Ws 7 regressie analyse

*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 08:48:52 -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/t1258559876fe0uwm2mdjk73rm.htm/, Retrieved Wed, 18 Nov 2009 16:58:08 +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/t1258559876fe0uwm2mdjk73rm.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 «

1901 10436 1395 9314 1639 9717 1643 8997 1751 9062 1797 8885 1373 9058 1558 9095 1555 9149 2061 9857 2010 9848 2119 10269 1985 10341 1963 9690 2017 10125 1975 9349 1589 9224 1679 9224 1392 9454 1511 9347 1449 9430 1767 9933 1899 10148 2179 10677 2217 10735 2049 9760 2343 10567 2175 9333 1607 9409 1702 9502 1764 9348 1766 9319 1615 9594 1953 10160 2091 10182 2411 10810 2550 11105 2351 9874 2786 10958 2525 9311 2474 9610 2332 9398 1978 9784 1789 9425 1904 9557 1997 10166 2207 10337 2453 10770 1948 11265 1384 10183 1989 10941 2140 9628 2100 9709 2045 9637 2083 9579 2022 9741 1950 9754 1422 10508 1859 10749 2147 11079

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] = -694.538989926541 + 0.266716866856385invoer[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -694.538989926541 595.029013 -1.1672 0.247892 0.123946 invoer 0.266716866856385 0.060151 4.4341 4.2e-05 2.1e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.503160799709195 R-squared 0.253170790363997 Adjusted R-squared 0.240294424680617 F-TEST (value) 19.6616651459958 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 4.17635438003661e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 283.357979599723 Sum Squared Residuals 4656921.18696454 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1901 2088.9182325867 -187.918232586700 2 1395 1789.66190797383 -394.661907973832 3 1639 1897.14880531696 -258.148805316957 4 1643 1705.11266118036 -62.1126611803589 5 1751 1722.44925752602 28.5507424739760 6 1797 1675.24037209244 121.759627907556 7 1373 1721.3823900586 -348.382390058598 8 1558 1731.25091413228 -173.250914132285 9 1555 1745.65362494253 -190.653624942530 10 2061 1934.48916667685 126.510833323149 11 2010 1932.08871487514 77.9112851248568 12 2119 2044.37651582168 74.6234841783185 13 1985 2063.58013023534 -78.5801302353413 14 1963 1889.94744991183 73.0525500881658 15 2017 2005.96928699436 11.0307130056380 16 1975 1798.99699831381 176.003001686193 17 1589 1765.65738995676 -176.657389956758 18 1679 1765.65738995676 -86.6573899567585 19 1392 1827.00226933373 -435.002269333727 20 1511 1798.46356458009 -287.463564580094 21 1449 1820.60106452917 -371.601064529174 22 1767 1954.75964855794 -187.759648557936 23 1899 2012.10377493206 -113.103774932059 24 2179 2153.19699749909 25.8030025009131 25 2217 2168.66657577676 48.3334242232427 26 2049 1908.61763059178 140.382369408219 27 2343 2123.85814214488 219.141857855115 28 2175 1794.72952844410 380.270471555895 29 1607 1815.00001032519 -208.00001032519 30 1702 1839.80467894283 -137.804678942834 31 1764 1798.73028144695 -34.7302814469503 32 1766 1790.99549230812 -24.9954923081151 33 1615 1864.34263069362 -249.342630693621 34 1953 2015.30437733434 -62.3043773343355 35 2091 2021.17214840518 69.827851594824 36 2411 2188.67034079099 222.329659209014 37 2550 2267.35181651362 282.64818348638 38 2351 1939.02335341341 411.976646586591 39 2786 2228.14443708573 557.855562914269 40 2525 1788.86175737326 736.138242626736 41 2474 1868.61010056332 605.389899436677 42 2332 1812.06612478977 519.93387521023 43 1978 1915.01883539633 62.9811646036655 44 1789 1819.26748019489 -30.267480194892 45 1904 1854.47410661993 49.5258933800651 46 1997 2016.90467853547 -19.9046785354738 47 2207 2062.51326276792 144.486737232084 48 2453 2178.00166611673 274.998333883269 49 1948 2310.02651521064 -362.026515210642 50 1384 2021.43886527203 -637.438865272032 51 1989 2223.61025034917 -234.610250349173 52 2140 1873.41100416674 266.588995833262 53 2100 1895.01507038211 204.984929617894 54 2045 1875.81145596845 169.188544031554 55 2083 1860.34187769078 222.658122309225 56 2022 1903.55001012151 118.44998987849 57 1950 1907.01732939064 42.9826706093571 58 1422 2108.12184700036 -686.121847000358 59 1859 2172.40061191275 -313.400611912747 60 2147 2260.41717797535 -113.417177975354 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.238809182977102 0.477618365954204 0.761190817022898 6 0.192455140210349 0.384910280420697 0.807544859789651 7 