*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: Wed, 01 Dec 2010 15:35:58 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq.htm/, Retrieved Wed, 01 Dec 2010 16:34:23 +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/2010/Dec/01/t1291217653oawqb3k2mfg9mdq.htm/}, year = {2010}, } @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 = {2010}, 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 «

8587 9743 9084 9081 9700 9731 8587 9743 9084 9081 9563 9731 8587 9743 9084 9998 9563 9731 8587 9743 9437 9998 9563 9731 8587 10038 9437 9998 9563 9731 9918 10038 9437 9998 9563 9252 9918 10038 9437 9998 9737 9252 9918 10038 9437 9035 9737 9252 9918 10038 9133 9035 9737 9252 9918 9487 9133 9035 9737 9252 8700 9487 9133 9035 9737 9627 8700 9487 9133 9035 8947 9627 8700 9487 9133 9283 8947 9627 8700 9487 8829 9283 8947 9627 8700 9947 8829 9283 8947 9627 9628 9947 8829 9283 8947 9318 9628 9947 8829 9283 9605 9318 9628 9947 8829 8640 9605 9318 9628 9947 9214 8640 9605 9318 9628 9567 9214 8640 9605 9318 8547 9567 9214 8640 9605 9185 8547 9567 9214 8640 9470 9185 8547 9567 9214 9123 9470 9185 8547 9567 9278 9123 9470 9185 8547 10170 9278 9123 9470 9185 9434 10170 9278 9123 9470 9655 9434 10170 9278 9123 9429 9655 9434 10170 9278 8739 9429 9655 9434 10170 9552 8739 9429 9655 9434 9687 9552 8739 9429 9655 9019 9687 9552 8739 9429 9672 9019 9687 9552 8739 9206 9672 9019 9687 9552 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Births[t] = + 3513.15322119259 + 1.81853714940414e-07`Y-1`[t] + 0.375914107327586`Y-2`[t] + 0.238512132682298`Y-3`[t] + 0.0192032651388202`Y-4`[t] -455.243573208415M1[t] -340.490770887514M2[t] -1.15456343944968M3[t] -131.707497084488M4[t] -175.963277570170M5[t] + 308.85928331459M6[t] + 125.997921695407M7[t] -124.082645567172M8[t] -162.455132647671M9[t] -554.306360664536M10[t] -649.343791013937M11[t] + 5.95960793016406t + 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) 3513.15322119259 1973.915859 1.7798 0.080738 0.040369 `Y-1` 1.81853714940414e-07 0 0.4529 0.652454 0.326227 `Y-2` 0.375914107327586 0.13056 2.8792 0.005703 0.002851 `Y-3` 0.238512132682298 0.130548 1.827 0.073228 0.036614 `Y-4` 0.0192032651388202 0.113252 0.1696 0.865988 0.432994 M1 -455.243573208415 238.441023 -1.9093 0.061549 0.030775 M2 -340.490770887514 234.199356 -1.4539 0.151775 0.075888 M3 -1.15456343944968 221.399277 -0.0052 0.995858 0.497929 M4 -131.707497084488 239.726328 -0.5494 0.584991 0.292495 M5 -175.963277570170 227.913236 -0.7721 0.443443 0.221722 M6 308.85928331459 230.077465 1.3424 0.185078 0.092539 M7 125.997921695407 227.773926 0.5532 0.582429 0.291215 M8 -124.082645567172 255.842326 -0.485 0.629641 0.31482 M9 -162.455132647671 238.882886 -0.6801 0.499371 0.249685 M10 -554.306360664536 241.348727 -2.2967 0.025541 0.01277 M11 -649.343791013937 233.390116 -2.7822 0.007422 0.003711 t 5.95960793016406 3.114315 1.9136 0.060976 0.030488 Multiple Linear Regression - Regression Statistics Multiple R 0.783419237471422 R-squared 0.613745701640305 Adjusted R-squared 0.499299983607802 F-TEST (value) 5.36276683996165 F-TEST (DF numerator) 16 F-TEST (DF denominator) 54 p-value 1.45945058027674e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 360.796934403332 Sum Squared Residuals 7029419.10524147 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8587 8830.87512741333 -243.875127413334 2 9731 9188.14343944753 542.856560552469 3 9563 9256.11986002877 306.88013997123 4 9998 9298.50716889098 699.492831109024 5 9437 9447.71641069886 -10.7164106988626 6 10038 10083.9196112095 -45.9196112095373 7 9918 9796.6567817773 121.343218222697 8 9252 9653.00829302694 -401.008293026938 9 9737 9708.0583598819 28.9416401180976 10 9035 9054.72773894064 -19.7277389406363 11 9133 8986.8146587309 146.185341269097 12 9487 9481.11538192116 5.88461807883782 13 8700 8910.54912998658 -210.549129986584 14 9627 9174.22848798815 452.771512011846 15 8947 9309.9952844311 -362.995284431105 16 9283 9352.96311998655 -69.9631199865498 17 8829 9265.03319288336 -436.03319288336 18 9947 9737.7355957585 209.264404241509 19 9628 9457.25089694206 170.749103057944 20 9318 9531.56964043943 -213.569640439427 21 9605 9637.17838664272 -32.1783866427248 22 8640 9080.13732557604 -440.137325576037 23 9214 9018.88207376019 195.117926239815 24 9567 9373.92843340398 193.071566596016 25 8547 8915.76635898255 -368.766358982553 26 9185 9287.55107693014 -102.551076930144 27 9470 9344.63207588344 125.367924116561 28 9123 9223.36837972993 -100.368379729933 29 9278 9424.79107486925 -146.791074869247 30 10170 9865.35871760185 304.641282398153 31 9434 9669.43303328592 -235.433033285924 32 9655 9790.93317140797 -135.933171407967 33 9429 9697.57687790333 -268.576877903327 34 8739 9236.34661728674 -497.346617286739 35 9552 9100.88965931302 