Multiple Lineair Regression 1 Bouwproductie

*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: Thu, 11 Dec 2008 12:46:38 -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/11/t1229028216gaclqvc48rzyznt.htm/, Retrieved Thu, 11 Dec 2008 20:43:36 +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/11/t1229028216gaclqvc48rzyznt.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 «

82.7 0 88.9 0 105.9 0 100.8 0 94 0 105 0 58.5 0 87.6 0 113.1 0 112.5 0 89.6 0 74.5 0 82.7 0 90.1 0 109.4 0 96 0 89.2 0 109.1 0 49.1 0 92.9 0 107.7 0 103.5 0 91.1 0 79.8 0 71.9 0 82.9 0 90.1 0 100.7 0 90.7 0 108.8 0 44.1 0 93.6 0 107.4 0 96.5 0 93.6 0 76.5 0 76.7 1 84 1 103.3 1 88.5 1 99 1 105.9 1 44.7 1 94 1 107.1 1 104.8 1 102.5 1 77.7 1 85.2 1 91.3 1 106.5 1 92.4 1 97.5 1 107 1 51.1 1 98.6 1 102.2 1 114.3 1 99.4 1 72.5 1 92.3 1 99.4 1 85.9 1 109.4 1 97.6 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Bouwproductie[t] = + 90.8472222222222 + 1.9389846743295d[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) 90.8472222222222 2.724766 33.3413 0 0 d 1.9389846743295 4.079312 0.4753 0.636202 0.318101 Multiple Linear Regression - Regression Statistics Multiple R 0.0597777939827954 R-squared 0.00357338465344953 Adjusted R-squared -0.0122429108282416 F-TEST (value) 0.225930569998900 F-TEST (DF numerator) 1 F-TEST (DF denominator) 63 p-value 0.636201700932833 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.3485954957065 Sum Squared Residuals 16838.4242049808 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 82.7 90.8472222222221 -8.14722222222212 2 88.9 90.8472222222222 -1.94722222222222 3 105.9 90.8472222222222 15.0527777777778 4 100.8 90.8472222222222 9.95277777777777 5 94 90.8472222222222 3.15277777777778 6 105 90.8472222222222 14.1527777777778 7 58.5 90.8472222222222 -32.3472222222222 8 87.6 90.8472222222222 -3.24722222222223 9 113.1 90.8472222222222 22.2527777777778 10 112.5 90.8472222222222 21.6527777777778 11 89.6 90.8472222222222 -1.24722222222223 12 74.5 90.8472222222222 -16.3472222222222 13 82.7 90.8472222222222 -8.14722222222222 14 90.1 90.8472222222222 -0.74722222222223 15 109.4 90.8472222222222 18.5527777777778 16 96 90.8472222222222 5.15277777777778 17 89.2 90.8472222222222 -1.64722222222222 18 109.1 90.8472222222222 18.2527777777778 19 49.1 90.8472222222222 -41.7472222222222 20 92.9 90.8472222222222 2.05277777777778 21 107.7 90.8472222222222 16.8527777777778 22 103.5 90.8472222222222 12.6527777777778 23 91.1 90.8472222222222 0.252777777777770 24 79.8 90.8472222222222 -11.0472222222222 25 71.9 90.8472222222222 -18.9472222222222 26 82.9 90.8472222222222 -7.94722222222222 27 90.1 90.8472222222222 -0.74722222222223 28 100.7 90.8472222222222 9.85277777777778 29 90.7 90.8472222222222 -0.147222222222222 30 108.8 90.8472222222222 17.9527777777778 31 44.1 90.8472222222222 -46.7472222222222 32 93.6 90.8472222222222 2.75277777777777 33 107.4 90.8472222222222 16.5527777777778 34 96.5 90.8472222222222 5.65277777777778 35 93.6 90.8472222222222 2.75277777777777 36 76.5 90.8472222222222 -14.3472222222222 37 76.7 92.7862068965517 -16.0862068965517 38 84 92.7862068965517 -8.78620689655173 39 103.3 92.7862068965517 10.5137931034483 40 88.5 92.7862068965517 -4.28620689655172 41 99 92.7862068965517 6.21379310344827 42 105.9 92.7862068965517 13.1137931034483 43 44.7 92.7862068965517 -48.0862068965517 44 94 92.7862068965517 1.21379310344828 45 107.1 92.7862068965517 14.3137931034483 46 104.8 92.7862068965517 12.0137931034483 47 102.5 92.7862068965517 9.71379310344827 48 77.7 92.7862068965517 -15.0862068965517 49 85.2 92.7862068965517 -7.58620689655172 50 91.3 92.7862068965517 -1.48620689655173 51 106.5 92.7862068965517 13.7137931034483 52 92.4 92.7862068965517 -0.386206896551718 53 97.5 92.7862068965517 4.71379310344828 54 107 92.7862068965517 14.2137931034483 55 51.1 92.7862068965517 -41.6862068965517 56 98.6 92.7862068965517 5.81379310344827 57 102.2 92.7862068965517 9.41379310344828 58 114.3 92.7862068965517 21.5137931034483 59 99.4 92.7862068965517 6.61379310344828 60 72.5 92.7862068965517 -20.2862068965517 61 92.3 92.7862068965517 -0.486206896551727 62 99.4 92.7862068965517 6.61379310344828 63 85.9 92.7862068965517 -6.88620689655172 64 109.4 92.7862068965517 16.6137931034483 65 97.6 92.7862068965517 4.81379310344827 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.247098426300116 0.494196852600231 0.752901573699884 6 0.178694767467817 0.357389534935634 