*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: Sun, 22 Nov 2009 08:53:26 -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/22/t12589054213vxzm7ck46fwtwf.htm/, Retrieved Sun, 22 Nov 2009 16:57:13 +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/22/t12589054213vxzm7ck46fwtwf.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:

ws7m2.1

Dataseries X:

» Textbox « » Textfile « » CSV «

2756.76 0 2849.27 0 2921.44 0 2981.85 0 3080.58 0 3106.22 0 3119.31 0 3061.26 0 3097.31 0 3161.69 0 3257.16 0 3277.01 0 3295.32 0 3363.99 0 3494.17 0 3667.03 1 3813.06 1 3917.96 1 3895.51 1 3801.06 1 3570.12 0 3701.61 1 3862.27 1 3970.1 1 4138.52 1 4199.75 1 4290.89 1 4443.91 1 4502.64 1 4356.98 1 4591.27 1 4696.96 1 4621.4 1 4562.84 1 4202.52 1 4296.49 1 4435.23 1 4105.18 1 4116.68 1 3844.49 1 3720.98 1 3674.4 1 3857.62 1 3801.06 1 3504.37 1 3032.6 1 3047.03 0 2962.34 1 2197.82 1 2014.45 1 1862.83 0 1905.41 0 1810.99 0 1670.07 0 1864.44 0 2052.02 0 2029.6 0 2070.83 0 2293.41 0 2443.27 0

