ws7

*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: Fri, 20 Nov 2009 08:56:42 -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/20/t1258732789t8tsn95s06ztobg.htm/, Retrieved Fri, 20 Nov 2009 17:00:02 +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/2009/Nov/20/t1258732789t8tsn95s06ztobg.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 «

2756,76 3016,70 2849,27 3052,40 2921,44 3099,60 2981,85 3103,30 3080,58 3119,80 3106,22 3093,70 3119,31 3164,90 3061,26 3311,50 3097,31 3410,60 3161,69 3392,60 3257,16 3338,20 3277,01 3285,10 3295,32 3294,80 3363,99 3611,20 3494,17 3611,30 3667,03 3521,00 3813,06 3519,30 3917,96 3438,30 3895,51 3534,90 3801,06 3705,80 3570,12 3807,60 3701,61 3663,00 3862,27 3604,50 3970,10 3563,80 4138,52 3511,40 4199,75 3546,50 4290,89 3525,40 4443,91 3529,90 4502,64 3591,60 4356,98 3668,30 4591,27 3728,80 4696,96 3853,60 4621,40 3897,70 4562,84 3640,70 4202,52 3495,50 4296,49 3495,10 4435,23 3268,00 4105,18 3479,10 4116,68 3417,80 3844,49 3521,30 3720,98 3487,10 3674,40 3529,90 3857,62 3544,30 3801,06 3710,80 3504,37 3641,90 3032,60 3447,10 3047,03 3386,80 2962,34 3438,50 2197,82 3364,30 2014,45 3462,70 1862,83 3291,90 1905,41 3550,00 1810,99 3611,00 1670,07 3708,60 1864,44 3771,10 2052,02 4042,70 2029,60 3988,40 2070,83 3851,20 2293,41 3876,70

