WS7

*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, 20 Nov 2009 15:38:21 -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/21/t12587580664nu6efu9qtsgifp.htm/, Retrieved Sat, 21 Nov 2009 00:01:18 +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/21/t12587580664nu6efu9qtsgifp.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 «

10414.9 10723.8 12476.8 13938.9 12384.6 13979.8 12266.7 13807.4 12919.9 12973.9 11497.3 12509.8 12142 12934.1 13919.4 14908.3 12656.8 13772.1 12034.1 13012.6 13199.7 14049.9 10881.3 11816.5 11301.2 11593.2 13643.9 14466.2 12517 13615.9 13981.1 14733.9 14275.7 13880.7 13435 13527.5 13565.7 13584 16216.3 16170.2 12970 13260.6 14079.9 14741.9 14235 15486.5 12213.4 13154.5 12581 12621.2 14130.4 15031.6 14210.8 15452.4 14378.5 15428 13142.8 13105.9 13714.7 14716.8 13621.9 14180 15379.8 16202.2 13306.3 14392.4 14391.2 15140.6 14909.9 15960.1 14025.4 14351.3 12951.2 13230.2 14344.3 15202.1 16093.4 17056 15413.6 16077.7 14705.7 13348.2 15972.8 16402.4 16241.4 16559.1 16626.4 16579 17136.2 17561.2 15622.9 16129.6 18003.9 18484.3 16136.1 16402.6 14423.7 14032.3 16789.4 17109.1 16782.2 17157.2 14133.8 13879.8 12607 12362.4 12004.5 12683.5 12175.4 12608.8 13268 13583.7 12299.3 12846.3 11800.6 12347.1 13873.3 13967 12269.6 13114.3

