Seatbelt law_Q3

*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, 26 Nov 2008 10:53:01 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7.htm/, Retrieved Wed, 26 Nov 2008 17:57:41 +0000

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/2008/Nov/26/t1227722261g8yv4m6by84zvh7.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

82.7 0 88.9 0 105.9 0 100.8 0 94 0 105 0 58.5 0 87.6 0 113.1 0 112.5 0 89.6 0 74.5 0 82.7 0 90.1 0 109.4 0 96 0 89.2 0 109.1 0 49.1 0 92.9 0 107.7 0 103.5 0 91.1 0 79.8 0 71.9 0 82.9 0 90.1 0 100.7 0 90.7 0 108.8 0 44.1 0 93.6 0 107.4 0 96.5 0 93.6 0 76.5 0 76.7 0 84 0 103.3 0 88.5 1 99 1 105.9 1 44.7 1 94 1 107.1 1 104.8 1 102.5 1 77.7 1 85.2 1 91.3 1 106.5 1 92.4 1 97.5 1 107 1 51.1 1 98.6 1 102.2 1 114.3 1 99.4 1 72.5 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 78.5888484848485 + 5.46787878787879X[t] + 3.33535353535353M1[t] + 11.0624646464646M2[t] + 26.7895757575758M3[t] + 18.4631111111111M4[t] + 16.9902222222222M5[t] + 30.1973333333333M6[t] -27.3355555555556M7[t] + 16.6315555555556M8[t] + 30.9186666666667M9[t] + 29.8657777777778M10[t] + 18.9128888888889M11[t] -0.127111111111111t + 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) 78.5888484848485 2.755952 28.516 0 0 X 5.46787878787879 2.403304 2.2752 0.027603 0.013802 M1 3.33535353535353 3.116519 1.0702 0.290105 0.145052 M2 11.0624646464646 3.110142 3.5569 0.000883 0.000441 M3 26.7895757575758 3.105172 8.6274 0 0 M4 18.4631111111111 3.132501 5.894 0 0 M5 16.9902222222222 3.12192 5.4422 2e-06 1e-06 M6 30.1973333333333 3.11272 9.7013 0 0 M7 -27.3355555555556 3.104914 -8.804 0 0 M8 16.6315555555556 3.098513 5.3676 3e-06 1e-06 M9 30.9186666666667 3.093525 9.9946 0 0 M10 29.8657777777778 3.089957 9.6654 0 0 M11 18.9128888888889 3.087815 6.125 0 0 t -0.127111111111111 0.066424 -1.9136 0.061901 0.030951 Multiple Linear Regression - Regression Statistics Multiple R 0.966106860242352 R-squared 0.933362465407336 Adjusted R-squared 0.914530118674626 F-TEST (value) 49.5616652908264 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.88113417995659 Sum Squared Residuals 1095.97166060606 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 82.7 81.797090909091 0.902909090909051 2 88.9 89.3970909090909 -0.497090909090899 3 105.9 104.997090909091 0.902909090909096 4 100.8 96.5435151515152 4.25648484848485 5 94 94.9435151515151 -0.943515151515137 6 105 108.023515151515 -3.02351515151516 7 58.5 50.3635151515152 8.13648484848484 8 87.6 94.2035151515151 -6.60351515151515 9 113.1 108.363515151515 4.73648484848485 10 112.5 107.183515151515 5.31648484848486 11 89.6 96.1035151515151 -6.50351515151515 12 74.5 77.0635151515151 -2.56351515151515 13 82.7 80.2717575757576 2.42824242424244 14 90.1 87.8717575757576 2.22824242424242 15 109.4 103.471757575758 5.92824242424243 16 96 95.0181818181818 0.981818181818181 17 89.2 93.4181818181818 -4.21818181818182 18 109.1 106.498181818182 2.60181818181818 19 49.1 48.8381818181818 0.261818181818177 20 92.9 92.6781818181818 0.221818181818191 21 107.7 106.838181818182 0.861818181818185 22 103.5 105.658181818182 -2.15818181818182 23 91.1 94.5781818181818 -3.47818181818182 24 79.8 75.5381818181818 4.26181818181818 25 71.9 78.7464242424242 -6.84642424242423 26 82.9 86.3464242424242 -3.44642424242424 27 90.1 101.946424242424 -11.8464242424242 28 100.7 93.4928484848485 7.20715151515152 29 90.7 91.8928484848485 -1.19284848484848 30 108.8 104.972848484848 3.82715151515151 31 44.1 47.3128484848485 -3.21284848484849 32 93.6 91.1528484848485 2.44715151515151 33 107.4 105.312848484848 2.08715151515152 34 96.5 104.132848484848 -7.63284848484849 35 93.6 93.0528484848485 0.547151515151511 36 76.5 74.0128484848485 2.48715151515151 37 76.7 77.2210909090909 -0.521090909090901 38 84 84.8210909090909 -0.821090909090911 39 103.3 100.421090909091 2.87890909090908 40 