*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, 15 Dec 2009 06:51:57 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0.htm/, Retrieved Tue, 15 Dec 2009 14:53:57 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0.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 «

2407.6 0 2472.81 2408.64 2440.25 2350.44 2454.62 0 2407.6 2472.81 2408.64 2440.25 2448.05 0 2454.62 2407.6 2472.81 2408.64 2497.84 0 2448.05 2454.62 2407.6 2472.81 2645.64 0 2497.84 2448.05 2454.62 2407.6 2756.76 0 2645.64 2497.84 2448.05 2454.62 2849.27 0 2756.76 2645.64 2497.84 2448.05 2921.44 0 2849.27 2756.76 2645.64 2497.84 2981.85 0 2921.44 2849.27 2756.76 2645.64 3080.58 0 2981.85 2921.44 2849.27 2756.76 3106.22 0 3080.58 2981.85 2921.44 2849.27 3119.31 0 3106.22 3080.58 2981.85 2921.44 3061.26 0 3119.31 3106.22 3080.58 2981.85 3097.31 0 3061.26 3119.31 3106.22 3080.58 3161.69 0 3097.31 3061.26 3119.31 3106.22 3257.16 0 3161.69 3097.31 3061.26 3119.31 3277.01 0 3257.16 3161.69 3097.31 3061.26 3295.32 0 3277.01 3257.16 3161.69 3097.31 3363.99 0 3295.32 3277.01 3257.16 3161.69 3494.17 0 3363.99 3295.32 3277.01 3257.16 3667.03 0 3494.17 3363.99 3295.32 3277.01 3813.06 0 3667.03 3494.17 3363.99 3295.32 3917.96 0 3813.06 3667.03 3494.17 3363.99 3895.51 0 3917.96 3813.06 3667.03 3494.1 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 307.404120226389 -187.434876602855X[t] + 1.22439213648172Y1[t] -0.362941374014930Y2[t] + 0.240779335616266Y3[t] -0.196043217120352Y4[t] + 1.22772848146842t + 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) 307.404120226389 140.085689 2.1944 0.032089 0.016045 X -187.434876602855 142.662392 -1.3138 0.193903 0.096952 Y1 1.22439213648172 0.124857 9.8064 0 0 Y2 -0.362941374014930 0.199771 -1.8168 0.074245 0.037123 Y3 0.240779335616266 0.198463 1.2132 0.2298 0.1149 Y4 -0.196043217120352 0.125815 -1.5582 0.124449 0.062225 t 1.22772848146842 2.292339 0.5356 0.594229 0.297115 Multiple Linear Regression - Regression Statistics Multiple R 0.983125559196972 R-squared 0.96653586514636 Adjusted R-squared 0.963189451660995 F-TEST (value) 288.827387701372 F-TEST (DF numerator) 6 F-TEST (DF denominator) 60 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 162.672144492003 Sum Squared Residuals 1587733.59561762 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2407.6 2588.89981110313 -181.299811103135 2 2454.62 2461.77730426569 -7.1573042656855 3 2448.05 2565.89109406371 -117.841094063706 4 2497.84 2513.72774908416 -15.8877490841586 5 2645.64 2602.40790941742 43.2320905825749 6 2756.76 2755.73007235469 1.02992764530988 7 2849.27 2852.64592701942 -3.3759270194159 8 2921.44 2952.63832058993 -31.1983205899308 9 2981.85 3006.43493533446 -24.5849353344558 10 3080.58 3055.92488786957 24.6551121304256 11 3106.22 3155.35265021726 -49.1326502172637 12 3119.31 3152.53763190663 -33.2276319066319 13 3061.26 3172.41600968406 -111.156009684057 14 3097.31 3084.63510739581 12.674892604185 15 3161.69 3149.19617257527 12.4938274247332 16 3257.16 3199.62278412556 57.537215874439 17 3277.01 3314.43746802066 -37.4274680206603 18 3295.32 3313.75338308387 -18.4333830838726 19 3363.99 3340.5612861632 23.4287138367982 20 3494.17 3405.28578997216 88.884210027841 21 3667.03 3541.49891440251 125.531085597492 22 3813.06 3720.07212519824 92.9878748017614 23 3917.96 3855.24215764878 62.7178423512174 24 3895.51 3948.00850234968 -52.4985023496837 25 3801.06 3884.94905310159 -83.889053101591 26 3570.12 3775.31053944906 -205.190539449057 27 3701.61 3502.08653114664 199.523468853364 28 3862.27 3729.78682454449 132.483175455510 29 3970.1 3842.91293449369 127.187065506314 30 4138.52 3994.7910013047 143.728998695304 31 4199.75 4176.00077049334 23.749229506659 32 4290.89 4185.53837577694 105.351624223064 33 4443.91 4295.54701884882 148.362981151181 34 4502.64 4432.77807531937 69.8619246806269 35 4356.98 4460.31796738844 -103.337967388437 36 4591.27 4280.86186550173 310.408134498269 37 4696.96 4605.96090547551 90.9990945244939 38 4621.4 4604.97556817643 16.4244318235746 39 4562.84 4560.29679855298 2.54320144701819 40 4202.52 4496.7649763848 -294.244976384805 41 4296.49 4039.16048289488 257.329517105121 42 4435.23 4286.93236391852 148.29763608148 43 4105.18 4348.64933708459 -243.469337084593 44 4116.68 3988.67628085010 128.003719149896 45 3844.49 4138.75686330534 -294.266863305342 46 3720.98 3695.87521469325 25.1047853067478 47 3674.4 3712.14030916115 -37.7403091611464 48 3857.62 3633.37051667160 224.249483328395 49 3801.06 3899.45952912689 -98.3995291268932 50 3504.37 3777.93531611547 -273.565316115469 51 3032.6 3489.67338866354 -457.073388663538 52 3047.03 2971.41319771027 75.616802289733 53 2962.34 2913.75026341367 48.5897365863276 54 2197.82 2750.61857275322 -552.79857275322 55 2014.45 1942.47328437082 71.9767156291824 56 1862.83 1973.43966049114 -110.609660491139 57 1905.41 1688.09989538494 217.310104615059 58 1810.99 1902.21866574684 -91.228665746842 