Model 3

*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 17:26:03 -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/t125876327321ji6l23z7zkex9.htm/, Retrieved Sat, 21 Nov 2009 01:28:04 +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/t125876327321ji6l23z7zkex9.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 «

104.08 99.2 103.86 93.6 107.47 104.2 111.1 95.3 117.33 102.7 119.04 103.1 123.68 100 125.9 107.2 124.54 107 119.39 119 118.8 110.4 114.81 101.7 117.9 102.4 120.53 98.8 125.15 105.6 126.49 104.4 131.85 106.3 127.4 107.2 131.08 108.5 122.37 106.9 124.34 114.2 119.61 125.9 119.97 110.6 116.46 110.5 117.03 106.7 120.96 104.7 124.71 107.4 127.08 109.8 131.91 103.4 137.69 114.8 142.46 114.3 144.32 109.6 138.06 118.3 124.45 127.3 126.71 112.3 121.83 114.9 122.51 108.2 125.48 105.4 127.77 122.1 128.03 113.5 132.84 110 133.41 125.3 139.99 114.3 138.53 115.6 136.12 127.1 124.75 123 122.88 122.2 121.46 126.4 118.4 112.7 122.45 105.8 128.94 120.9 133.25 116.3 137.94 115.7 140.04 127.9 130.74 108.3 131.55 121.1 129.47 128.6 125.45 123.1 127.87 127.7 124.68 126.6

