regression

*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: Tue, 21 Dec 2010 19:59:58 +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/21/t12929628934xr2wl9zhzgu3k3.htm/, Retrieved Tue, 21 Dec 2010 21:21:45 +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/21/t12929628934xr2wl9zhzgu3k3.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 «

-999,00 38,60 6654,00 5712,00 645,00 3,30 3,00 5,00 3,00 6,30 4,50 1,00 6600,00 42,00 8,30 3,00 1,00 3,00 -999,00 14,00 3,39 44,50 60,00 12,50 1,00 1,00 1,00 -999,00 -999,00 0,92 5,70 25,00 16,50 5,00 2,00 3,00 2,10 69,00 2547,00 4603,00 624,00 3,90 3,00 5,00 4,00 9,10 27,00 10,55 179,50 180,00 9,80 4,00 4,00 4,00 15,80 19,00 0,02 0,30 35,00 19,70 1,00 1,00 1,00 5,20 30,40 160,00 169,00 392,00 6,20 4,00 5,00 4,00 10,90 28,00 3,30 25,60 63,00 14,50 1,00 2,00 1,00 8,30 50,00 52,16 440,00 230,00 9,70 1,00 1,00 1,00 11,00 7,00 0,43 6,40 112,00 12,50 5,00 4,00 4,00 3,20 30,00 465,00 423,00 281,00 3,90 5,00 5,00 5,00 7,60 -999,00 0,55 2,40 -999,00 10,30 2,00 1,00 2,00 -999,00 40,00 187,10 419,00 365,00 3,10 5,00 5,00 5,00 6,30 3,50 0,08 1,20 42,00 8,40 1,00 1,00 1,00 8,60 50,00 3,00 25,00 28,00 8,60 2,00 2,00 2,00 6,60 6,00 0,79 3500,00 42,00 10,70 2,00 2,00 2,00 9,50 10,40 0,20 5,00 120,00 10,70 2,00 2,00 2,00 4,80 34,00 1,41 17,50 -999,00 6,10 1,00 2,00 1,00 12,00 7,00 60,00 81,00 -999,00 18,10 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation SWS[t] = -192.398252454159 + 0.196980459486057L[t] -0.0894302120127955Wb[t] + 0.0270719269611047Wbr[t] -0.162991037668553Tg[t] + 0.78500630910943Ts[t] -25.9723351193777P[t] -83.0275670875686S[t] + 121.616239149268D[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) -192.398252454159 111.812203 -1.7207 0.091134 0.045567 L 0.196980459486057 0.200838 0.9808 0.331151 0.165575 Wb -0.0894302120127955 0.076477 -1.1694 0.247484 0.123742 Wbr 0.0270719269611047 0.05041 0.537 0.593488 0.296744 Tg -0.162991037668553 0.176564 -0.9231 0.360124 0.180062 Ts 0.78500630910943 0.198289 3.9589 0.000226 0.000113 P -25.9723351193777 91.790596 -0.283 0.778316 0.389158 S -83.0275670875686 60.60128 -1.3701 0.176443 0.088222 D 121.616239149268 120.378818 1.0103 0.316952 0.158476 Multiple Linear Regression - Regression Statistics Multiple R 0.58753404519096 R-squared 0.345196254258452 Adjusted R-squared 0.246357953014445 F-TEST (value) 3.49253528150235 F-TEST (DF numerator) 8 F-TEST (DF denominator) 53 p-value 0.00262481877877097 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 368.755482812332 Sum Squared Residuals 7206972.12352026 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -855.973412473636 -143.026587526364 2 6.3 190.647521130438 -184.347521130438 3 -999 -176.089540144232 -822.910459855768 4 -999 -311.299961457735 -687.700038542265 5 2.1 -387.208038042801 389.308038042801 6 9.1 -154.343835077033 163.443835077033 7 15.8 -166.273015836697 182.073015836697 8 5.2 -287.731391719281 292.931391719281 9 10.9 -255.781951994266 266.681951994266 10 8.3 -192.559301998642 200.859301998642 11 11 -174.833988601473 185.833988601473 12 3.2 -196.279734418709 199.479734418709 13 7.6 -109.992092885708 117.592092885708 14 -999 -183.884813824783 -815.115186175217 15 6.3 -179.318722593804 185.618722593804 16 8.6 -154.720742853602 163.320742853602 17 6.6 -69.7486573908317 76.3486573908317 18 9.5 -176.158865211214 185.658865211214 19 4.8 -88.1478997375475 92.9478997375475 20 12 -4.53937810658883 16.5393781065888 21 -999 -1002.11850456590 3.11850456590095 22 3.3 -145.876976216908 149.176976216908 23 11 -100.629548259331 111.629548259331 24 -999 -460.297802040344 -538.702197959656 25 4.7 -382.224225167548 386.924225167548 26 -999 -176.668089753433 -822.331910246567 27 10.4 -32.2972708578043 42.6972708578043 28 7.4 -87.9710718735596 95.3710718735596 29 2.1 -201.604965265552 203.704965265552 30 -999 -187.883317516210 -811.11668248379 31 -999 -986.99139171745 -12.0086082825502 32 7.7 2.30418943622661 5.39581056377340 33 17.9 -167.576437136702 185.476437136702 34 6.1 -167.132155703995 173.232155703995 35 8.2 -399.035909755841 407.235909755841 36 8.4 -183.721560689520 192.121560689520 37 11.9 -6.56181136999343 18.4618113699934 38 10.8 -8.9096698006222 19.7096698006222 39 13.8 -191.477596609651 205.277596609651 40 14.3 -210.394321317143 224.694321317143 41 -999 -989.697497065422 -9.30250293457835 42 15.2 -173.898762708289 189.098762708289 43 10 -154.886837557217 164.886837557217 44 11.9 -73.4297836902989 85.329783690299 45 6.5 -161.062002467572 167.562002467572 46 7.5 -124.125593856811 131.625593856811 47 -999 -164.245238856468 -834.754761143532 48 10.6 19.3973683985945 -8.79736839859451 49 7.4 -177.648738118461 185.048738118461 50 8.4 -259.43743245783 267.83743245783 51 5.7 -197.012745145804 202.712745145804 52 4.9 -102.527179486006 107.427179486006 53 -999 -147.043325994456 -851.956674005544 54 3.2 -147.231370814683 150.431370814683 55 -999 -170.484853933813 -828.515146066187 56 8.1 61.5144027358274 -53.4144027358274 57 11 -82.6005779439305 93.6005779439305 58 4.9 -15.2218031928355 20.1218031928355 59 13.2 -187.720609884699 200.920609884699 60 9.7 -79.1249003207975 88.8249003207975 61 12.8 -192.423486610290 205.223486610290 62 -999 -1019.48277160931 20.4827716093122 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.734610874130411 0.530778251739178 0.265389125869589 13 0.758643448910156 0.482713102179688 0.241356551089844 14 0.932300653828956 0.135398692342088 0.0676993461710439 15 0.925938415783465 0.148123168433070 0.0740615842165349 16 0.88872831970448 0.222543360591041 0.111271680295521 17 0.847881678422831 0.304236643154337 0.152118321577169 18 0.80122797870609 0.39754404258782 0.19877202129391 19 0.766983366732374 0.466033266535252 0.233016633267626 20 0.714158446164638 0.571683107670724 0.285841553835362 21 0.651022007094285 0.69795598581143 0.348977992905715 22 0.568292594188462 0.863414811623075 0.431707405811538 23 0.549466471590243 0.901067056819514 0.450533528409757 24 0.61993451847082 0.760130963058361 0.380065481529181 25 0.643039102419731 0.713921795160537 0.356960897580269 26 0.859815244531392 0.280369510937215 0.140184755468608 27 0.82073360469525 0.358532790609499 0.179266395304750 28 0.760046922277336 0.479906155445327 0.239953077722664 29 0.70042461323565 0.5991507735287 0.29957538676435 30 0.891055640263962 0.217888719472076 0.108944359736038 31 0.869959154784694 0.260081690430613 0.130040845215306 32 0.818564129893355 0.362871740213289 0.181435870106645 33 0.78047470638473 0.439050587230539 0.219525293615270 34 0.738977683372951 0.522044633254097 0.261022316627049 35 0.747797109089725 0.50440578182055 0.252202890910275 36 0.681966637019698 0.636066725960605 0.318033362980302 37 0.610431792321398 0.779136415357204 0.389568207678602 38 0.552754212299862 0.894491575400276 0.447245787700138 39 0.477766923712732 0.955533847425464 0.522233076287268 40 0.403473740462824 0.806947480925648 0.596526259537176 41 0.314126053134734 0.628252106269467 0.685873946865266 42 0.274696378076934 0.549392756153869 0.725303621923066 43 0.211911088171335 0.42382217634267 0.788088911828665 44 0.150407042279301 0.300814084558602 0.8495929577207 45 0.0977526718361644 0.195505343672329 0.902247328163836 46 0.329078884837666 0.658157769675332 0.670921115162334 47 0.525234551774890 0.94953089645022 0.47476544822511 48 0.403165645989074 0.806331291978149 0.596834354010926 49 0.274077630455569 0.548155260911137 0.725922369544431 50 0.166560390260245 0.333120780520489 0.833439609739755 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/21/t12929628934xr2wl9zhzgu3k3/10r00m1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/10r00m1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/12z3a1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/12z3a1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/22z3a1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/22z3a1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/3dq2v1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/3dq2v1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/4dq2v1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/4dq2v1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/5dq2v1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/5dq2v1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/6o0jy1292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/6o0jy1292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/7yr111292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/7yr111292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/8yr111292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/8yr111292961589.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/9yr111292961589.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3/9yr111292961589.ps (open in new window)

Parameters (Session):

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