88.5 97.435393939394 -8.93539393939394 41 99 95.835393939394 3.16460606060606 42 105.9 108.915393939394 -3.01539393939393 43 44.7 51.2553939393939 -6.55539393939393 44 94 95.095393939394 -1.09539393939394 45 107.1 109.255393939394 -2.15539393939395 46 104.8 108.075393939394 -3.27539393939394 47 102.5 96.995393939394 5.50460606060606 48 77.7 77.955393939394 -0.255393939393936 49 85.2 81.1636363636364 4.03636363636364 50 91.3 88.7636363636364 2.53636363636363 51 106.5 104.363636363636 2.13636363636363 52 92.4 95.9100606060606 -3.5100606060606 53 97.5 94.3100606060606 3.18993939393939 54 107 107.390060606061 -0.390060606060606 55 51.1 49.7300606060606 1.3699393939394 56 98.6 93.5700606060606 5.02993939393938 57 102.2 107.730060606061 -5.53006060606061 58 114.3 106.550060606061 7.74993939393939 59 99.4 95.4700606060606 3.9299393939394 60 72.5 76.4300606060606 -3.9300606060606 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.249376670897890 0.498753341795781 0.75062332910211 18 0.205271232533461 0.410542465066921 0.794728767466539 19 0.339345848965805 0.67869169793161 0.660654151034195 20 0.322025611773023 0.644051223546045 0.677974388226977 21 0.275424995981604 0.550849991963208 0.724575004018396 22 0.292552552011266 0.585105104022533 0.707447447988734 23 0.222749759648484 0.445499519296968 0.777250240351516 24 0.261065483784016 0.522130967568031 0.738934516215984 25 0.341300819726434 0.682601639452868 0.658699180273566 26 0.265468937446474 0.530937874892949 0.734531062553526 27 0.618167014735459 0.763665970529082 0.381832985264541 28 0.843445129927721 0.313109740144558 0.156554870072279 29 0.805603120991282 0.388793758017437 0.194396879008718 30 0.827127225245415 0.345745549509171 0.172872774754585 31 0.78539777793647 0.42920444412706 0.21460222206353 32 0.763564603413887 0.472870793172226 0.236435396586113 33 0.812294623365126 0.375410753269747 0.187705376634874 34 0.871897363038449 0.256205273923102 0.128102636961551 35 0.849682208629059 0.300635582741883 0.150317791370941 36 0.877899360706807 0.244201278586385 0.122100639293193 37 0.823109074944036 0.353781850111928 0.176890925055964 38 0.752463904054341 0.495072191891317 0.247536095945659 39 0.665966030948833 0.668067938102334 0.334033969051167 40 0.560225126371588 0.879549747256825 0.439774873628412 41 0.551307739080816 0.897384521838367 0.448692260919184 42 0.398046473457172 0.796092946914343 0.601953526542828 43 0.325148347509542 0.650296695019083 0.674851652490458 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/10zr4l1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/10zr4l1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/1npai1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/1npai1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/2ik671227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/2ik671227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/3ga0d1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/3ga0d1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/430d41227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/430d41227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/50zsz1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/50zsz1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/6z0oz1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/6z0oz1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/72ykl1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/72ykl1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/82nhh1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/82nhh1227721969.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/9jlth1227721969.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227722261g8yv4m6by84zvh7/9jlth1227721969.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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