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] = + 3339.86513177408 + 1304.98832890692`X `[t] -22.5208594318981t + 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) 3339.86513177408 134.536134 24.825 0 0 `X ` 1304.98832890692 124.828528 10.4542 0 0 t -22.5208594318981 3.585925 -6.2803 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.835206925173309 R-squared 0.697570607857453 Adjusted R-squared 0.686959050238417 F-TEST (value) 65.7368722765109 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 1.55431223447522e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 475.167067871291 Sum Squared Residuals 12869673.3161958 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2756.76 3317.34427234219 -560.584272342194 2 2849.27 3294.82341291029 -445.553412910291 3 2921.44 3272.30255347839 -350.862553478391 4 2981.85 3249.78169404649 -267.931694046493 5 3080.58 3227.26083461459 -146.680834614595 6 3106.22 3204.73997518270 -98.5199751826969 7 3119.31 3182.2191157508 -62.9091157507986 8 3061.26 3159.6982563189 -98.4382563189002 9 3097.31 3137.177396887 -39.8673968870024 10 3161.69 3114.65653745510 47.0334625448959 11 3257.16 3092.13567802321 165.024321976794 12 3277.01 3069.61481859131 207.395181408692 13 3295.32 3047.09395915941 248.226040840590 14 3363.99 3024.57309972751 339.416900272488 15 3494.17 3002.05224029561 492.117759704387 16 3667.03 4284.51970977064 -617.489709770638 17 3813.06 4261.99885033874 -448.93885033874 18 3917.96 4239.47799090684 -321.517990906842 19 3895.51 4216.95713147494 -321.447131474943 20 3801.06 4194.43627204305 -393.376272043046 21 3570.12 2866.92708370422 703.192916295775 22 3701.61 4149.39455317925 -447.784553179249 23 3862.27 4126.87369374735 -264.603693747351 24 3970.1 4104.35283431545 -134.252834315453 25 4138.52 4081.83197488355 56.6880251164455 26 4199.75 4059.31111545166 140.438884548343 27 4290.89 4036.79025601976 254.099743980242 28 4443.91 4014.26939658786 429.640603412139 29 4502.64 3991.74853715596 510.891462844038 30 4356.98 3969.22767772406 387.752322275935 31 4591.27 3946.70681829217 644.563181707834 32 4696.96 3924.18595886027 772.774041139732 33 4621.4 3901.66509942837 719.73490057163 34 4562.84 3879.14423999647 683.695760003529 35 4202.52 3856.62338056457 345.896619435427 36 4296.49 3834.10252113268 462.387478867324 37 4435.23 3811.58166170078 623.648338299222 38 4105.18 3789.06080226888 316.119197731121 39 4116.68 3766.53994283698 350.140057163019 40 3844.49 3744.01908340508 100.470916594917 41 3720.98 3721.49822397318 -0.518223973184703 42 3674.4 3698.97736454129 -24.5773645412865 43 3857.62 3676.45650510939 181.163494890611 44 3801.06 3653.93564567749 147.124354322510 45 3504.37 3631.41478624559 -127.044786245592 46 3032.6 3608.89392681369 -576.293926813694 47 3047.03 2281.38473847487 765.645261525127 48 2962.34 3563.8522079499 -601.512207949898 49 2197.82 3541.331348518 -1343.511348518 50 2014.45 3518.8104890861 -1504.3604890861 51 1862.83 2191.30130074728 -328.471300747281 52 1905.41 2168.78044131538 -263.370441315382 53 1810.99 2146.25958188348 -335.269581883484 54 1670.07 2123.73872245159 -453.668722451586 55 1864.44 2101.21786301969 -236.777863019688 56 2052.02 2078.69700358779 -26.6770035877898 57 2029.6 2056.17614415589 -26.5761441558916 58 2070.83 2033.65528472399 37.1747152760064 59 2293.41 2011.13442529210 282.275574707905 60 2443.27 1988.61356586020 454.656434139803 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.000124209146068425 0.000248418292136851 0.999875790853932 7 5.54915922406518e-05 0.000110983184481304 0.99994450840776 8 0.000125180127005182 0.000250360254010364 0.999874819872995 9 3.64016714144054e-05 7.28033428288108e-05 0.999963598328586 10 5.39546348968978e-06 1.07909269793796e-05 0.99999460453651 11 6.84196580268048e-07 1.36839316053610e-06 0.99999931580342 12 7.69511262090618e-08 1.53902252418124e-07 0.999999923048874 13 8.73703521956666e-09 1.74740704391333e-08 0.999999991262965 14 8.88558719092688e-10 1.77711743818538e-09 0.999999999111441 15 3.06631004237721e-10 6.13262008475442e-10 0.99999999969337 16 4.59790369025354e-11 9.19580738050708e-11 0.999999999954021 17 1.61353527280524e-11 3.22707054561048e-11 0.999999999983865 18 8.8016062624149e-12 1.76032125248298e-11 0.999999999991198 19 1.48587334604746e-12 2.97174669209492e-12 0.999999999998514 20 2.13675350796598e-12 4.27350701593197e-12 0.999999999997863 21 6.07973410896417e-13 1.21594682179283e-12 0.999999999999392 22 6.97497282635232e-11 1.39499456527046e-10 0.99999999993025 23 6.16946446089152e-11 1.23389289217830e-10 0.999999999938305 24 3.36728872938233e-11 6.73457745876466e-11 0.999999999966327 25 3.71620200587646e-11 7.43240401175292e-11 0.999999999962838 26 4.53255388543792e-11 9.06510777087585e-11 0.999999999954674 27 8.15161214116907e-11 1.63032242823381e-10 0.999999999918484 28 4.7808271913803e-10 9.5616543827606e-10 0.999999999521917 29 1.14954052233281e-09 2.29908104466562e-09 0.99999999885046 30 5.23545498595001e-10 1.04709099719000e-09 0.999999999476455 31 5.44911830431442e-10 1.08982366086288e-09 0.999999999455088 32 7.1852006141997e-10 1.43704012283994e-09 0.99999999928148 33 2.24089271327725e-10 4.48178542655450e-10 0.99999999977591 34 6.403631128786e-11 1.2807262257572e-10 0.999999999935964 35 2.87751789060804e-09 5.75503578121607e-09 0.999999997122482 36 7.82228092667963e-09 1.56445618533593e-08 0.999999992177719 37 6.98647747021682e-09 1.39729549404336e-08 0.999999993013523 38 1.56956868939592e-07 3.13913737879185e-07 0.99999984304313 39 1.01446168510863e-06 2.02892337021725e-06 0.999998985538315 40 2.37600546629739e-05 4.75201093259477e-05 0.999976239945337 41 0.000233414248863525 0.000466828497727049 0.999766585751136 42 0.000936452793840267 0.00187290558768053 0.99906354720616 43 0.00234455292420612 0.00468910584841224 0.997655447075794 44 0.0103590732120489 0.0207181464240977 0.989640926787951 45 0.0540845483564334 0.108169096712867 0.945915451643567 46 0.176298843656041 0.352597687312082 0.823701156343959 47 0.742600935130472 0.514798129739055 0.257399064869528 48 0.9978828266335 0.00423434673299958 0.00211717336649979 49 0.999210907721384 0.00157818455723265 0.000789092278616324 50 0.998901992690212 0.00219601461957596 0.00109800730978798 51 0.99820743916395 0.00358512167210126 0.00179256083605063 52 0.998901170129479 0.00219765974104289 0.00109882987052144 53 0.998399892633088 0.00320021473382482 0.00160010736691241 54 0.992158454902455 0.0156830901950897 0.00784154509754487 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 44 0.897959183673469 NOK 5% type I error level 46 0.938775510204082 NOK 10% type I error level 46 0.938775510204082 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/105siw1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/105siw1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/1sgzi1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/1sgzi1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/2yggf1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/2yggf1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/3fdet1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/3fdet1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/4p51m1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/4p51m1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/5le6g1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/5le6g1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/6qu9h1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/6qu9h1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/7uwc51258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/7uwc51258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/8xj6r1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/8xj6r1258905202.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/91pup1258905202.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589054213vxzm7ck46fwtwf/91pup1258905202.ps (open in new window)

Parameters (Session):

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

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