Multiple Regression werklh ecogr 2 vertragingen

*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, 17 Dec 2009 07:43:11 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz.htm/, Retrieved Thu, 17 Dec 2009 15:44:30 +0100

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/2009/Dec/17/t1261061058br7p8r69t2o2xuz.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8,7 110,3 9,3 9,3 8,2 103,9 8,7 9,3 8,3 101,6 8,2 8,7 8,5 94,6 8,3 8,2 8,6 95,9 8,5 8,3 8,5 104,7 8,6 8,5 8,2 102,8 8,5 8,6 8,1 98,1 8,2 8,5 7,9 113,9 8,1 8,2 8,6 80,9 7,9 8,1 8,7 95,7 8,6 7,9 8,7 113,2 8,7 8,6 8,5 105,9 8,7 8,7 8,4 108,8 8,5 8,7 8,5 102,3 8,4 8,5 8,7 99 8,5 8,4 8,7 100,7 8,7 8,5 8,6 115,5 8,7 8,7 8,5 100,7 8,6 8,7 8,3 109,9 8,5 8,6 8 114,6 8,3 8,5 8,2 85,4 8 8,3 8,1 100,5 8,2 8 8,1 114,8 8,1 8,2 8 116,5 8,1 8,1 7,9 112,9 8 8,1 7,9 102 7,9 8 8 106 7,9 7,9 8 105,3 8 7,9 7,9 118,8 8 8 8 106,1 7,9 8 7,7 109,3 8 7,9 7,2 117,2 7,7 8 7,5 92,5 7,2 7,7 7,3 104,2 7,5 7,2 7 112,5 7,3 7,5 7 122,4 7 7,3 7 113,3 7 7 7,2 100 7 7 7,3 110,7 7,2 7 7,1 112,8 7,3 7,2 6,8 109,8 7,1 7,3 6,4 117,3 6,8 7,1 6,1 109,1 6,4 6,8 6,5 115,9 6,1 6,4 7,7 96 6,5 6,1 7,9 99,8 7,7 6,5 7,5 116,8 7,9 7,7 6,9 115,7 7,5 7,9 6,6 99,4 6,9 7,5 6,9 94,3 6,6 6,9 7,7 91 6,9 6,6 8 93,2 7,7 6,9 8 103,1 8 7,7 7,7 94,1 8 8 7,3 91,8 7,7 8 7,4 102,7 7,3 7,7 8,1 82,6 7,4 7,3

