Multiple regression - model 1

*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, 18 Nov 2009 08:49:42 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb.htm/, Retrieved Wed, 18 Nov 2009 16:52:17 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb.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 «

3.58 98.2 3.52 98.71 3.45 98.54 3.36 98.2 3.27 96.92 3.21 99.06 3.19 99.65 3.16 99.82 3.12 99.99 3.06 100.33 3.01 99.31 2.98 101.1 2.97 101.1 3.02 100.93 3.07 100.85 3.18 100.93 3.29 99.6 3.43 101.88 3.61 101.81 3.74 102.38 3.87 102.74 3.88 102.82 4.09 101.72 4.19 103.47 4.2 102.98 4.29 102.68 4.37 102.9 4.47 103.03 4.61 101.29 4.65 103.69 4.69 103.68 4.82 104.2 4.86 104.08 4.87 104.16 5.01 103.05 5.03 104.66 5.13 104.46 5.18 104.95 5.21 105.85 5.26 106.23 5.25 104.86 5.2 107.44 5.16 108.23 5.19 108.45 5.39 109.39 5.58 110.15 5.76 109.13 5.89 110.28 5.98 110.17 6.02 109.99 5.62 109.26 4.87 109.11 4.24 107.06 4.02 109.53 3.74 108.92 3.45 109.24 3.34 109.12 3.21 109 3.12 107.23 3.04 109.49