0.209391052809708 0.418782105619416 0.790608947190292 8 0.124014260561188 0.248028521122376 0.875985739438812 9 0.0707616998206119 0.141523399641224 0.929238300179388 10 0.107702732492168 0.215405464984337 0.892297267507832 11 0.0912024487854103 0.182404897570821 0.90879755121459 12 0.06518110191935 0.130362203838700 0.93481889808065 13 0.0373231188351037 0.0746462376702073 0.962676881164896 14 0.0262192943458127 0.0524385886916255 0.973780705654187 15 0.0145122553442834 0.0290245106885668 0.985487744655717 16 0.0160497495080356 0.0320994990160712 0.983950250491964 17 0.0099724830312809 0.0199449660625618 0.99002751696872 18 0.00532189476589542 0.0106437895317908 0.994678105234105 19 0.0139850470421694 0.0279700940843389 0.98601495295783 20 0.0130927012470310 0.0261854024940620 0.98690729875297 21 0.0190542280607404 0.0381084561214808 0.98094577193926 22 0.0136514283798344 0.0273028567596688 0.986348571620166 23 0.0083288289477495 0.016657657895499 0.99167117105225 24 0.00479472601780487 0.00958945203560974 0.995205273982195 25 0.00270736245298319 0.00541472490596638 0.997292637547017 26 0.00238293779145240 0.00476587558290479 0.997617062208548 27 0.00216584022440876 0.00433168044881752 0.99783415977559 28 0.0083327968531255 0.016665593706251 0.991667203146875 29 0.00692901470587082 0.0138580294117416 0.993070985294129 30 0.00493571508280453 0.00987143016560907 0.995064284917195 31 0.00327454263484251 0.00654908526968503 0.996725457365157 32 0.00221770088552197 0.00443540177104395 0.997782299114478 33 0.00267769716561474 0.00535539433122947 0.997322302834385 34 0.00157973242842502 0.00315946485685004 0.998420267571575 35 0.000890550606507647 0.00178110121301529 0.999109449393492 36 0.000743808917635702 0.00148761783527140 0.999256191082364 37 0.000995365936907992 0.00199073187381598 0.999004634063092 38 0.00251765885021441 0.00503531770042881 0.997482341149786 39 0.0306243735042785 0.061248747008557 0.969375626495722 40 0.215278427687933 0.430556855375867 0.784721572312067 41 0.423061564235388 0.846123128470775 0.576938435764612 42 0.548663614891597 0.902672770216805 0.451336385108403 43 0.463434082502947 0.926868165005894 0.536565917497053 44 0.403607053705047 0.807214107410093 0.596392946294954 45 0.326159719155728 0.652319438311455 0.673840280844272 46 0.250996733912325 0.501993467824651 0.749003266087675 47 0.217111912418836 0.434223824837671 0.782888087581164 48 0.383491602928888 0.766983205857776 0.616508397071112 49 0.376864984426089 0.753729968852179 0.623135015573911 50 0.766851537062119 0.466296925875762 0.233148462937881 51 0.698892459390027 0.602215081219947 0.301107540609973 52 0.614480405661177 0.771039188677646 0.385519594338823 53 0.506783577931862 0.986432844136276 0.493216422068138 54 0.37366151556633 0.74732303113266 0.62633848443367 55 0.267102741863354 0.534205483726708 0.732897258136646 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.254901960784314 NOK 5% type I error level 24 0.470588235294118 NOK 10% type I error level 27 0.529411764705882 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/10bggb1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/10bggb1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/1d6nq1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/1d6nq1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/2rgji1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/2rgji1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/3dp891258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/3dp891258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/4j7fd1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/4j7fd1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/5hqhq1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/5hqhq1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/65fa01258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/65fa01258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/7v7gc1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/7v7gc1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/8herx1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/8herx1258559327.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/95snk1258559327.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559876fe0uwm2mdjk73rm/95snk1258559327.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by