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3.62399518778054 -0.0116550205248898X[t] + 1.33908375456985Y1[t] -0.61955467605507Y2[t] -0.0436568455591268M1[t] + 0.00299857923202550M2[t] + 0.143563257409505M3[t] + 0.122170287553879M4[t] -0.103225295505422M5[t] + 0.00667643070592493M6[t] -0.072453772067619M7[t] -0.138084807294505M8[t] + 0.101050909401817M9[t] + 0.405496234642471M10[t] -0.370533810105535M11[t] -0.00742558647969111t + 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) 3.62399518778054 0.961784 3.768 0.000507 0.000254 X -0.0116550205248898 0.004553 -2.5601 0.014151 0.007075 Y1 1.33908375456985 0.117256 11.4202 0 0 Y2 -0.61955467605507 0.119921 -5.1663 6e-06 3e-06 M1 -0.0436568455591268 0.123152 -0.3545 0.724742 0.362371 M2 0.00299857923202550 0.131882 0.0227 0.981968 0.490984 M3 0.143563257409505 0.147543 0.973 0.33611 0.168055 M4 0.122170287553879 0.14572 0.8384 0.406558 0.203279 M5 -0.103225295505422 0.140214 -0.7362 0.465703 0.232852 M6 0.00667643070592493 0.123197 0.0542 0.957038 0.478519 M7 -0.072453772067619 0.130933 -0.5534 0.582946 0.291473 M8 -0.138084807294505 0.134657 -1.0255 0.311019 0.15551 M9 0.101050909401817 0.127655 0.7916 0.433044 0.216522 M10 0.405496234642471 0.184213 2.2012 0.033267 0.016634 M11 -0.370533810105535 0.164088 -2.2581 0.029192 0.014596 t -0.00742558647969111 0.00262 -2.8342 0.007032 0.003516 Multiple Linear Regression - Regression Statistics Multiple R 0.972152036416714 R-squared 0.945079581909165 Adjusted R-squared 0.925465146876724 F-TEST (value) 48.1828602427782 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.181892943332620 Sum Squared Residuals 1.38957179903655 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.7 8.97898442203381 -0.278984422033815 2 8.2 8.28935613896266 -0.0893561389626577 3 8.3 8.15149270621581 0.148507293784190 4 8.5 8.64794500703924 -0.147945007039241 5 8.6 8.60583359412635 -0.00583359412635362 6 8.5 8.61574299348495 -0.115742993484950 7 8.2 8.35546790016651 -0.155467900166515 8 8.1 7.99742021616147 0.102579783838529 9 7.9 8.09693904944438 -0.196939049444379 10 8.6 8.57271318221824 0.0272868177817554 11 8.7 8.67803281063208 0.021967189367915 12 8.7 8.5373982772908 0.162601722709207 13 8.5 8.50944202747816 -0.00944202747816331 14 8.4 8.24705555535347 0.152944444646525 15 8.5 8.44595484021708 0.0540451597829233 16 8.7 8.65146169467639 0.0485383053236123 17 8.7 8.60468827355354 0.0953117264464555 18 8.6 8.41075917430582 0.189240825694183 19 8.5 8.36278931336397 0.137210686636033 20 8.3 8.11055359497693 0.189446405023075 21 8 8.08162384541811 -0.0816238454181131 22 8.2 8.44115599234592 -0.241155992345917 23 8.1 7.93539270492287 0.164607295077126 24 8.1 7.8740148243748 0.225985175625205 25 8 7.86507432504917 0.134925674950829 26 7.9 7.81235386179325 0.0876461382067493 27 7.9 8.00057976936086 -0.100579769360861 28 8 7.9870965985315 0.0129034014685088 29 8 7.8963423188169 0.103657681183094 30 7.9 7.77952021385704 0.120479786142958 31 8 7.70707480981292 0.292925190187076 32 7.7 7.79258596548919 -0.0925859654891908 33 7.2 7.46854083958273 -0.268540839582731 34 7.5 7.56976411084007 -0.0697641108400692 35 7.3 7.36144720386965 -0.0614472038696505 36 7 7.17413560340842 -0.174135603408418 37 7 6.72985427701325 0.270145722986750 38 7 7.06101120491773 -0.0610112049177301 39 7.2 7.34916206959655 -0.149162069596553 40 7.3 7.46345154455888 -0.163451544558885 41 7.1 7.21615227216359 -0.116152272163594 42 6.8 7.02382125495044 -0.223821254950443 43 6.4 6.5720386206006 -0.172038620600593 44 6.1 6.2447860681867 -0.144786068186696 45 6.5 6.24333880288515 0.256661197114851 46 7.7 7.49379335473588 0.206206645264122 47 7.9 8.0251272805754 -0.125127280575391 48 7.5 7.714451294926 -0.214451294925994 49 6.9 7.0166449484256 -0.116644948425601 50 6.6 6.69022323897289 -0.0902232389728868 51 6.9 6.8528106146097 0.0471893853903009 52 7.7 7.450045155194 0.249954844806005 53 8 8.0769835413396 -0.0769835413396017 54 8 7.97015636340175 0.0298436365982526 55 7.7 7.802629356056 -0.102629356056000 56 7.3 7.35465415518572 -0.0546541551857161 57 7.4 7.10955746266963 0.290442537330372 58 8.1 8.02257335985989 0.0774266401401095 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.110386684021665 0.22077336804333 0.889613315978335 20 0.0474235347152211 0.0948470694304422 0.95257646528478 21 0.0199295154048947 0.0398590308097894 0.980070484595105 22 0.33626285068218 0.67252570136436 0.66373714931782 23 0.256893983121180 0.513787966242359 0.743106016878820 24 0.243423559801891 0.486847119603783 0.756576440198109 25 0.162235360667181 0.324470721334361 0.83776463933282 26 0.143944282298391 0.287888564596782 0.856055717701609 27 0.212250147057955 0.424500294115910 0.787749852942045 28 0.152482212898269 0.304964425796539 0.84751778710173 29 0.117896606932236 0.235793213864473 0.882103393067764 30 0.127600706191216 0.255201412382432 0.872399293808784 31 0.297335119147114 0.594670238294229 0.702664880852885 32 0.444727132441478 0.889454264882957 0.555272867558521 33 0.537645440291964 0.924709119416072 0.462354559708036 34 0.445976725922443 0.891953451844886 0.554023274077557 35 0.461052492918866 0.922104985837733 0.538947507081134 36 0.424755292263422 0.849510584526844 0.575244707736578 37 0.786116215129241 0.427767569741517 0.213883784870759 38 0.698885028455685 0.602229943088629 0.301114971544315 39 0.54475091640926 0.91049816718148 0.45524908359074 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 1 0.0476190476190476 OK 10% type I error level 2 0.0952380952380952 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/10dcvn1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/10dcvn1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/17tfw1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/17tfw1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/2knel1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/2knel1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/343hx1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/343hx1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/4wnpn1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/4wnpn1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/5t66q1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/5t66q1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/6cpe21261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/6cpe21261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/74q7v1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/74q7v1261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/83k621261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/83k621261060986.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/9mqez1261060986.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261061058br7p8r69t2o2xuz/9mqez1261060986.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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