Ws7.1 werkloosheid - inflatie

*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, 19 Nov 2009 12:11:02 -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/Nov/19/t1258658082xej0vnl6tz3l315.htm/, Retrieved Thu, 19 Nov 2009 20:14:54 +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/Nov/19/t1258658082xej0vnl6tz3l315.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:

Ws7.1 werkloosheid - inflatie

Dataseries X:

» Textbox « » Textfile « » CSV «

8.2 1.4 8.0 1.2 7.5 1.0 6.8 1.7 6.5 2.4 6.6 2.0 7.6 2.1 8.0 2.0 8.1 1.8 7.7 2.7 7.5 2.3 7.6 1.9 7.8 2.0 7.8 2.3 7.8 2.8 7.5 2.4 7.5 2.3 7.1 2.7 7.5 2.7 7.5 2.9 7.6 3.0 7.7 2.2 7.7 2.3 7.9 2.8 8.1 2.8 8.2 2.8 8.2 2.2 8.2 2.6 7.9 2.8 7.3 2.5 6.9 2.4 6.6 2.3 6.7 1.9 6.9 1.7 7.0 2.0 7.1 2.1 7.2 1.7 7.1 1.8 6.9 1.8 7.0 1.8 6.8 1.3 6.4 1.3 6.7 1.3 6.6 1.2 6.4 1.4 6.3 2.2 6.2 2.9 6.5 3.1 6.8 3.5 6.8 3.6 6.4 4.4 6.1 4.1 5.8 5.1 6.1 5.8 7.2 5.9 7.3 5.4 6.9 5.5 6.1 4.8 5.8 3.2 6.2 2.7 7.1 2.1 7.7 1.9 7.9 0.6 7.7 0.7

