Y(t-4)

*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 10:12:35 -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/t1261069984z419d782am5zexr.htm/, Retrieved Thu, 17 Dec 2009 18:13:16 +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/t1261069984z419d782am5zexr.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 «

0.7905 0.313 0.7744 0.779 0.7775 0.7461 0.7719 0.364 0.7905 0.7744 0.779 0.7775 0.7811 0.363 0.7719 0.7905 0.7744 0.779 0.7557 -0.155 0.7811 0.7719 0.7905 0.7744 0.7637 0.052 0.7557 0.7811 0.7719 0.7905 0.7595 0.568 0.7637 0.7557 0.7811 0.7719 0.7471 0.668 0.7595 0.7637 0.7557 0.7811 0.7615 1.378 0.7471 0.7595 0.7637 0.7557 0.7487 0.252 0.7615 0.7471 0.7595 0.7637 0.7389 -0.402 0.7487 0.7615 0.7471 0.7595 0.7337 -0.05 0.7389 0.7487 0.7615 0.7471 0.751 0.555 0.7337 0.7389 0.7487 0.7615 0.7382 0.05 0.751 0.7337 0.7389 0.7487 0.7159 0.15 0.7382 0.751 0.7337 0.7389 0.7542 0.45 0.7159 0.7382 0.751 0.7337 0.7636 0.299 0.7542 0.7159 0.7382 0.751 0.7433 0.199 0.7636 0.7542 0.7159 0.7382 0.7658 0.496 0.7433 0.7636 0.7542 0.7159 0.7627 0.444 0.7658 0.7433 0.7636 0.7542 0.748 -0.393 0.7627 0.7658 0.7433 0.7636 0.7692 -0.444 0.748 0.7627 0.7658 0.7433 0.785 0.198 0.7692 0.748 0.7627 0.7658 0.7913 0.494 0.785 0.7692 0.748 0.7627 0.772 0.133 0.7913 0.785 0.7692 0.748 0.788 0.388 0.772 0.7913 0.785 0 etc...

