Invoer X crisis

*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: Thu, 25 Nov 2010 08:22:21 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p.htm/, Retrieved Thu, 25 Nov 2010 09:21:57 +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/2010/Nov/25/t12906733177weelrhx085744p.htm/}, year = {2010}, } @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 = {2010}, 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 «

16198.9 16896.2 0 0 16554.2 16698.00 0 0 19554.2 19691.6 0 0 15903.8 15930.7 0 0 18003.8 17444.6 0 0 18329.6 17699.4 0 0 16260.7 15189.8 0 0 14851.9 15672.7 0 0 18174.1 17180.8 0 0 18406.6 17664.9 0 0 18466.5 17862.9 0 0 16016.5 16162.3 0 0 17428.5 17463.6 0 0 17167.2 16772.1 0 0 19630.00 19106.9 0 0 17183.6 16721.3 0 0 18344.7 18161.3 0 0 19301.4 18509.9 0 0 18147.5 17802.7 0 0 16192.9 16409.9 0 0 18374.4 17967.7 0 0 20515.2 20286.6 0 0 18957.2 19537.3 0 0 16471.5 18021.9 0 0 18746.8 20194.3 0 0 19009.5 19049.6 0 0 19211.2 20244.7 0 0 20547.7 21473.3 0 0 19325.8 19673.6 0 0 20605.5 21053.2 0 0 20056.9 20159.5 0 0 16141.4 18203.6 0 0 20359.8 21289.5 0 0 19711.6 20432.3 1 20432.3 15638.6 17180.4 1 17180.4 14384.5 15816.8 1 15816.8 13855.6 15071.8 1 15071.8 14308.3 14521.1 1 14521.1 15290.6 15668.8 1 15668.8 14423.8 14346.9 1 14346.9 13779.7 13881.00 1 13881.00 15686.3 15465.9 1 15465.9 14733.8 14238.2 1 14238.2 12522.5 13557.7 1 13557.7 16189.4 16127.6 1 16127.6 160 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation uitvoer[t] = + 2114.23967542119 + 0.876300281407902invoer[t] -674.860763094272crisis[t] + 5.89353497229609e-05invoerXcrisis[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) 2114.23967542119 1216.861087 1.7375 0.088343 0.044172 invoer 0.876300281407902 0.066888 13.101 0 0 crisis -674.860763094272 2086.161701 -0.3235 0.747644 0.373822 invoerXcrisis 5.89353497229609e-05 0.126285 5e-04 0.999629 0.499815 Multiple Linear Regression - Regression Statistics Multiple R 0.943748600071709 R-squared 0.89066142013731 Adjusted R-squared 0.884229738968916 F-TEST (value) 138.480343912908 F-TEST (DF numerator) 3 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 686.987281817376 Sum Squared Residuals 24069527.7943201 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16198.9 16920.3844901454 -721.48449014536 2 16554.2 16746.7017743703 -192.501774370325 3 19554.2 19369.994296793 184.205703206981 4 15903.8 16074.3165684460 -170.516568446044 5 18003.8 17400.9475644695 602.852435530535 6 18329.6 17624.2288761722 705.371123827798 7 16260.7 15425.0656899509 835.634310049072 8 14851.9 15848.2310958428 -996.331095842806 9 18174.1 17169.7795502341 1004.32044976594 10 18406.6 17593.9965164636 812.603483536371 11 18466.5 17767.5039721824 698.996027817608 12 16016.5 16277.2677136201 -260.767713620112 13 17428.5 17417.5972698162 10.9027301837855 14 17167.2 16811.6356252227 355.56437477735 15 19630 18857.6215222538 772.378477746178 16 17183.6 16767.1195709271 416.480429072869 17 18344.7 18028.9919761545 315.708023845493 18 19301.4 18334.4702542533 966.929745746697 19 18147.5 17714.7506952416 432.749304758364 20 16192.9 16494.2396632967 -301.339663296711 21 18374.4 17859.3402416739 515.059758326062 22 20515.2 19891.3929642307 623.807035769279 23 18957.2 19234.7811633718 -277.58116337178 24 16471.5 17906.8357169262 -1435.33571692625 25 18746.8 19810.5104482568 -1063.71044825677 26 19009.5 18807.4095161291 202.090483870853 27 19211.2 19854.6759824397 -643.475982439732 28 20547.7 20931.2985081775 -383.598508177478 29 19325.8 19354.2208917277 -28.4208917276783 30 20605.5 20563.1647599580 42.335240041979 31 20056.9 19780.0151984638 276.884801536223 32 16141.4 18066.0594780581 -1924.65947805806 33 20359.8 20770.2345164547 -410.434516454708 34 19711.6 19345.4133368837 366.186663116273 35 15638.6 16495.5807999096 -856.980799909607 36 14384.5 15300.5773719389 -916.077371938909 37 13855.6 14647.6897554545 -792.089755454478 38 14308.3 14165.0787347861 143.221265213944 39 15290.6 15170.8762078588 119.723792141221 40 14423.8 14012.4169592269 411.383040773123 41 13779.7 13604.1212001395 175.578799860502 42 15686.3 14993.0629227787 693.237077221341 43 14733.8 13917.1567123653 816.643287634676 44 12522.5 13320.7942653618 -798.29426536176 45 16189.4 15572.9498165072 616.450183492821 