model 5

*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, 19 Nov 2009 01:51:35 -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/19/t1258620989z144dgiv07t546s.htm/, Retrieved Thu, 19 Nov 2009 09:56:41 +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/19/t1258620989z144dgiv07t546s.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 «

135 0 139 149 130 0 135 139 127 0 130 135 122 0 127 130 117 0 122 127 112 0 117 122 113 0 112 117 149 0 113 112 157 0 149 113 157 0 157 149 147 0 157 157 137 0 147 157 132 0 137 147 125 0 132 137 123 0 125 132 117 0 123 125 114 0 117 123 111 0 114 117 112 0 111 114 144 0 112 111 150 0 144 112 149 0 150 144 134 0 149 150 123 0 134 149 116 0 123 134 117 0 116 123 111 0 117 116 105 0 111 117 102 0 105 111 95 0 102 105 93 0 95 102 124 0 93 95 130 0 124 93 124 0 130 124 115 0 124 130 106 0 115 124 105 0 106 115 105 0 105 106 101 0 105 105 95 0 101 105 93 0 95 101 84 0 93 95 87 0 84 93 116 0 87 84 120 0 116 87 117 1 120 116 109 1 117 120 105 1 109 117 107 1 105 109 109 1 107 105 109 1 109 107 108 1 109 109 107 1 108 109 99 1 107 108 103 1 99 107 131 1 103 99 137 1 131 103 135 1 137 131