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 2724.04422398202 + 0.188672136013879`Zichtrekeningen `[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) 2724.04422398202 1711.204342 1.5919 0.116941 0.058471 `Zichtrekeningen ` 0.188672136013879 0.486376 0.3879 0.699525 0.349762 Multiple Linear Regression - Regression Statistics Multiple R 0.0513127948145057 R-squared 0.00263300291167556 Adjusted R-squared -0.0148646637039092 F-TEST (value) 0.150477373327620 F-TEST (DF numerator) 1 F-TEST (DF denominator) 57 p-value 0.699524510799623 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 853.9877328034 Sum Squared Residuals 41569817.7233853 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2756.76 3293.21145669509 -536.451456695092 2 2849.27 3299.94705195079 -450.677051950789 3 2921.44 3308.85237677064 -387.412376770645 4 2981.85 3309.55046367390 -327.700463673896 5 3080.58 3312.66355391813 -232.083553918125 6 3106.22 3307.73921116816 -201.519211168163 7 3119.31 3321.17266725235 -201.862667252351 8 3061.26 3348.83200239199 -287.572002391986 9 3097.31 3367.52941107096 -270.219411070961 10 3161.69 3364.13331262271 -202.443312622711 11 3257.16 3353.86954842356 -96.7095484235565 12 3277.01 3343.85105800122 -66.8410580012192 13 3295.32 3345.68117772055 -50.3611777205539 14 3363.99 3405.37704155535 -41.3870415553457 15 3494.17 3405.39590876895 88.7740912310531 16 3667.03 3388.35881488689 278.671185113107 17 3813.06 3388.03807225567 425.02192774433 18 3917.96 3372.75562923855 545.204370761454 19 3895.51 3390.98135757749 504.528642422514 20 3801.06 3423.22542562226 377.834574377741 21 3570.12 3442.43224906847 127.687750931528 22 3701.61 3415.15025820086 286.459741799136 23 3862.27 3404.11293824405 458.157061755947 24 3970.1 3396.43398230829 573.666017691712 25 4138.52 3386.54756238116 751.97243761884 26 4199.75 3393.16995435525 806.580045644752 27 4290.89 3389.18897228535 901.701027714646 28 4443.91 3390.03799689742 1053.87200310258 29 4502.64 3401.67906768947 1100.96093231053 30 4356.98 3416.15022052174 940.829779478261 31 4591.27 3427.56488475058 1163.70511524942 32 4696.96 3451.11116732511 1245.84883267489 33 4621.4 3459.43160852332 1161.96839147668 34 4562.84 3410.94286956775 1151.89713043225 35 4202.52 3383.54767541854 818.97232458146 36 4296.49 3383.47220656413 913.017793435866 37 4435.23 3340.62476447538 1094.60523552462 38 4105.18 3380.45345238791 724.726547612088 39 4116.68 3368.88785045026 747.792149549739 40 3844.49 3388.4154165277 456.074583472302 41 3720.98 3381.96282947602 339.017170523977 42 3674.4 3390.03799689742 284.362003102583 43 3857.62 3392.75487565602 464.865124343983 44 3801.06 3424.16878630233 376.891213697672 45 3504.37 3411.16927613097 93.2007238690282 46 3032.6 3374.41594403547 -341.815944035468 47 3047.03 3363.03901423383 -316.009014233831 48 2962.34 3372.79336366575 -410.453363665748 49 2197.82 3358.79389117352 -1160.97389117352 50 2014.45 3377.35922935728 -1362.90922935728 51 1862.83 3345.13402852611 -1482.30402852611 52 1905.41 3393.83030683130 -1488.42030683130 53 1810.99 3405.33930712814 -1594.34930712814 54 1670.07 3423.7537076031 -1753.68370760310 55 1864.44 3435.54571610396 -1571.10571610396 56 2052.02 3486.78906824533 -1434.76906824533 57 2029.6 3476.54417125978 -1446.94417125978 58 2070.83 3450.65835419868 -1379.82835419868 59 2293.41 3455.46949366703 -1162.05949366703 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000160260858545991 0.000320521717091981 0.999839739141454 6 0.000107590566214935 0.000215181132429869 0.999892409433785 7 1.09216463710682e-05 2.18432927421364e-05 0.999989078353629 8 1.75815778138549e-05 3.51631556277098e-05 0.999982418422186 9 2.93994683241157e-06 5.87989366482313e-06 0.999997060053168 10 3.24501830791238e-07 6.49003661582476e-07 0.99999967549817 11 7.20580726249537e-08 1.44116145249907e-07 0.999999927941927 12 2.41226928391766e-08 4.82453856783531e-08 0.999999975877307 13 6.8883202382099e-09 1.37766404764198e-08 0.99999999311168 14 8.60218118610691e-10 1.72043623722138e-09 0.999999999139782 15 1.22252677963667e-10 2.44505355927335e-10 0.999999999877747 16 2.46892871807879e-10 4.93785743615758e-10 0.999999999753107 17 9.94794726997828e-10 1.98958945399566e-09 0.999999999005205 18 1.00179869090714e-08 2.00359738181427e-08 0.999999989982013 19 7.00995827068686e-09 1.40199165413737e-08 0.999999992990042 20 1.46896592907789e-09 2.93793185815578e-09 0.999999998531034 21 9.77708595705104e-10 1.95541719141021e-09 0.999999999022291 22 1.99881184723772e-10 3.99762369447544e-10 0.999999999800119 23 7.3007192157926e-11 1.46014384315852e-10 0.999999999926993 24 6.10301213008156e-11 1.22060242601631e-10 0.99999999993897 25 2.46130638261067e-10 4.92261276522134e-10 0.99999999975387 26 5.82469712530865e-10 1.16493942506173e-09 0.99999999941753 27 2.02023073248592e-09 4.04046146497183e-09 0.99999999797977 28 1.12543661175767e-08 2.25087322351535e-08 0.999999988745634 29 3.20887868923206e-08 6.41775737846412e-08 0.999999967911213 30 2.62202467596579e-08 5.24404935193159e-08 0.999999973779753 31 4.12725668255237e-08 8.25451336510474e-08 0.999999958727433 32 7.99774216482774e-08 1.59954843296555e-07 0.999999920022578 33 2.46891102144227e-07 4.93782204288454e-07 0.999999753108898 34 1.11674851500439e-06 2.23349703000877e-06 0.999998883251485 35 1.57611303054016e-06 3.15222606108033e-06 0.99999842388697 36 3.68355018513957e-06 7.36710037027913e-06 0.999996316449815 37 4.53393797149537e-05 9.06787594299073e-05 0.999954660620285 38 6.76338247154399e-05 0.000135267649430880 0.999932366175285 39 0.000135449213342421 0.000270898426684842 0.999864550786658 40 0.000190091761492391 0.000380183522984783 0.999809908238508 41 0.000254862511913534 0.000509725023827068 0.999745137488086 42 0.000470221011065732 0.000940442022131465 0.999529778988934 43 0.00223110596542600 0.00446221193085201 0.997768894034574 44 0.0308317567773984 0.0616635135547968 0.969168243222602 45 0.215915040349879 0.431830080699757 0.784084959650121 46 0.378249474587941 0.756498949175883 0.621750525412059 47 0.682419882944925 0.63516023411015 0.317580117055075 48 0.992309892246493 0.0153802155070131 0.00769010775350654 49 0.99730666207316 0.00538667585368074 0.00269333792684037 50 0.997602897119908 0.00479420576018311 0.00239710288009156 51 0.996470656928029 0.0070586861439426 0.0035293430719713 52 0.994588836601942 0.0108223267961165 0.00541116339805827 53 0.986933123812263 0.0261337523754741 0.0130668761877370 54 0.981539077676503 0.0369218446469932 0.0184609223234966 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 42 0.84 NOK 5% type I error level 46 0.92 NOK 10% type I error level 47 0.94 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/10mpkz1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/10mpkz1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/127u01258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/127u01258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/2g8ad1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/2g8ad1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/37hpa1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/37hpa1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/4nicp1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/4nicp1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/54izr1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/54izr1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/65imb1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/65imb1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/76nxt1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/76nxt1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/8a8fe1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/8a8fe1258732597.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/92tqf1258732597.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732789t8tsn95s06ztobg/92tqf1258732597.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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