451.110340686979 36 9687 9447.15265165764 239.847348342362 37 9019 9134.5735707148 -115.573570714807 38 9672 9486.69437490174 185.305625098264 39 9206 9628.69107780558 -422.691077805584 40 9069 9592.83591559376 -523.835915593758 41 9788 9522.28438563844 265.715614361565 42 10312 9862.959530808 449.040469192 43 10105 9914.7152318467 190.284768153303 44 9863 9862.99892077404 0.00107922596195564 45 9863 10008.3596619558 -145.359661955810 46 9656 9682.1354607746 -26.1354607745962 47 9295 9448.00957531412 -153.009575314117 48 9946 9955.25822341259 -9.25822341259005 49 9701 9342.808297834 358.191702165993 50 9049 9612.84348717567 -563.843487175672 51 10190 10022.4446187094 167.555381290614 52 9706 9596.77804794262 109.221952057380 53 9765 9833.82940310084 -68.8294031008421 54 9893 10423.2417314850 -530.241731485028 55 9994 10146.1658857374 -152.165885737405 56 10433 9964.98309367881 468.016906321184 57 10073 9984.82120987824 88.1787901217598 58 10112 9798.68415172883 313.315848271169 59 9266 9684.30338845494 -418.303388454939 60 9820 10249.5453096046 -429.545309604625 61 10097 9516.42751506872 580.572484931285 62 9115 9629.53913355676 -514.539133556763 63 10411 10225.1170831417 185.882916858284 64 9678 9792.54736785616 -114.547367856164 65 10408 10011.3455328093 396.654467190746 66 10153 10539.7848131371 -386.784813137097 67 10368 10462.7781704106 -94.7781704106148 68 10581 10298.5068806728 282.493119327186 69 10597 10268.005503738 328.994496262004 70 10680 10009.9687056932 670.03129430684 71 9738 9959.10064442684 -221.100644426835 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.516404362281381 0.967191275437238 0.483595637718619 21 0.348518062193177 0.697036124386355 0.651481937806823 22 0.246522463015029 0.493044926030057 0.753477536984971 23 0.254161905457367 0.508323810914735 0.745838094542633 24 0.195653038840779 0.391306077681559 0.80434696115922 25 0.146017441332907 0.292034882665814 0.853982558667093 26 0.115424067643550 0.230848135287099 0.88457593235645 27 0.190291640100442 0.380583280200884 0.809708359899558 28 0.157997288628736 0.315994577257473 0.842002711371264 29 0.122833455836040 0.245666911672079 0.87716654416396 30 0.186985931174429 0.373971862348859 0.81301406882557 31 0.142060002264449 0.284120004528899 0.85793999773555 32 0.133697084364649 0.267394168729298 0.866302915635351 33 0.0918751229125984 0.183750245825197 0.908124877087402 34 0.129175992507754 0.258351985015509 0.870824007492246 35 0.183561831610821 0.367123663221642 0.81643816838918 36 0.186801589744421 0.373603179488841 0.81319841025558 37 0.245531502637144 0.491063005274288 0.754468497362856 38 0.559818886158455 0.880362227683091 0.440181113841545 39 0.892938210821239 0.214123578357523 0.107061789178761 40 0.899311497827786 0.201377004344429 0.100688502172214 41 0.946330006881184 0.107339986237631 0.0536699931188156 42 0.941887914443547 0.116224171112906 0.0581120855564532 43 0.924794791108786 0.150410417782427 0.0752052088912137 44 0.884339728480172 0.231320543039655 0.115660271519828 45 0.819437294003603 0.361125411992795 0.180562705996397 46 0.802816993643211 0.394366012713577 0.197183006356789 47 0.768804411944524 0.462391176110952 0.231195588055476 48 0.885573040460596 0.228853919078808 0.114426959539404 49 0.840817597072849 0.318364805854303 0.159182402927151 50 0.804528204537375 0.390943590925251 0.195471795462625 51 0.738377750905351 0.523244498189298 0.261622249094649 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://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/10dkwz1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/10dkwz1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/1yagq1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/1yagq1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/2yagq1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/2yagq1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/3yagq1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/3yagq1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/49jfb1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/49jfb1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/59jfb1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/59jfb1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/69jfb1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/69jfb1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/7ksfe1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/7ksfe1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/8ksfe1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/8ksfe1291217747.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/9dkwz1291217747.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291217653oawqb3k2mfg9mdq/9dkwz1291217747.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