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 WLH[t] = + 12.3068296466899 + 4.33014916145824X[t] + 1.01868507239736`Y(t-1)`[t] -0.148970908506937`Y(t-2)`[t] + 4.26344451199578M1[t] + 4.33777525395675M2[t] + 2.85371517838113M3[t] + 0.970842279890648M4[t] + 2.74283742150709M5[t] -1.39068521698368M6[t] + 6.24100022216994M7[t] + 35.1906468259991M8[t] + 9.74545135874112M9[t] + 5.14442295711167M10[t] -1.95298550853488M11[t] -0.129219519629923t + 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) 12.3068296466899 8.19304 1.5021 0.140551 0.070276 X 4.33014916145824 1.340706 3.2298 0.002408 0.001204 `Y(t-1)` 1.01868507239736 0.144455 7.0519 0 0 `Y(t-2)` -0.148970908506937 0.14104 -1.0562 0.296899 0.148449 M1 4.26344451199578 1.681337 2.5357 0.01503 0.007515 M2 4.33777525395675 2.028613 2.1383 0.038355 0.019177 M3 2.85371517838113 2.144412 1.3308 0.190445 0.095223 M4 0.970842279890648 2.08789 0.465 0.644342 0.322171 M5 2.74283742150709 2.052061 1.3366 0.188541 0.09427 M6 -1.39068521698368 2.245373 -0.6194 0.539027 0.269514 M7 6.24100022216994 2.184915 2.8564 0.006635 0.003317 M8 35.1906468259991 2.704291 13.0129 0 0 M9 9.74545135874112 6.079908 1.6029 0.116453 0.058227 M10 5.14442295711167 2.814623 1.8277 0.074697 0.037349 M11 -1.95298550853488 2.059561 -0.9483 0.348425 0.174213 t -0.129219519629923 0.051414 -2.5133 0.015884 0.007942 Multiple Linear Regression - Regression Statistics Multiple R 0.993002779799179 R-squared 0.986054520688897 Adjusted R-squared 0.981073992363503 F-TEST (value) 197.98191201149 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.38810920845841 Sum Squared Residuals 239.528754844003 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 135 135.841614334755 -0.841614334755449 2 130 133.201694352566 -3.20169435256636 3 127 127.090873029402 -0.0908730294017102 4 122 122.767579936624 -0.767579936623912 5 117 119.763842922144 -2.76384292214445 6 112 111.152529944572 0.847470055428355 7 113 114.306425044643 -1.30642504464321 8 149 144.890391743775 4.10960825622543 9 157 155.839668454685 1.16033154531534 10 157 153.895948406354 3.10405159364556 11 147 145.477553153022 1.52244684697752 12 137 137.114468417954 -0.114468417953803 13 132 132.551551771415 -0.551551771415443 14 125 128.892946716829 -3.89294671682906 15 123 120.893726157377 2.10627384262333 16 117 117.88705995401 -0.887059954010097 17 114 113.715666958626 0.284333041373675 18 111 107.290695034355 3.70930496564482 19 112 112.184018462208 -0.184018462207592 20 144 142.470043344325 1.52995665567495 21 150 149.344579765646 0.655420234354274 22 149 145.959373206549 3.04062679345147 23 134 136.820234697833 -2.82023469783308 24 123 123.512695509285 -0.512695509284553 25 116 118.675948332883 -2.6759483328835 26 117 113.128944042009 3.87105595799067 27 111 113.577145878750 -2.57714587874970 28 105 105.303972117738 -0.303972117738187 29 102 101.728462756382 0.271537243617835 30 95 95.303490832111 -0.303490832111012 31 93 96.122073970374 -3.12207397037399 32 124 123.947927269327 0.0520727306728794 33 130 130.250691343771 -0.250691343771236 34 124 127.014455693181 -3.01445569318098 35 115 112.781891822479 2.21810817752128 36 106 106.331317610849 -0.331317610849049 37 105 102.638115128201 2.36188487179890 38 105 102.905279454697 2.09472054530279 39 101 101.440970767999 -0.440970767998598 40 95 95.3541380602887 -0.354138060288751 41 93 91.4806868819188 1.51931311808115 42 84 86.074400030045 -2.07440003004506 43 87 84.7066421150064 2.29335788499362 44 116 117.923862592960 -1.92386259296019 45 120 121.444401980075 -1.44440198007489 46 117 120.798887163162 -3.79888716316202 47 109 109.920320326666 -0.92032032666572 48 105 104.041518461913 0.958481538087397 49 107 105.292770432745 1.70722956725549 50 109 107.871135433898 1.12886456610197 51 109 107.997284166473 1.00271583352667 52 108 105.687249931339 2.31275006866095 53 107 106.311340480928 0.688659519071789 54 99 101.178884158917 -2.1788841589171 55 103 100.680840407769 2.31915959223117 56 131 134.767775049613 -3.76777504961307 57 137 137.120658455823 -0.120658455823482 58 135 134.331335530754 0.668664469245966 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.192830144016746 0.385660288033493 0.807169855983254 20 0.140575807283262 0.281151614566524 0.859424192716738 21 0.0717162680200237 0.143432536040047 0.928283731979976 22 0.208851234881068 0.417702469762136 0.791148765118932 23 0.56289031844168 0.874219363116639 0.437109681558319 24 0.44876986491339 0.89753972982678 0.55123013508661 25 0.522263631712589 0.955472736574822 0.477736368287411 26 0.88698099453639 0.226038010927219 0.113019005463609 27 0.89323302101032 0.213533957979360 0.106766978989680 28 0.851454436864561 0.297091126270877 0.148545563135439 29 0.78467827937304 0.43064344125392 0.21532172062696 30 0.786274831309862 0.427450337380276 0.213725168690138 31 0.90647890631879 0.187042187362419 0.0935210936812095 32 0.94554094713492 0.108918105730161 0.0544590528650805 33 0.961534900300635 0.0769301993987293 0.0384650996993647 34 0.947738548192084 0.104522903615831 0.0522614518079157 35 0.980671718877877 0.0386565622442467 0.0193282811221234 36 0.958885198074412 0.0822296038511756 0.0411148019255878 37 0.942068180125394 0.115863639749213 0.0579318198746063 38 0.963863995520063 0.0722720089598748 0.0361360044799374 39 0.988721775714146 0.0225564485717079 0.0112782242858540 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 2 0.0952380952380952 NOK 10% type I error level 5 0.238095238095238 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/109hsb1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/109hsb1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/1z6b21258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/1z6b21258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/2a3p81258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/2a3p81258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/3euwh1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/3euwh1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/4njiz1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/4njiz1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/58bg61258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/58bg61258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/68xwq1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/68xwq1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/7wjgd1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/7wjgd1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/8qdmk1258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/8qdmk1258620691.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/9ixx21258620691.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620989z144dgiv07t546s/9ixx21258620691.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.

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