Sleep article - PS Multiple 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, 14 Dec 2010 09:54:19 +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/14/t1292320372o79asr8yx4l7uu0.htm/, Retrieved Tue, 14 Dec 2010 10:53:02 +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/14/t1292320372o79asr8yx4l7uu0.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 «

6654000 5712000 -999.0 38.6 645.0 3 5 3 1000 6600 2.0 4.5 42.0 3 1 3 3385 44500 -999.0 14.0 60.0 1 1 1 0.920 5700 -999.0 -999.0 25.0 5 2 3 2547000 4603000 1.8 69.0 624.0 3 5 4 10550 179500 .7 27.0 180.0 4 4 4 0.023 0.300 3.9 19.0 35.0 1 1 1 160000 169000 1.0 30.4 392.0 4 5 4 3300 25600 3.6 28.0 63.0 1 2 1 52160 440000 1.4 50.0 230.0 1 1 1 0.425 6400 1.5 7.0 112.0 5 4 4 465000 423000 .7 30.0 281.0 5 5 5 0.550 2400 2.7 -999.0 -999.0 2 1 2 187100 419000 -999.0 40.0 365.0 5 5 5 0.075 1200 2.1 3.5 42.0 1 1 1 3000 25000 .0 50.0 28.0 2 2 2 0.785 3500 4.1 6.0 42.0 2 2 2 0.200 5000 1.2 10.4 120.0 2 2 2 1410 17500 1.3 34.0 -999.0 1 2 1 60000 81000 6.1 7.0 -999.0 1 1 1 529000 680000 .3 28.0 400.0 5 5 5 27660 115000 .5 20.0 148.0 5 5 5 0.120 1000 3.4 3.9 16.0 3 1 2 207000 406000 -999.0 39.3 252.0 1 4 1 85000 325000 1.5 41.0 310.0 1 3 1 36330 119500 -999.0 16.2 63.0 1 1 1 0.101 4000 3.4 9.0 28.0 5 1 3 1040 5500 .8 7.6 68.0 5 3 4 521000 655000 .8 46.0 336.0 5 5 5 100000 157000 -999.0 22.4 100.0 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation PS[t] = -224.797977596438 -0.000235682027927057Wbo[t] + 0.000199220263264062Wbr[t] + 0.178340090407748Lifeyears[t] -0.167908843596768Gestation[t] -44.4794213593373Predation[t] -121.269232984052Sleep_exposure[t] + 177.683241346138overall_danger[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) -224.797977596438 116.527909 -1.9291 0.058976 0.029488 Wbo -0.000235682027927057 0.00016 -1.4697 0.147459 0.07373 Wbr 0.000199220263264062 0.000161 1.2363 0.221702 0.110851 Lifeyears 0.178340090407748 0.211316 0.844 0.40242 0.20121 Gestation -0.167908843596768 0.189422 -0.8864 0.379318 0.189659 Predation -44.4794213593373 96.446191 -0.4612 0.646519 0.323259 Sleep_exposure -121.269232984052 60.885986 -1.9917 0.051467 0.025733 overall_danger 177.683241346138 123.277385 1.4413 0.155266 0.077633 Multiple Linear Regression - Regression Statistics Multiple R 0.398342798198946 R-squared 0.158676984876966 Adjusted R-squared 0.049616594027684 F-TEST (value) 1.45494604999401 F-TEST (DF numerator) 7 F-TEST (DF denominator) 54 p-value 0.203183241774995 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 388.676692498816 Sum Squared Residuals 8157756.84975824 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -963.23202924879 -35.7679707512100 2 2 48.373780065298 -46.3737800652980 3 -999 -212.373641893069 -786.626358106931 4 -999 -337.907959056934 -661.092040943066 5 1.8 -29.5903467021456 31.3903467021456 6 0.7 -169.194447130584 169.894447130584 7 3.9 -215.351684056437 219.251684056437 8 1 -362.768490487736 363.768490487736 9 3.6 -335.39507014552 338.99507014552 10 1.4 -167.201678841046 168.601678841046 11 1.5 -237.821551274737 239.321551274737 12 0.7 -232.279196546538 232.979196546538 13 2.7 -89.6023871643393 92.3023871643393 14 -999 -179.900983996716 -819.099016003284 15 2.1 -219.052325068562 221.152325068562 16 0 -192.439786193443 192.439786193443 17 4.1 -206.213848568526 210.313848568526 18 1.2 -218.227073702398 219.427073702398 19 1.3 -157.174082802964 158.474082802964 20 6.1 -41.8781555588987 47.9781555588987 21 0.3 -216.501071243837 216.801071243837 22 0.5 -170.017384243957 170.517384243957 23 3.4 -125.930815129763 129.330815129763 24 -999 -579.878105474899 -419.121894525101 25 1.5 -455.428041183054 456.928041183054 26 -999 -205.308044890215 -793.691955109785 27 3.4 -37.7141228966307 41.1141228966307 28 0.8 -109.481632499302 110.281632499302 29 0.8 -205.639833984489 206.439833984489 30 -999 -217.950078388480 -781.04992161152 31 -999 -253.576025809866 -745.423974190134 32 1.4 17.8530858334797 -16.4530858334797 33 2 -216.978623155496 218.978623155496 34 1.9 8.49741898383276 -6.59741898383276 35 2.4 -439.944195541685 442.344195541685 36 2.8 -130.877885504545 133.677885504545 37 1.3 6.44532254895981 -5.14532254895981 38 2 4.38429732343694 -2.38429732343694 39 5.6 -257.611589413061 263.211589413061 40 3.1 -275.005970851481 278.105970851481 41 1 -196.098685614024 197.098685614024 42 1.8 -219.208159656376 221.008159656376 43 0.9 -181.448153174561 182.348153174561 44 1.8 -78.3062936567644 80.1062936567644 45 1.9 -200.945266132527 