn1227787251.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/5kklf1227787251.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/5kklf1227787251.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/6pj1q1227787252.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/6pj1q1227787252.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/72yy61227787252.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/72yy61227787252.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/8dj831227787252.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/8dj831227787252.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/9mu4z1227787252.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227787397zukxwj3dhstt0cu/9mu4z1227787252.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