Ws 7 regressie analyse 2laatste maanden

*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, 18 Nov 2009 09:31:58 -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/18/t1258562002kaarbsh5v8xc2v7.htm/, Retrieved Wed, 18 Nov 2009 17:33:34 +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/18/t1258562002kaarbsh5v8xc2v7.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 «

1643 8997 1639 1395 1751 9062 1643 1639 1797 8885 1751 1643 1373 9058 1797 1751 1558 9095 1373 1797 1555 9149 1558 1373 2061 9857 1555 1558 2010 9848 2061 1555 2119 10269 2010 2061 1985 10341 2119 2010 1963 9690 1985 2119 2017 10125 1963 1985 1975 9349 2017 1963 1589 9224 1975 2017 1679 9224 1589 1975 1392 9454 1679 1589 1511 9347 1392 1679 1449 9430 1511 1392 1767 9933 1449 1511 1899 10148 1767 1449 2179 10677 1899 1767 2217 10735 2179 1899 2049 9760 2217 2179 2343 10567 2049 2217 2175 9333 2343 2049 1607 9409 2175 2343 1702 9502 1607 2175 1764 9348 1702 1607 1766 9319 1764 1702 1615 9594 1766 1764 1953 10160 1615 1766 2091 10182 1953 1615 2411 10810 2091 1953 2550 11105 2411 2091 2351 9874 2550 2411 2786 10958 2351 2550 2525 9311 2786 2351 2474 9610 2525 2786 2332 9398 2474 2525 1978 9784 2332 2474 1789 9425 1978 2332 1904 9557 1789 1978 1997 10166 1904 1789 2207 10337 1997 1904 2453 10770 2207 1997 1948 11265 2453 2207 1384 10183 1948 2453 1989 10941 1384 1948 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation aanbod[t] = + 617.674552839261 + 0.0404402210843701invoer[t] + 0.710157341244884`y(t-1)`[t] -0.0773485495535648`y(t-2)`[t] -315.540905496075M1[t] -436.959058722751M2[t] -299.931399291189M3[t] -517.866187168882M4[t] -367.629587341215M5[t] -430.560458513548M6[t] -286.062967850213M7[t] -224.916251638326M8[t] -107.946933806307M9[t] -376.257868939131M10[t] -475.138020364354M11[t] + 0.943798257753726t + 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) 617.674552839261 2726.586818 0.2265 0.821909 0.410954 invoer 0.0404402210843701 0.262938 0.1538 0.878521 0.43926 `y(t-1)` 0.710157341244884 0.165066 4.3023 0.000102 5.1e-05 `y(t-2)` -0.0773485495535648 0.160768 -0.4811 0.63299 0.316495 M1 -315.540905496075 341.592062 -0.9237 0.361029 0.180514 M2 -436.959058722751 333.404964 -1.3106 0.197289 0.098644 M3 -299.931399291189 361.535908 -0.8296 0.411567 0.205784 M4 -517.866187168882 331.986235 -1.5599 0.126469 0.063235 M5 -367.629587341215 359.605365 -1.0223 0.312627 0.156313 M6 -430.560458513548 331.870048 -1.2974 0.201754 0.100877 M7 -286.062967850213 202.444197 -1.413 0.165189 0.082595 M8 -224.916251638326 182.255332 -1.2341 0.224203 0.112102 M9 -107.946933806307 153.819601 -0.7018 0.486784 0.243392 M10 -376.257868939131 184.584485 -2.0384 0.047994 0.023997 M11 -475.138020364354 240.700152 -1.974 0.055147 0.027574 t 0.943798257753726 4.263194 0.2214 0.825893 0.412947 Multiple Linear Regression - Regression Statistics Multiple R 0.837502903368616 R-squared 0.701411113150861 Adjusted R-squared 0.592171276498738 F-TEST (value) 6.42083634182388 F-TEST (DF numerator) 15 F-TEST (DF denominator) 41 p-value 1.03794539541013e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 206.136457647118 Sum Squared Residuals 1742181.80602339 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1643 1722.96477037016 -79.9647703701586 2 1751 1589.08661304563 161.913386954369 3 1797 1796.28775025925 0.712249740753709 4 1373 1610.60651323238 -237.606513232383 5 1558 1458.61845353063 99.3815464693688 6 1555 1562.99004569562 -7.99004569562253 7 2061 1720.6230574533 340.376942546699 8 2010 2141.92127025175 -131.921270251755 9 2119 2201.50332894045 -82.5033289404535 10 1985 2018.39981420638 -33.3998142063823 11 1963 1790.54480148484 172.455198515165 12 2017 2278.95936041143 -261.959360411434 13 1975 1973.03080612904 1.96919387095642 14 1589 1813.49799351640 -224.497993516397 15 1679 1680.59735656644 -1.59735656643769 16 1392 1566.67831863562 -174.678318635619 17 1511 1502.75508666791 8.24491333208958 18 1449 1550.83230943335 -101.832309433348 19 1767 1663.38079700582 103.619202994182 20 1899 1964.79160359679 -65.7916035967924 21 2179 2173.24152692649 5.75847307351248 22 2217 2096.85396988181 120.146030118193 