the Seatbelt Law-Q3 lineair+seasonality

*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, 21 Nov 2008 05:07:32 -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/21/t1227269333mezf0ezroikfit6.htm/, Retrieved Fri, 21 Nov 2008 12:09:06 +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/21/t1227269333mezf0ezroikfit6.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 «

99.29 0 98.69 0 107.92 0 101.03 0 97.55 0 103.02 0 94.08 0 94.12 0 115.08 0 116.48 0 103.42 0 112.51 0 95.55 0 97.53 0 119.26 0 100.94 0 97.73 0 115.25 0 92.8 0 99.2 0 118.69 0 110.12 0 110.26 0 112.9 0 102.17 1 99.38 1 116.1 1 103.77 1 101.81 1 113.74 1 89.67 1 99.5 1 122.89 1 108.61 1 114.37 1 110.5 1 104.08 1 103.64 1 121.61 1 101.14 1 115.97 1 120.12 1 95.97 1 105.01 1 124.68 1 123.89 1 123.61 1 114.76 1 108.75 1 106.09 1 123.17 1 106.16 1 115.18 1 120.6 1 109.48 1 114.44 1 121.44 1 129.48 1 124.32 1 112.59 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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation omzet[t] = + 102.207166666667 -3.04361111111111dummievariabele[t] -6.93452777777782M1[t] -8.1773888888889M2[t] + 8.02775000000002M3[t] -7.3171111111111M4[t] -4.61797222222222M5[t] + 3.93916666666666M6[t] -14.5476944444444M7[t] -8.83455555555555M8[t] + 8.92658333333334M9[t] + 5.74572222222222M10[t] + 2.88486111111111M11[t] + 0.340861111111111t + 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) 102.207166666667 2.273375 44.9583 0 0 dummievariabele -3.04361111111111 2.187556 -1.3913 0.170818 0.085409 M1 -6.93452777777782 2.71542 -2.5538 0.014035 0.007018 M2 -8.1773888888889 2.699956 -3.0287 0.004019 0.002009 M3 8.02775000000002 2.685888 2.9889 0.004483 0.002241 M4 -7.3171111111111 2.673238 -2.7372 0.00878 0.00439 M5 -4.61797222222222 2.662026 -1.7348 0.08948 0.04474 M6 3.93916666666666 2.652271 1.4852 0.144311 0.072155 M7 -14.5476944444444 2.643988 -5.5022 2e-06 1e-06 M8 -8.83455555555555 2.637192 -3.35 0.001621 0.000811 M9 8.92658333333334 2.631895 3.3917 0.001436 0.000718 M10 5.74572222222222 2.628104 2.1863 0.033922 0.016961 M11 2.88486111111111 2.625827 1.0986 0.277639 0.138819 t 0.340861111111111 0.063149 5.3977 2e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.927865011933247 R-squared 0.860933480369885 Adjusted R-squared 0.821632072648331 F-TEST (value) 21.9059196675472 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.99840144432528e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.15059599061323 Sum Squared Residuals 792.462565555554 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.29 95.6135000000002 3.67649999999981 2 98.69 94.7115 3.97850000000001 3 107.92 111.2575 -3.33749999999999 4 101.03 96.2535 4.77650000000004 5 97.55 99.2935 -1.74350000000002 6 103.02 108.1915 -5.1715 7 94.08 90.0455 4.0345 8 94.12 96.0995 -1.97949999999998 9 115.08 114.2015 0.878500000000005 10 116.48 111.3615 5.1185 11 103.42 108.8415 -5.4215 12 112.51 106.2975 6.21250000000002 13 95.55 99.7038333333333 -4.15383333333329 14 97.53 98.8018333333333 -1.27183333333334 15 119.26 115.347833333333 3.91216666666667 16 100.94 100.343833333333 0.596166666666658 17 97.73 103.383833333333 -5.65383333333332 18 115.25 112.281833333333 2.96816666666667 19 92.8 94.1358333333333 -1.33583333333333 20 99.2 100.189833333333 -0.98983333333333 21 118.69 118.291833333333 0.39816666666667 22 110.12 115.451833333333 -5.33183333333332 23 110.26 112.931833333333 -2.67183333333332 24 112.9 110.387833333333 2.51216666666667 25 102.17 100.750555555556 1.41944444444450 26 99.38 99.8485555555555 -0.468555555555560 27 116.1 116.394555555556 -0.294555555555559 28 103.77 101.390555555556 2.37944444444443 29 101.81 104.430555555556 -2.62055555555555 30 113.74 113.328555555556 0.411444444444442 31 89.67 95.1825555555556 -5.51255555555555 32 99.5 101.236555555556 -1.73655555555556 33 122.89 119.338555555556 3.55144444444445 34 108.61 116.498555555556 -7.88855555555555 35 114.37 113.978555555556 0.391444444444453 36 110.5 111.434555555556 -0.934555555555555 37 104.08 104.840888888889 -0.760888888888846 38 103.64 103.938888888889 -0.298888888888893 39 121.61 120.484888888889 1.12511111111111 40 101.14 105.480888888889 -4.3408888888889 41 115.97 108.520888888889 7.44911111111111 42 120.12 117.418888888889 2.70111111111111 43 95.97 99.2728888888889 -3.30288888888889 44 105.01 105.326888888889 -0.316888888888894 45 124.68 123.428888888889 1.25111111111112 46 123.89 120.588888888889 3.30111111111111 47 123.61 118.068888888889 5.5411111111111 48 114.76 115.524888888889 -0.764888888888888 49 108.75 108.931222222222 -0.181222222222181 50 106.09 108.029222222222 -1.93922222222223 51 123.17 124.575222222222 -1.40522222222223 52 106.16 109.571222222222 -3.41122222222224 53 115.18 112.611222222222 2.56877777777778 54 120.6 121.509222222222 -0.909222222222235 55 109.48 103.363222222222 6.11677777777777 56 114.44 109.417222222222 5.02277777777776 57 121.44 127.519222222222 -6.07922222222223 58 129.48 124.679222222222 4.80077777777776 59 124.32 122.159222222222 2.16077777777776 60 112.59 119.615222222222 -7.02522222222223 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.757627577155488 0.484744845689023 0.242372422844512 18 0.858460624314138 0.283078751371723 0.141539375685861 19 0.793852787478458 0.412294425043084 0.206147212521542 20 0.698803313461676 0.602393373076648 0.301196686538324 21 0.586433835227943 0.827132329544114 0.413566164772057 22 0.66221661748048 0.67556676503904 0.33778338251952 23 0.669003722745523 0.661992554508955 0.330996277254477 24 0.569844433264945 0.86031113347011 0.430155566735055 25 0.470014481690058 0.940028963380116 0.529985518309942 26 0.383763518947294 0.767527037894588 0.616236481052706 27 0.28835018722968 0.57670037445936 0.71164981277032 28 0.270564536601402 0.541129073202804 0.729435463398598 29 0.241440052463029 0.482880104926057 0.758559947536971 30 0.172314819804712 0.344629639609424 0.827685180195288 31 0.225277134050088 0.450554268100176 0.774722865949912 32 0.173303525686602 0.346607051373204 0.826696474313398 33 0.179973012324558 0.359946024649116 0.820026987675442 34 0.524109257385755 0.95178148522849 0.475890742614245 35 0.550147596039259 0.899704807921483 0.449852403960741 36 0.482700859672184 0.965401719344367 0.517299140327816 37 0.382602829466275 0.76520565893255 0.617397170533725 38 0.281046340884992 0.562092681769984 0.718953659115008 39 0.205401676145315 0.41080335229063 0.794598323854685 40 0.157146076844321 0.314292153688641 0.84285392315568 41 0.234601044696182 0.469202089392363 0.765398955303818 42 0.165518205132721 0.331036410265443 0.834481794867279 43 0.313963497940848 0.627926995881696 0.686036502059152 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/21/t1227269333mezf0ezroikfit6/10ld511227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/10ld511227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/13xll1227269247.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/13xll1227269247.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/2re201227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/2re201227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/3o32u1227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/3o32u1227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/4xehw1227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/4xehw1227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/5gn511227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/5gn511227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/687px1227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/687px1227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/733mk1227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/733mk1227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/8ocuo1227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/8ocuo1227269248.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/9jah81227269248.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227269333mezf0ezroikfit6/9jah81227269248.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