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 InIEU[t] = -113.758343866225 + 0.9693392397551UitIEU[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) -113.758343866225 618.458406 -0.1839 0.854703 0.427352 UitIEU 0.9693392397551 0.042779 22.659 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.94789261083603 R-squared 0.898500401677547 Adjusted R-squared 0.896750408603022 F-TEST (value) 513.430832817097 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 536.973566743385 Sum Squared Residuals 16723755.4601045 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10414.9 10281.2417954196 133.658204580408 2 12476.8 13397.7643851561 -920.964385156146 3 12384.6 13437.4103600621 -1052.81036006213 4 12266.7 13270.2962751284 -1003.59627512835 5 12919.9 12462.3520187925 457.547981207523 6 11497.3 12012.4816776221 -515.181677622135 7 12142 12423.7723170502 -281.772317050225 8 13919.4 14337.4418441747 -418.041844174743 9 12656.8 13236.078599965 -579.278599964999 10 12034.1 12499.865447371 -465.765447370999 11 13199.7 13505.3610407690 -305.661040768964 12 10881.3 11340.4387826999 -459.138782699925 13 11301.2 11123.9853304626 177.214669537390 14 13643.9 13908.8969662790 -264.996966279015 15 12517 13084.6678107153 -567.667810715251 16 13981.1 14168.3890807615 -187.289080761453 17 14275.7 13341.3488414024 934.351158597598 18 13435 12998.9782219209 436.021778079099 19 13565.7 13053.7458889671 511.954111032937 20 16216.3 15560.6510308217 655.648969178294 21 12970 12740.2615788303 229.738421169735 22 14079.9 14176.1437946795 -96.2437946794948 23 14235 14897.9137926011 -662.913792601142 24 12213.4 12637.4146854922 -424.014685492249 25 12581 12120.4660689309 460.533931069146 26 14130.4 14456.9613724365 -326.561372436548 27 14210.8 14864.8593245255 -654.059324525494 28 14378.5 14841.2074470755 -462.707447075469 29 13142.8 12590.3047984401 552.495201559849 30 13714.7 14151.8133797616 -437.11337976164 31 13621.9 13631.4720758611 -9.57207586110413 32 15379.8 15591.6698864939 -211.869886493869 33 13306.3 13837.3597303851 -531.059730385087 34 14391.2 14562.6193495699 -171.419349569853 35 14909.9 15356.9928565492 -447.092856549159 36 14025.4 13797.5198876312 227.880112368848 37 12951.2 12710.7936659417 240.406334058291 38 14344.3 14622.2337128148 -277.933712814793 39 16093.4 16419.2917293968 -325.891729396773 40 15413.6 15470.9871511444 -57.3871511443583 41 14705.7 12825.1756962328 1880.52430376719 42 15972.8 15785.7316022928 187.068397707159 43 16241.4 15937.6270611625 303.772938837538 44 16626.4 15956.9169120336 669.483087966412 45 17136.2 16909.0019133211 227.198086678951 46 15622.9 15521.2958576876 101.604142312352 47 18003.9 17803.798965539 200.101034461019 48 16136.1 15785.9254701408 350.174529859212 49 14423.7 13488.3006701493 935.399329850726 50 16789.4 16470.7636430278 318.636356972234 51 16782.2 16517.38886046 264.811139540011 52 14133.8 13340.4764360866 793.323563913377 53 12607 11869.6010736822 737.398926317767 54 12004.5 12180.8559035676 -176.355903567596 55 12175.4 12108.4462623579 66.9537376421105 56 13268 13053.4550871951 214.544912804862 57 12299.3 12338.6643317997 -39.3643317997262 58 11800.6 11854.7701833140 -54.1701833139801 59 13873.3 13425.0028177933 448.297182206732 60 12269.6 12598.4472480541 -328.847248054092 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.725619795766844 0.548760408466311 0.274380204233156 6 0.611285408661648 0.777429182676705 0.388714591338352 7 0.484061576006891 0.968123152013783 0.515938423993109 8 0.514300508296248 0.971398983407505 0.485699491703752 9 0.412487315979948 0.824974631959895 0.587512684020052 10 0.320098382758765 0.640196765517531 0.679901617241235 11 0.274113189410236 0.548226378820472 0.725886810589764 12 0.24292350012674 0.48584700025348 0.75707649987326 13 0.193361661232874 0.386723322465748 0.806638338767126 14 0.179520078934425 0.359040157868851 0.820479921065575 15 0.146483087503431 0.292966175006861 0.85351691249657 16 0.147428808743692 0.294857617487385 0.852571191256308 17 0.601723562530558 0.796552874938884 0.398276437469442 18 0.646909329281903 0.706181341436193 0.353090670718097 19 0.695879674857288 0.608240650285424 0.304120325142712 20 0.799163224981118 0.401673550037763 0.200836775018882 21 0.760020535324044 0.479958929351913 0.239979464675956 22 0.697928419505991 0.604143160988019 0.302071580494009 23 0.71413335488312 0.57173329023376 0.28586664511688 24 0.695270686963923 0.609458626072155 0.304729313036077 25 0.683506223331356 0.632987553337289 0.316493776668644 26 0.637319221972827 0.725361556054347 0.362680778027173 27 0.6605756779786 0.6788486440428 0.3394243220214 28 0.641870395169956 0.716259209660088 0.358129604830044 29 0.648706173209859 0.702587653580283 0.351293826790141 30 0.636115006654915 0.727769986690169 0.363884993345084 31 0.575499105297203 0.849001789405595 0.424500894702797 32 0.5236354630181 0.9527290739638 0.4763645369819 33 0.558805627107412 0.882388745785175 0.441194372892588 34 0.512700952746699 0.974598094506602 0.487299047253301 35 0.522603321208739 0.954793357582522 0.477396678791261 36 0.471244505677097 0.942489011354195 0.528755494322903 37 0.41179395508802 0.82358791017604 0.58820604491198 38 0.396513365457171 0.793026730914342 0.603486634542829 39 0.393814202278176 0.787628404556353 0.606185797721824 40 0.356448601713431 0.712897203426862 0.643551398286569 41 0.985451977501068 0.0290960449978640 0.0145480224989320 42 0.977818501931264 0.0443629961374713 0.0221814980687357 43 0.966351635197022 0.0672967296059552 0.0336483648029776 44 0.967109385769897 0.0657812284602058 0.0328906142301029 45 0.947806025666428 0.104387948667144 0.0521939743335719 46 0.922431563159438 0.155136873681125 0.0775684368405624 47 0.891051314436481 0.217897371127038 0.108948685563519 48 0.838974579007587 0.322050841984827 0.161025420992413 49 0.914919511285753 0.170160977428494 0.0850804887142469 50 0.862123817076009 0.275752365847982 0.137876182923991 51 0.827361955741803 0.345276088516393 0.172638044258196 52 0.848635143010894 0.302729713978212 0.151364856989106 53 0.983165925067538 0.0336681498649244 0.0168340749324622 54 0.955477740254552 0.0890445194908954 0.0445222597454477 55 0.90177663510465 0.196446729790700 0.0982233648953501 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 3 0.0588235294117647 NOK 10% type I error level 6 0.117647058823529 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/10ezi01258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/10ezi01258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/1wwhq1258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/1wwhq1258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/2lp8o1258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/2lp8o1258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/3xo811258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/3xo811258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/4ut881258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/4ut881258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/5ll2m1258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/5ll2m1258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/6ify31258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/6ify31258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/7qeep1258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/7qeep1258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/8lxd71258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/8lxd71258756696.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/915cd1258756696.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587580664nu6efu9qtsgifp/915cd1258756696.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