59 1670.07 1771.82672685337 -101.756726853371 60 1864.44 1674.75849668665 189.681503313347 61 2052.02 1934.03511810839 117.984881891614 62 2029.6 2078.96918526927 -49.3691852692728 63 2070.83 2059.09218873343 11.7378112665661 64 2293.41 2125.99921827067 167.410781729325 65 2443.27 2342.61601626766 100.653983732343 66 2513.17 2460.87028022933 52.29971977067 67 2466.92 2538.80242742059 -71.8824274205897 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0229416989403446 0.0458833978806891 0.977058301059655 11 0.00588021589841729 0.0117604317968346 0.994119784101583 12 0.00222681038404363 0.00445362076808726 0.997773189615956 13 0.00238514139779229 0.00477028279558458 0.997614858602208 14 0.000615547066245252 0.00123109413249050 0.999384452933755 15 0.000156724706886389 0.000313449413772779 0.999843275293114 16 3.61026939754374e-05 7.22053879508747e-05 0.999963897306025 17 5.2051270582645e-05 0.00010410254116529 0.999947948729417 18 3.42194139727023e-05 6.84388279454046e-05 0.999965780586027 19 9.40827885258713e-06 1.88165577051743e-05 0.999990591721147 20 3.77650278872600e-06 7.55300557745199e-06 0.999996223497211 21 1.93104398333936e-06 3.86208796667873e-06 0.999998068956017 22 5.58884512309171e-07 1.11776902461834e-06 0.999999441115488 23 1.46281497565550e-07 2.92562995131101e-07 0.999999853718502 24 5.41567581616034e-08 1.08313516323207e-07 0.999999945843242 25 2.21774668205670e-08 4.43549336411341e-08 0.999999977822533 26 1.34249980327344e-07 2.68499960654687e-07 0.99999986575002 27 2.21462406839869e-06 4.42924813679738e-06 0.999997785375932 28 7.7444289190244e-07 1.54888578380488e-06 0.999999225557108 29 1.01670082706493e-06 2.03340165412986e-06 0.999998983299173 30 3.27087787700312e-07 6.54175575400623e-07 0.999999672912212 31 1.46604035214357e-07 2.93208070428714e-07 0.999999853395965 32 5.26124952833673e-08 1.05224990566735e-07 0.999999947387505 33 7.26595483817889e-08 1.45319096763578e-07 0.999999927340452 34 2.63422708076914e-08 5.26845416153829e-08 0.99999997365773 35 3.74704165096123e-08 7.49408330192246e-08 0.999999962529583 36 1.43748485717807e-06 2.87496971435613e-06 0.999998562515143 37 9.84289870938838e-07 1.96857974187768e-06 0.999999015710129 38 4.32473702981861e-07 8.64947405963721e-07 0.999999567526297 39 2.70053625535055e-07 5.40107251070109e-07 0.999999729946375 40 2.91747480832156e-05 5.83494961664313e-05 0.999970825251917 41 5.51358457072108e-05 0.000110271691414422 0.999944864154293 42 8.47601567813944e-05 0.000169520313562789 0.999915239843219 43 0.00226512331105596 0.00453024662211192 0.997734876688944 44 0.00845157462335766 0.0169031492467153 0.991548425376642 45 0.0218163041783675 0.043632608356735 0.978183695821633 46 0.0192424584906255 0.0384849169812511 0.980757541509374 47 0.0130512335139735 0.026102467027947 0.986948766486026 48 0.0553269980095596 0.110653996019119 0.94467300199044 49 0.0780247646436685 0.156049529287337 0.921975235356332 50 0.115851845894683 0.231703691789365 0.884148154105317 51 0.14447564361592 0.28895128723184 0.85552435638408 52 0.111670949717246 0.223341899434492 0.888329050282754 53 0.79716440154936 0.405671196901281 0.202835598450641 54 0.769762059075781 0.460475881848437 0.230237940924219 55 0.968730130218135 0.0625397395637304 0.0312698697818652 56 0.920827466005956 0.158345067988088 0.079172533994044 57 0.879276986507603 0.241446026984794 0.120723013492397 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 32 0.666666666666667 NOK 5% type I error level 38 0.791666666666667 NOK 10% type I error level 39 0.8125 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/10hyhm1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/10hyhm1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/1zs091260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/1zs091260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/2copi1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/2copi1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/32ac11260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/32ac11260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/42sqq1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/42sqq1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/57r671260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/57r671260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/6sl4z1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/6sl4z1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/7b3wl1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/7b3wl1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/823qn1260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/823qn1260885113.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/9qhm91260885113.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260885225e25wkrwp784xoz0/9qhm91260885113.ps (open in new window)

Parameters (Session):

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by