23 2049 1964.81678624939 84.1832137506149 24 2343 2351.28818507440 -8.2881850744033 25 2175 2208.56865966896 -33.5686596689639 26 1607 1949.12085460457 -342.120854604565 27 1702 1700.47843935263 1.52156064736768 28 1764 1588.65857925039 175.341420749611 29 1766 1775.34785387396 -9.34785387395748 30 1615 1721.10654636775 -106.106546367748 31 1953 1782.04854479551 170.951455204494 32 2091 2096.74155645236 -5.74155645236218 33 2411 2311.90903472581 99.0909652741918 34 2550 2273.04801243060 276.951987569403 35 2351 2199.29008168417 151.709918315833 36 2786 2567.13634066605 218.863659333945 37 2525 2510.24499410446 14.7550058955406 38 2474 2182.86458011905 291.135419880951 39 2332 2296.23265796847 35.7673420315308 40 1978 1997.95402725755 -19.9540272575546 41 1789 1894.20418120960 -105.204181209605 42 1904 1730.71686652484 173.283133475159 43 1997 1997.07322019510 -0.0732201950963134 44 2207 2123.22856200728 83.771437992721 45 2453 2400.59192037953 52.4080796204722 46 1948 2311.69820348121 -363.698203481213 47 1384 1792.34833058161 -408.348330581613 48 1989 1937.61611384811 51.3838861518918 49 2140 2043.19076972737 96.8092302726257 50 2100 1986.42995871436 113.570041285643 51 2045 2081.40379585321 -36.4037958532144 52 2083 1826.10256162405 256.897438375945 53 2022 2015.07442471790 6.92557528210368 54 1950 1907.35423197844 42.6457680215599 55 1422 2036.87438055028 -614.87438055028 56 1859 1739.31700769181 119.682992308188 57 2147 2221.75418902772 -74.754189027723 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0565705249168277 0.113141049833655 0.943429475083172 20 0.0195890138288041 0.0391780276576082 0.980410986171196 21 0.00520366919487076 0.0104073383897415 0.99479633080513 22 0.00187726410208006 0.00375452820416013 0.99812273589792 23 0.00375666304374266 0.00751332608748531 0.996243336956257 24 0.00228057792597236 0.00456115585194473 0.997719422074028 25 0.00484807458406905 0.0096961491681381 0.99515192541593 26 0.0134908570307777 0.0269817140615554 0.986509142969222 27 0.00625721885149454 0.0125144377029891 0.993742781148505 28 0.0852496727107245 0.170499345421449 0.914750327289276 29 0.0535923759402655 0.107184751880531 0.946407624059735 30 0.0550186877984614 0.110037375596923 0.944981312201539 31 0.0388147160502103 0.0776294321004207 0.96118528394979 32 0.0910732798310168 0.182146559662034 0.908926720168983 33 0.059472449420021 0.118944898840042 0.94052755057998 34 0.0954474973829158 0.190894994765832 0.904552502617084 35 0.0964601311275386 0.192920262255077 0.903539868872461 36 0.136323391820261 0.272646783640522 0.86367660817974 37 0.0844450441021759 0.168890088204352 0.915554955897824 38 0.169334097177306 0.338668194354613 0.830665902822694 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.2 NOK 5% type I error level 8 0.4 NOK 10% type I error level 9 0.45 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/10qxyi1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/10qxyi1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/1japm1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/1japm1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/2d1sq1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/2d1sq1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/3tks11258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/3tks11258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/402th1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/402th1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/5prox1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/5prox1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/67d4y1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/67d4y1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/7j5c81258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/7j5c81258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/8w5gw1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/8w5gw1258561913.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/9pkwl1258561913.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258562002kaarbsh5v8xc2v7/9pkwl1258561913.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