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -11.3899845756242 + 0.149706958152602X[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) -11.3899845756242 2.629199 -4.3321 5.9e-05 3e-05 X 0.149706958152602 0.025231 5.9334 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.614588260410374 R-squared 0.377718729834249 Adjusted R-squared 0.366989742417598 F-TEST (value) 35.2054406595769 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.75453659823077e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.752389648675282 Sum Squared Residuals 32.8332306391554 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.58 3.31123871496123 0.268761285038765 2 3.52 3.38758926361906 0.132410736380944 3 3.45 3.36213908073312 0.0878609192668842 4 3.36 3.31123871496123 0.048761285038769 5 3.27 3.1196138085259 0.150386191474099 6 3.21 3.43998669897247 -0.229986698972468 7 3.19 3.52831380428250 -0.338313804282504 8 3.16 3.55376398716844 -0.393763987168444 9 3.12 3.57921417005439 -0.459214170054387 10 3.06 3.63011453582627 -0.570114535826271 11 3.01 3.47741343851062 -0.467413438510619 12 2.98 3.74538889360377 -0.765388893603774 13 2.97 3.74538889360377 -0.775388893603774 14 3.02 3.71993871071783 -0.699938710717834 15 3.07 3.70796215406562 -0.637962154065624 16 3.18 3.71993871071783 -0.539938710717834 17 3.29 3.52082845637487 -0.230828456374872 18 3.43 3.86216032096280 -0.432160320962803 19 3.61 3.85168083389212 -0.241680833892123 20 3.74 3.93701380003910 -0.197013800039104 21 3.87 3.99090830497404 -0.120908304974041 22 3.88 4.00288486162625 -0.122884861626249 23 4.09 3.83820720765839 0.251792792341612 24 4.19 4.10019438442544 0.0898056155745597 25 4.2 4.02683797493067 0.173162025069333 26 4.29 3.98192588748489 0.308074112515113 27 4.37 4.01486141827846 0.355138581721541 28 4.47 4.0343233228383 0.435676677161703 29 4.61 3.77383321565277 0.83616678434723 30 4.65 4.13312991521901 0.516870084780988 31 4.69 4.13163284563749 0.558367154362512 32 4.82 4.20948046387684 0.61051953612316 33 4.86 4.19151562889853 0.668484371101473 34 4.87 4.20349218555074 0.666507814449265 35 5.01 4.03731746200135 0.972682537998652 36 5.03 4.27834566462704 0.751654335372964 37 5.13 4.24840427299652 0.881595727003484 38 5.18 4.32176068249129 0.858239317508708 39 5.21 4.45649694482863 0.753503055171368 40 5.26 4.51338558892662 0.746614411073378 41 5.25 4.30828705625756 0.941712943742443 42 5.2 4.69453100829127 0.505468991708731 43 5.16 4.81279950523183 0.347200494768175 44 5.19 4.8457350360254 0.344264963974603 45 5.39 4.98645957668884 0.403540423311157 46 5.58 5.10023686488482 0.47976313511518 47 5.76 4.94753576756916 0.812464232430835 48 5.89 5.11969876944466 0.770301230555342 49 5.98 5.10323100404787 0.876768995952129 50 6.02 5.0762837515804 0.943716248419597 51 5.62 4.966997672129 0.653002327870995 52 4.87 4.94454162840611 -0.0745416284061137 53 4.24 4.63764236419328 -0.397642364193281 54 4.02 5.00741855083021 -0.987418550830207 55 3.74 4.91609730635712 -1.17609730635712 56 3.45 4.96400353296595 -1.51400353296595 57 3.34 4.94603869798764 -1.60603869798764 58 3.21 4.92807386300933 -1.71807386300933 59 3.12 4.66309254707922 -1.54309254707922 60 3.04 5.0014302725041 -1.96143027250410 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00245264811398895 0.00490529622797789 0.997547351886011 6 0.00319922961207913 0.00639845922415825 0.99680077038792 7 0.00108145187021525 0.00216290374043051 0.998918548129785 8 0.000267792092570144 0.000535584185140288 0.99973220790743 9 6.10005097000873e-05 0.000122001019400175 0.9999389994903 10 1.35171005977400e-05 2.70342011954799e-05 0.999986482899402 11 7.37820152169173e-06 1.47564030433835e-05 0.999992621798478 12 1.50474025134314e-06 3.00948050268628e-06 0.999998495259749 13 3.05655911312344e-07 6.11311822624688e-07 0.999999694344089 14 5.948841386796e-08 1.1897682773592e-07 0.999999940511586 15 1.19244085247179e-08 2.38488170494358e-08 0.999999988075591 16 4.04612341420364e-09 8.09224682840728e-09 0.999999995953877 17 9.31911020203407e-10 1.86382204040681e-09 0.99999999906809 18 1.13662533408620e-08 2.27325066817239e-08 0.999999988633747 19 9.93316750146693e-08 1.98663350029339e-07 0.999999900668325 20 5.2828342363588e-07 1.05656684727176e-06 0.999999471716576 21 1.62191644549707e-06 3.24383289099414e-06 0.999998378083555 22 2.13101809415254e-06 4.26203618830509e-06 0.999997868981906 23 6.80422435370481e-06 1.36084487074096e-05 0.999993195775646 24 9.20499868458795e-06 1.84099973691759e-05 0.999990795001315 25 1.02308421526926e-05 2.04616843053852e-05 0.999989769157847 26 1.32191743338568e-05 2.64383486677136e-05 0.999986780825666 27 1.51241554156497e-05 3.02483108312994e-05 0.999984875844584 28 1.70819036375978e-05 3.41638072751955e-05 0.999982918096362 29 7.02890649473422e-05 0.000140578129894684 0.999929710935053 30 5.73797713037114e-05 0.000114759542607423 0.999942620228696 31 4.37228431317021e-05 8.74456862634041e-05 0.999956277156868 32 2.99174030299255e-05 5.9834806059851e-05 0.99997008259697 33 2.04061823048621e-05 4.08123646097242e-05 0.999979593817695 34 1.23560771178477e-05 2.47121542356954e-05 0.999987643922882 35 1.65023187277443e-05 3.30046374554886e-05 0.999983497681272 36 8.91016882175798e-06 1.78203376435160e-05 0.999991089831178 37 5.76133901855328e-06 1.15226780371066e-05 0.999994238660981 38 3.28011134143863e-06 6.56022268287726e-06 0.999996719888659 39 1.63009974931822e-06 3.26019949863643e-06 0.99999836990025 40 9.10091282958437e-07 1.82018256591687e-06 0.999999089908717 41 2.18213060906647e-06 4.36426121813294e-06 0.99999781786939 42 3.52044572634056e-06 7.04089145268112e-06 0.999996479554274 43 5.22724233460637e-06 1.04544846692127e-05 0.999994772757665 44 7.40575459433527e-06 1.48115091886705e-05 0.999992594245406 45 6.06329863160839e-06 1.21265972632168e-05 0.999993936701368 46 3.44918759742395e-06 6.8983751948479e-06 0.999996550812403 47 6.92325939860476e-06 1.38465187972095e-05 0.999993076740601 48 5.84976181480035e-06 1.16995236296007e-05 0.999994150238185 49 1.38233523040288e-05 2.76467046080576e-05 0.999986176647696 50 0.000333491614070526 0.000666983228141053 0.99966650838593 51 0.0280227229520301 0.0560454459040602 0.97197727704797 52 0.330124302599801 0.660248605199601 0.669875697400199 53 0.646907072175468 0.706185855649065 0.353092927824532 54 0.858648346010733 0.282703307978533 0.141351653989266 55 0.949576216582894 0.100847566834211 0.0504237834171055 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 46 0.901960784313726 NOK 5% type I error level 46 0.901960784313726 NOK 10% type I error level 47 0.92156862745098 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/10b7i41258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/10b7i41258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/168vx1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/168vx1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/2ktlx1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/2ktlx1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/3w5sz1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/3w5sz1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/4u7xf1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/4u7xf1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/5x7s71258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/5x7s71258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/66rd61258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/66rd61258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/7xrlz1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/7xrlz1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/8kotm1258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/8kotm1258559376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/9aid81258559376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559525cxe0xg7jrhzk4mb/9aid81258559376.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