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 USDOLLAR[t] = + 0.139527755525393 + 0.0131669901810773Amerikaanse_inflatie[t] + 1.04991533179280`Y[t-1]`[t] -0.239723305758452`Y[t-2]`[t] + 0.288489458849696`Y[t-3]`[t] -0.287960825487985`Y[t-4]`[t] + 0.000313710754245402t + 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) 0.139527755525393 0.047251 2.9529 0.004862 0.002431 Amerikaanse_inflatie 0.0131669901810773 0.012482 1.0549 0.296775 0.148387 `Y[t-1]` 1.04991533179280 0.157815 6.6528 0 0 `Y[t-2]` -0.239723305758452 0.208727 -1.1485 0.256454 0.128227 `Y[t-3]` 0.288489458849696 0.208311 1.3849 0.172487 0.086244 `Y[t-4]` -0.287960825487985 0.136952 -2.1026 0.040767 0.020384 t 0.000313710754245402 0.000282 1.1142 0.270734 0.135367 Multiple Linear Regression - Regression Statistics Multiple R 0.930948624007176 R-squared 0.866665340540855 Adjusted R-squared 0.849998508108461 F-TEST (value) 51.9994032493198 F-TEST (DF numerator) 6 F-TEST (DF denominator) 48 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0309802869114829 Sum Squared Residuals 0.0460693525016543 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.7905 0.779725694319876 0.0107743056801237 2 0.7719 0.79010804988966 -0.0182080498896601 3 0.7811 0.765261630510727 0.0158383694892728 4 0.7557 0.7788422149755 -0.0231422149755000 5 0.7637 0.743006115631753 0.0206938843682472 6 0.7595 0.772612462315535 -0.0131124623155349 7 0.7471 0.757938569399019 -0.0108385693990188 8 0.7615 0.765210851589976 -0.00371085158997645 9 0.7487 0.765274538838477 -0.0165745388384771 10 0.7389 0.737718272341742 0.00118172765825810 11 0.7337 0.743171014145352 -0.00947101414535184 12 0.751 0.740501181669956 0.0104988183300437 13 0.7382 0.754434360682236 -0.0162343606822362 14 0.7159 0.739800511921784 -0.0239005119217841 15 0.7542 0.730207930075719 0.0239920699242812 16 0.7636 0.765416624884481 -0.00181662488448086 17 0.7433 0.76235402176282 -0.0190540217628200 18 0.7658 0.760482320973648 0.00531767902635243 19 0.7627 0.780283727607709 -0.0175837276077088 20 0.748 0.752364987898034 -0.00436498789803381 21 0.7692 0.749653186605066 0.0195468133949344 22 0.785 0.776828806788305 0.00817119321169461 23 0.7913 0.789198358310319 0.00210164168968107 24 0.772 0.797934624630793 -0.0259346246307934 25 0.788 0.778285659100814 0.00971434089918577 26 0.807 0.798556408570309 0.00844359142969119 27 0.8268 0.804888538002807 0.0219114619971932 28 0.8244 0.83280750089713 -0.00840750089713013 29 0.8487 0.824042753914452 0.0246572460855478 30 0.8572 0.850553908864224 0.00664609113577566 31 0.8214 0.846677269230563 -0.0252772692305634 32 0.8827 0.819373368628111 0.063326631371889 33 0.9216 0.887372678438494 0.0342273215615064 34 0.8865 0.909155166274381 -0.0226551662743812 35 0.8816 0.882002285590992 -0.000402285590992284 36 0.8884 0.88032780272453 0.0080721972754696 37 0.9466 0.865126197681672 0.0814738023183285 38 0.918 0.936400090811753 -0.0184000908117533 39 0.9337 0.893118156275547 0.0405818437244526 40 0.9559 0.935975017915483 0.019924982084517 41 0.9626 0.936603383258112 0.0259966167418883 42 0.9434 0.948800736394756 -0.00540073639475557 43 0.8639 0.91946350094194 -0.0555635009419402 44 0.7996 0.837241798748545 -0.037641798748545 45 0.668 0.770246174828995 -0.102246174828995 46 0.6572 0.619141395745574 0.0380586042544255 47 0.6928 0.655672574676964 0.0371274253230362 48 0.6438 0.690552129762691 -0.0467521297626909 49 0.6454 0.667206335903917 -0.0218063359039167 50 0.6873 0.687255881094275 4.41189057245569e-05 51 0.7265 0.70839647089251 0.0181035291074904 52 0.7912 0.755947824566191 0.0352521754338087 53 0.8114 0.834887249393154 -0.0234872493931535 54 0.8281 0.830438308402549 -0.00233830840254906 55 0.8393 0.856653374700077 -0.0173533747000768 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0148720786127371 0.0297441572254743 0.985127921387263 11 0.0034365448879983 0.0068730897759966 0.996563455112002 12 0.00274645967769676 0.00549291935539352 0.997253540322303 13 0.000929772450455636 0.00185954490091127 0.999070227549544 14 0.000389666045431158 0.000779332090862316 0.999610333954569 15 0.000686672427090172 0.00137334485418034 0.99931332757291 16 0.00563973997493733 0.0112794799498747 0.994360260025063 17 0.00291014923524618 0.00582029847049236 0.997089850764754 18 0.00130310843174608 0.00260621686349216 0.998696891568254 19 0.000526728749487952 0.00105345749897590 0.999473271250512 20 0.000206896638450005 0.000413793276900011 0.99979310336155 21 0.000103697666894941 0.000207395333789882 0.999896302333105 22 5.70047851863195e-05 0.000114009570372639 0.999942995214814 23 2.18351707291133e-05 4.36703414582265e-05 0.99997816482927 24 3.44205933863158e-05 6.88411867726315e-05 0.999965579406614 25 1.23135334506723e-05 2.46270669013445e-05 0.99998768646655 26 4.4286370856544e-06 8.8572741713088e-06 0.999995571362914 27 4.26316253765911e-06 8.52632507531822e-06 0.999995736837462 28 1.88595790230237e-06 3.77191580460475e-06 0.999998114042098 29 7.4244804893096e-07 1.48489609786192e-06 0.999999257551951 30 2.67996315944247e-07 5.35992631888494e-07 0.999999732003684 31 4.92154902185338e-06 9.84309804370676e-06 0.999995078450978 32 4.80042281665100e-06 9.60084563330199e-06 0.999995199577183 33 4.72741963992569e-06 9.45483927985138e-06 0.99999527258036 34 4.49525095946404e-06 8.99050191892808e-06 0.99999550474904 35 6.40247680715861e-06 1.28049536143172e-05 0.999993597523193 36 1.46747030998700e-05 2.93494061997401e-05 0.9999853252969 37 6.32555535813644e-05 0.000126511107162729 0.999936744446419 38 9.13370277169951e-05 0.000182674055433990 0.999908662972283 39 4.17358140832399e-05 8.34716281664798e-05 0.999958264185917 40 1.77411773282186e-05 3.54823546564373e-05 0.999982258822672 41 7.57318102174488e-05 0.000151463620434898 0.999924268189783 42 0.000796152187602797 0.00159230437520559 0.999203847812397 43 0.0119961203426487 0.0239922406852974 0.988003879657351 44 0.557270681108281 0.885458637783439 0.442729318891719 45 0.603668212019367 0.792663575961266 0.396331787980633 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.861111111111111 NOK 5% type I error level 34 0.944444444444444 NOK 10% type I error level 34 0.944444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/10nz2j1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/10nz2j1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/1xllz1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/1xllz1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/21k5a1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/21k5a1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/3m2gi1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/3m2gi1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/40e0b1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/40e0b1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/5d26o1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/5d26o1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/6z67t1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/6z67t1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/7wuqy1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/7wuqy1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/8w82c1261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/8w82c1261069950.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/9rae61261069950.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261069984z419d782am5zexr/9rae61261069950.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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