202.845266132527 46 0.9 -165.298734991178 166.198734991178 47 -999 -202.264971714751 -796.735028285249 48 2.6 51.2348145985303 -48.6348145985303 49 2.4 -210.804329694488 213.204329694488 50 1.2 -310.505434968675 311.705434968675 51 0.9 -235.009680290732 235.909680290732 52 0.5 -101.099262642958 101.599262642958 53 -999 -171.211322206906 -827.788677793094 54 0.6 -165.129282636393 165.729282636393 55 -999 -211.615381914769 -787.384618085231 56 2.2 44.4253392257145 -42.2253392257145 57 2.3 -88.4138102451983 90.7138102451983 58 0.5 23.2790765209106 -22.7790765209106 59 2.6 -252.223823400562 254.823823400562 60 0.6 -76.2038240146257 76.8038240146257 61 6.6 -259.206443563173 265.806443563173 62 -999 -303.452115931356 -695.547884068644 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.801888922508332 0.396222154983336 0.198111077491668 12 0.673137787118209 0.653724425763583 0.326862212881791 13 0.574807734560411 0.850384530879178 0.425192265439589 14 0.884317864451818 0.231364271096365 0.115682135548182 15 0.832997862875202 0.334004274249597 0.167002137124798 16 0.761181340194212 0.477637319611576 0.238818659805788 17 0.684503595783213 0.630992808433575 0.315496404216787 18 0.614208135252041 0.771583729495919 0.385791864747959 19 0.626717251914011 0.746565496171979 0.373282748085989 20 0.553650045098432 0.892699909803136 0.446349954901568 21 0.47408866149845 0.9481773229969 0.52591133850155 22 0.389911026383028 0.779822052766057 0.610088973616971 23 0.341126582547975 0.68225316509595 0.658873417452025 24 0.367492463336919 0.734984926673837 0.632507536663081 25 0.406889952080144 0.813779904160288 0.593110047919856 26 0.641724846384116 0.716550307231767 0.358275153615884 27 0.568109076207282 0.863781847585435 0.431890923792718 28 0.485765408243362 0.971530816486724 0.514234591756638 29 0.40507037313195 0.8101407462639 0.59492962686805 30 0.639557076490597 0.720885847018807 0.360442923509403 31 0.79650598342529 0.406988033149419 0.203494016574709 32 0.735757595051537 0.528484809896927 0.264242404948463 33 0.683594947097537 0.632810105804925 0.316405052902463 34 0.606887254812848 0.786225490374304 0.393112745187152 35 0.625690550924523 0.748618898150954 0.374309449075477 36 0.572597251502355 0.85480549699529 0.427402748497645 37 0.486867344462906 0.973734688925811 0.513132655537094 38 0.401592341278907 0.803184682557815 0.598407658721092 39 0.34669714330261 0.69339428660522 0.65330285669739 40 0.307551917478212 0.615103834956425 0.692448082521788 41 0.260293124575068 0.520586249150136 0.739706875424932 42 0.217586882481224 0.435173764962448 0.782413117518776 43 0.166889338950296 0.333778677900591 0.833110661049704 44 0.119107078535938 0.238214157071875 0.880892921464062 45 0.0872045261008273 0.174409052201655 0.912795473899173 46 0.195752775860494 0.391505551720989 0.804247224139506 47 0.310285026697057 0.620570053394113 0.689714973302943 48 0.232592546715942 0.465185093431884 0.767407453284058 49 0.155372592669991 0.310745185339982 0.844627407330009 50 0.0913350976532389 0.182670195306478 0.908664902346761 51 0.068321929500564 0.136643859001128 0.931678070499436 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/14/t1292320372o79asr8yx4l7uu0/10sm2k1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/10sm2k1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/1l3581292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/1l3581292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/2vumb1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/2vumb1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/3vumb1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/3vumb1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/4vumb1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/4vumb1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/5vumb1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/5vumb1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/66m3e1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/66m3e1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/7zv2z1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/7zv2z1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/8zv2z1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/8zv2z1292320451.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/9zv2z1292320451.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320372o79asr8yx4l7uu0/9zv2z1292320451.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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