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 7.63677077903145 -0.186016840579967X[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) 7.63677077903145 0.186913 40.8574 0 0 X -0.186016840579967 0.06694 -2.7789 0.00721 0.003605 Multiple Linear Regression - Regression Statistics Multiple R 0.332800503518977 R-squared 0.110756175142485 Adjusted R-squared 0.0964135328060733 F-TEST (value) 7.72215973491224 F-TEST (DF numerator) 1 F-TEST (DF denominator) 62 p-value 0.00720990438056168 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.629449386680064 Sum Squared Residuals 24.5648048842983 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.37634720221949 0.82365279778051 2 8 7.41355057033548 0.586449429664515 3 7.5 7.45075393845148 0.0492460615485204 4 6.8 7.3205421500455 -0.520542150045503 5 6.5 7.19033036163953 -0.690330361639527 6 6.6 7.26473709787151 -0.664737097871514 7 7.6 7.24613541381352 0.353864586186483 8 8 7.26473709787151 0.735262902128487 9 8.1 7.3019404659875 0.798059534012493 10 7.7 7.13452530946554 0.565474690534463 11 7.5 7.20893204569752 0.291067954302477 12 7.6 7.28333878192951 0.316661218070490 13 7.8 7.26473709787151 0.535262902128486 14 7.8 7.20893204569752 0.591067954302476 15 7.8 7.11592362540754 0.68407637459246 16 7.5 7.19033036163953 0.309669638360473 17 7.5 7.20893204569752 0.291067954302477 18 7.1 7.13452530946554 -0.0345253094655372 19 7.5 7.13452530946554 0.365474690534463 20 7.5 7.09732194134954 0.402678058650456 21 7.6 7.07872025729155 0.521279742708453 22 7.7 7.22753372975552 0.47246627024448 23 7.7 7.20893204569752 0.491067954302477 24 7.9 7.11592362540754 0.78407637459246 25 8.1 7.11592362540754 0.98407637459246 26 8.2 7.11592362540754 1.08407637459246 27 8.2 7.22753372975552 0.97246627024448 28 8.2 7.15312699352353 1.04687300647647 29 7.9 7.11592362540754 0.78407637459246 30 7.3 7.17172867758153 0.128271322418470 31 6.9 7.19033036163953 -0.290330361639526 32 6.6 7.20893204569752 -0.608932045697524 33 6.7 7.28333878192951 -0.58333878192951 34 6.9 7.3205421500455 -0.420542150045503 35 7 7.26473709787151 -0.264737097871513 36 7.1 7.24613541381352 -0.146135413813517 37 7.2 7.3205421500455 -0.120542150045503 38 7.1 7.3019404659875 -0.201940465987507 39 6.9 7.3019404659875 -0.401940465987506 40 7 7.3019404659875 -0.301940465987507 41 6.8 7.39494888627749 -0.59494888627749 42 6.4 7.39494888627749 -0.99494888627749 43 6.7 7.39494888627749 -0.69494888627749 44 6.6 7.41355057033549 -0.813550570335487 45 6.4 7.3763472022195 -0.976347202219493 46 6.3 7.22753372975552 -0.92753372975552 47 6.2 7.09732194134954 -0.897321941349543 48 6.5 7.06011857323355 -0.56011857323355 49 6.8 6.98571183700156 -0.185711837001564 50 6.8 6.96711015294357 -0.167110152943567 51 6.4 6.81829668047959 -0.418296680479594 52 6.1 6.87410173265358 -0.774101732653584 53 5.8 6.68808489207362 -0.888084892073618 54 6.1 6.55787310366764 -0.457873103667642 55 7.2 6.53927141960964 0.660728580390356 56 7.3 6.63227983989963 0.667720160100372 57 6.9 6.61367815584163 0.286321844158369 58 6.1 6.74388994424761 -0.643889944247608 59 5.8 7.04151688917555 -1.24151688917555 60 6.2 7.13452530946554 -0.934525309465537 61 7.1 7.24613541381352 -0.146135413813517 62 7.7 7.28333878192951 0.41666121807049 63 7.9 7.52516067468347 0.374839325316534 64 7.7 7.50655899062547 0.193441009374531 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.4689381981809 0.9378763963618 0.5310618018191 6 0.339243787537495 0.67848757507499 0.660756212462505 7 0.420654309854167 0.841308619708334 0.579345690145833 8 0.542221092862914 0.915557814274171 0.457778907137086 9 0.572877606084968 0.854244787830064 0.427122393915032 10 0.58584900460216 0.82830199079568 0.41415099539784 11 0.488956807361089 0.977913614722178 0.511043192638911 12 0.393937457881563 0.787874915763126 0.606062542118437 13 0.335256114131071 0.670512228262141 0.664743885868929 14 0.295101929023741 0.590203858047481 0.70489807097626 15 0.272330625197159 0.544661250394318 0.727669374802841 16 0.206810357989231 0.413620715978463 0.793189642010769 17 0.152748455442848 0.305496910885696 0.847251544557152 18 0.116697685865686 0.233395371731371 0.883302314134314 19 0.084330458084889 0.168660916169778 0.915669541915111 20 0.0606621253991418 0.121324250798284 0.939337874600858 21 0.0463490944084701 0.0926981888169401 0.95365090559153 22 0.0344640428272853 0.0689280856545707 0.965535957172715 23 0.0259398523606496 0.0518797047212992 0.97406014763935 24 0.0282085706568464 0.0564171413136928 0.971791429343154 25 0.0451292648498360 0.0902585296996719 0.954870735150164 26 0.08727143623386 0.17454287246772 0.91272856376614 27 0.153425396678556 0.306850793357111 0.846574603321444 28 0.286992247133877 0.573984494267753 0.713007752866123 29 0.376678344205801 0.753356688411603 0.623321655794199 30 0.364996058087260 0.729992116174519 0.63500394191274 31 0.388459882494148 0.776919764988296 0.611540117505852 32 0.479865100939269 0.959730201878539 0.520134899060731 33 0.515132674316887 0.969734651366226 0.484867325683113 34 0.490331726982327 0.980663453964653 0.509668273017673 35 0.452662901472848 0.905325802945695 0.547337098527152 36 0.410574608869014 0.821149217738028 0.589425391130986 37 0.362885137362518 0.725770274725037 0.637114862637482 38 0.317293570151581 0.634587140303162 0.68270642984842 39 0.280288033838616 0.560576067677233 0.719711966161384 40 0.238192732094316 0.476385464188632 0.761807267905684 41 0.200072030149485 0.40014406029897 0.799927969850515 42 0.218891298001656 0.437782596003313 0.781108701998344 43 0.183300215836041 0.366600431672083 0.816699784163958 44 0.160299612919949 0.320599225839898 0.839700387080051 45 0.175872143349204 0.351744286698408 0.824127856650796 46 0.240496190851651 0.480992381703302 0.759503809148349 47 0.361441070263678 0.722882140527356 0.638558929736322 48 0.371968436504433 0.743936873008865 0.628031563495568 49 0.323584552584648 0.647169105169297 0.676415447415352 50 0.269196777784206 0.538393555568413 0.730803222215794 51 0.242939823087395 0.48587964617479 0.757060176912605 52 0.260651059802287 0.521302119604573 0.739348940197713 53 0.30974663840482 0.61949327680964 0.69025336159518 54 0.261926803721463 0.523853607442927 0.738073196278537 55 0.262883977036113 0.525767954072226 0.737116022963887 56 0.36608077656958 0.73216155313916 0.63391922343042 57 0.65714779804981 0.685704403900382 0.342852201950191 58 0.780945212101863 0.438109575796275 0.219054787898137 59 0.717944145647239 0.564111708705522 0.282055854352761 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 5 0.090909090909091 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/10iugg1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/10iugg1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/1ojds1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/1ojds1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/2aey11258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/2aey11258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/3kji61258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/3kji61258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/4ph2o1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/4ph2o1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/5xc4a1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/5xc4a1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/63o2d1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/63o2d1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/7bwcj1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/7bwcj1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/8x1dv1258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/8x1dv1258657857.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/9sm821258657857.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658082xej0vnl6tz3l315/9sm821258657857.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