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 85.7644343849996 + 0.234218267557403X[t] + 0.631597084370433M1[t] + 4.09069715906612M2[t] + 5.61957925812608M3[t] + 8.78867933282185M4[t] + 13.8369594333415M5[t] + 12.9039122784857M6[t] + 16.3271361953193M7[t] + 14.3765491089529M8[t] + 10.5264576830592M9[t] + 1.47643700324979M10[t] + 3.44471695780859M11[t] + 0.191932283694180t + 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) 85.7644343849996 19.643654 4.366 7.1e-05 3.6e-05 X 0.234218267557403 0.187796 1.2472 0.218639 0.10932 M1 0.631597084370433 3.416654 0.1849 0.854153 0.427076 M2 4.09069715906612 3.795732 1.0777 0.286783 0.143391 M3 5.61957925812608 3.193311 1.7598 0.085091 0.042546 M4 8.78867933282185 3.343361 2.6287 0.011613 0.005807 M5 13.8369594333415 3.375053 4.0998 0.000167 8.3e-05 M6 12.9039122784857 3.194649 4.0392 0.000202 0.000101 M7 16.3271361953193 3.324165 4.9117 1.2e-05 6e-06 M8 14.3765491089529 3.210468 4.478 4.9e-05 2.5e-05 M9 10.5264576830592 3.262372 3.2266 0.00231 0.001155 M10 1.47643700324979 3.535892 0.4176 0.678214 0.339107 M11 3.44471695780859 3.175797 1.0847 0.283717 0.141858 t 0.191932283694180 0.07491 2.5622 0.013742 0.006871 Multiple Linear Regression - Regression Statistics Multiple R 0.859456865028675 R-squared 0.738666102844917 Adjusted R-squared 0.66481087104022 F-TEST (value) 10.0015406464128 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.77725079097968e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.01286005806824 Sum Squared Residuals 1155.92323424169 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.08 109.822415894758 -5.7424158947582 2 103.86 112.161825954827 -8.301825954827 3 107.47 116.365353973690 -8.89535397368965 4 111.1 117.641843750819 -6.5418437508187 5 117.33 124.615271314957 -7.28527131495727 6 119.04 123.967843750819 -4.92784375081868 7 123.68 126.856923321918 -3.17692332191848 8 125.9 126.784640045660 -0.884640045659552 9 124.54 123.079637249949 1.46036275005146 10 119.39 117.032168064522 2.35783193547781 11 118.8 117.178103201781 1.62189679821851 12 114.81 111.887619599918 2.92238040008234 13 117.9 112.875101755272 5.02489824472755 14 120.53 115.682948350456 4.84705164954433 15 125.15 118.99644695260 6.15355304739985 16 126.49 122.076417389921 4.41358261007877 17 131.85 127.761644482494 4.08835551750593 18 127.4 127.231326052134 0.168673947865794 19 131.08 131.150966000487 -0.0709660004865453 20 122.37 129.017561969722 -6.6475619697225 21 124.34 127.069196180692 -2.72919618069201 22 119.61 120.951461514998 -1.34146151499843 23 119.97 119.528134259623 0.441865740376879 24 116.46 116.251927758753 0.208072241247019 25 117.03 116.185427710099 0.84457228990055 26 120.96 119.368023533375 1.59197646662548 27 124.71 121.721227238534 2.98877276146635 28 127.08 125.644383439061 1.43561656093864 29 131.91 129.385598910908 2.52440108909225 30 137.69 131.314572289901 6.37542771009937 31 142.46 134.812619356650 7.64738064335036 32 144.32 131.953138696458 12.3668613035424 33 138.06 130.332678482008 7.72732151799248 34 124.45 123.582554493909 0.867445506091054 35 126.71 122.229492718801 4.48050728119913 36 121.83 119.585675540336 2.24432445966429 37 122.51 118.839942515766 3.67005748423429 38 125.48 121.835163724995 3.64483627500515 39 127.77 127.467423175958 0.302576824042367 40 128.03 128.814178433354 -0.78417843335391 41 132.84 133.234626881117 -0.39462688111677 42 133.41 136.077051503584 -2.66705150358353 43 139.99 137.115806760980 2.87419323902021 44 138.53 135.661635706132 2.86836429386778 45 136.12 134.696986640843 1.42301335915717 46 124.75 124.878603347742 -0.128603347742271 47 122.88 126.851440971949 -3.97144097194932 48 121.46 124.582373021576 -3.12237302157601 49 118.4 122.197112124104 -3.79711212410418 50 122.45 124.232038436348 -1.78203843634797 51 128.94 129.489548659219 -0.549548659218909 52 133.25 131.773176986845 1.47682301315520 53 137.94 136.872858410524 1.06714158947587 54 140.04 138.989206403563 1.05079359643705 55 130.74 138.013684559966 -7.27368455996554 56 131.55 139.253023582028 -7.70302358202809 57 129.47 137.351501446509 -7.8815014465091 58 125.45 127.205212578828 -1.75521257882816 59 127.87 130.442828847845 -2.57282884784519 60 124.68 126.932404079418 -2.25240407941764 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0286176913528057 0.0572353827056114 0.971382308647194 18 0.143652281831704 0.287304563663407 0.856347718168296 19 0.154146999046656 0.308293998093312 0.845853000953344 20 0.79898903476068 0.402021930478642 0.201010965239321 21 0.925493293735853 0.149013412528293 0.0745067062641466 22 0.94845883218564 0.103082335628721 0.0515411678143605 23 0.944806970949244 0.110386058101512 0.0551930290507562 24 0.948935109565474 0.102129780869051 0.0510648904345256 25 0.93820165698407 0.123596686031858 0.0617983430159292 26 0.917942369423405 0.164115261153189 0.0820576305765947 27 0.881916710524679 0.236166578950642 0.118083289475321 28 0.857638413040286 0.284723173919427 0.142361586959714 29 0.825905031802106 0.348189936395787 0.174094968197894 30 0.770787783438673 0.458424433122654 0.229212216561327 31 0.742978337415059 0.514043325169882 0.257021662584941 32 0.872090426759332 0.255819146481336 0.127909573240668 33 0.862933018416606 0.274133963166788 0.137066981583394 34 0.838277266284344 0.323445467431313 0.161722733715656 35 0.80097266652139 0.398054666957221 0.199027333478610 36 0.751975877310892 0.496048245378216 0.248024122689108 37 0.740974983089632 0.518050033820736 0.259025016910368 38 0.654380516014774 0.691238967970452 0.345619483985226 39 0.586117176435559 0.827765647128882 0.413882823564441 40 0.540122091502696 0.919755816994608 0.459877908497304 41 0.447931329798714 0.895862659597428 0.552068670201286 42 0.484588646596725 0.96917729319345 0.515411353403275 43 0.42447662042479 0.84895324084958 0.57552337957521 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 1 0.0370370370370370 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/10hsn81258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/10hsn81258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/1kqid1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/1kqid1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/2mzab1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/2mzab1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/34hrs1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/34hrs1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/4xtv11258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/4xtv11258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/5q9871258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/5q9871258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/69zsu1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/69zsu1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/7azhi1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/7azhi1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/80tjm1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/80tjm1258763158.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/917mb1258763158.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876327321ji6l23z7zkex9/917mb1258763158.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