46 16059.1 16156.8679626328 -97.7679626327849 47 16007.1 15473.3954094835 533.704590516488 48 15806.8 16221.7185446728 -414.91854467285 49 15160 16147.8381620562 -987.838162056166 50 15692.1 16028.6613237847 -336.561323784691 51 18908.9 18470.2967978715 438.603202128472 52 16969.9 17854.7834802106 -884.883480210616 53 16997.5 17302.8895629799 -305.389562979922 54 19858.9 19238.9874047225 619.912595277463 55 17681.2 17189.7591966502 491.440803349841 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.719152152389884 0.561695695220232 0.280847847610116 8 0.797064041291364 0.405871917417272 0.202935958708636 9 0.823184531640502 0.353630936718996 0.176815468359498 10 0.789348897745006 0.421302204509988 0.210651102254994 11 0.730445105870134 0.539109788259731 0.269554894129865 12 0.647558646709221 0.704882706581558 0.352441353290779 13 0.553893470507653 0.892213058984695 0.446106529492347 14 0.465460482157156 0.930920964314311 0.534539517842844 15 0.403853118189793 0.807706236379586 0.596146881810207 16 0.337810908500610 0.675621817001221 0.66218909149939 17 0.266452379068343 0.532904758136687 0.733547620931656 18 0.277963648792925 0.555927297585851 0.722036351207075 19 0.229515824216388 0.459031648432776 0.770484175783612 20 0.185850007983003 0.371700015966006 0.814149992016997 21 0.160656462056801 0.321312924113602 0.839343537943199 22 0.144000269074472 0.288000538148944 0.855999730925528 23 0.173862325511807 0.347724651023614 0.826137674488193 24 0.505168778199442 0.989662443601116 0.494831221800558 25 0.67302253888956 0.653954922220879 0.326977461110440 26 0.611732677441224 0.776534645117552 0.388267322558776 27 0.603802046457534 0.792395907084932 0.396197953542466 28 0.551062807633029 0.897874384733942 0.448937192366971 29 0.468511415693534 0.937022831387068 0.531488584306466 30 0.386295232123468 0.772590464246936 0.613704767876532 31 0.325336162491117 0.650672324982234 0.674663837508883 32 0.772703043231794 0.454593913536412 0.227296956768206 33 0.790057317935213 0.419885364129573 0.209942682064787 34 0.757699235736977 0.484601528526046 0.242300764263023 35 0.748754480283738 0.502491039432523 0.251245519716262 36 0.799845563575853 0.400308872848295 0.200154436424147 37 0.849242982906036 0.301514034187927 0.150757017093964 38 0.820443700532786 0.359112598934427 0.179556299467214 39 0.761368051106152 0.477263897787697 0.238631948893848 40 0.709095532309888 0.581808935380224 0.290904467690112 41 0.620482845698563 0.759034308602874 0.379517154301437 42 0.596641732853054 0.806716534293893 0.403358267146946 43 0.696609435644852 0.606781128710295 0.303390564355148 44 0.846375322976956 0.307249354046089 0.153624677023044 45 0.766241482353578 0.467517035292843 0.233758517646422 46 0.652135129740728 0.695729740518545 0.347864870259272 47 0.507586464077593 0.984827071844813 0.492413535922406 48 0.345192898771131 0.690385797542262 0.654807101228869 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://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/10nxt11290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/10nxt11290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/1zxwq1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/1zxwq1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/2zxwq1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/2zxwq1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/3rova1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/3rova1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/4rova1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/4rova1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/5rova1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/5rova1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/6kfuv1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/6kfuv1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/7d6by1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/7d6by1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/8d6by1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/8d6by1290673332.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/9d6by1290673332.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t12906733177weelrhx085744p/9d6by1290673332.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