0.821305232532183 7 0.687516314393811 0.624967371212377 0.312483685606189 8 0.567571031374338 0.864857937251324 0.432428968625662 9 0.632193572374767 0.735612855250465 0.367806427625233 10 0.655406787579566 0.689186424840868 0.344593212420434 11 0.560367173577711 0.879265652844578 0.439632826422289 12 0.579040742313236 0.841918515373527 0.420959257686764 13 0.510951375430516 0.978097249138967 0.489048624569484 14 0.416882308908128 0.833764617816256 0.583117691091872 15 0.427794964176638 0.855589928353275 0.572205035823362 16 0.346376837330167 0.692753674660335 0.653623162669833 17 0.271937547359627 0.543875094719255 0.728062452640373 18 0.278886975739037 0.557773951478074 0.721113024260963 19 0.714018244368505 0.571963511262991 0.285981755631495 20 0.642957928241627 0.714084143516747 0.357042071758373 21 0.640011996018301 0.719976007963398 0.359988003981699 22 0.606814344645104 0.786371310709792 0.393185655354896 23 0.532337571402348 0.935324857195303 0.467662428597652 24 0.490432840017004 0.980865680034007 0.509567159982996 25 0.510831399827477 0.978337200345046 0.489168600172523 26 0.452098772864909 0.904197545729817 0.547901227135091 27 0.379074564653221 0.758149129306442 0.620925435346779 28 0.335145523838112 0.670291047676224 0.664854476161888 29 0.2706756106796 0.5413512213592 0.7293243893204 30 0.291222012061563 0.582444024123127 0.708777987938437 31 0.748908131225028 0.502183737549945 0.251091868774972 32 0.688078404553411 0.623843190893177 0.311921595446589 33 0.684801214892936 0.630397570214127 0.315198785107064 34 0.632173304587535 0.73565339082493 0.367826695412465 35 0.582797958528223 0.834404082943554 0.417202041471777 36 0.53403581687202 0.93192836625596 0.46596418312798 37 0.500606425748222 0.998787148503556 0.499393574251778 38 0.443592550958576 0.887185101917153 0.556407449041424 39 0.422908415564928 0.845816831129857 0.577091584435072 40 0.354576547786686 0.709153095573372 0.645423452213314 41 0.299920168638863 0.599840337277726 0.700079831361137 42 0.275487076945042 0.550974153890084 0.724512923054958 43 0.795567727121026 0.408864545757949 0.204432272878974 44 0.737676151032425 0.52464769793515 0.262323848967575 45 0.723107675344755 0.55378464931049 0.276892324655245 46 0.689158420320946 0.621683159358109 0.310841579679054 47 0.63793564583274 0.724128708334521 0.362064354167261 48 0.629648665484197 0.740702669031607 0.370351334515803 49 0.568095890198741 0.863808219602518 0.431904109801259 50 0.482417121890004 0.964834243780007 0.517582878109996 51 0.443043225040199 0.886086450080399 0.556956774959801 52 0.353344124165868 0.706688248331735 0.646655875834132 53 0.272284981685594 0.544569963371188 0.727715018314406 54 0.240161362865138 0.480322725730276 0.759838637134862 55 0.823601548434078 0.352796903131844 0.176398451565922 56 0.736112370612986 0.527775258774028 0.263887629387014 57 0.639604874543123 0.720790250913754 0.360395125456877 58 0.709399255134434 0.581201489731132 0.290600744865566 59 0.586635278030023 0.826729443939954 0.413364721969977 60 0.782762089114104 0.434475821771792 0.217237910885896 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/11/t1229028216gaclqvc48rzyznt/1068xg1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/1068xg1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/1e0cz1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/1e0cz1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/20j1c1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/20j1c1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/3nxz11229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/3nxz11229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/4lv6i1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/4lv6i1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/57sgt1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/57sgt1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/6gk361229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/6gk361229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/78v4m1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/78v4m1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/8rg7u1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/8rg7u1229024786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/978ny1229024786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229028216gaclqvc48rzyznt/978ny1229024786.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