*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 30 Dec 2009 12:58:03 -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/Dec/30/t1262203161aw48ninigf6ue4n.htm/, Retrieved Wed, 30 Dec 2009 20:59:33 +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/Dec/30/t1262203161aw48ninigf6ue4n.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 «

28029 0 29383 0 36438 0 32034 0 22679 0 24319 0 18004 0 17537 0 20366 0 22782 0 19169 0 13807 0 29743 0 25591 0 29096 0 26482 0 22405 0 27044 0 17970 0 18730 0 19684 0 19785 0 18479 0 10698 0 31956 0 29506 0 34506 0 27165 0 26736 0 23691 0 18157 0 17328 0 18205 0 20995 0 17382 0 9367 0 31124 0 26551 0 30651 0 25859 0 25100 0 25778 0 20418 0 18688 0 20424 0 24776 0 19814 1 12738 1 31566 1 30111 1 30019 1 31934 1 25826 1 26835 1 20205 1 17789 1 20520 1 22518 1 15572 1 11509 1 25447 1 24090 1 27786 1 26195 1 20516 1 22759 1 19028 1 16971 1 20036 1 22485 1

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 inschrijvingen[t] = + 23361.2391304348 -766.697463768116dummyvariabele[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) 23361.2391304348 865.401924 26.9947 0 0 dummyvariabele -766.697463768116 1477.955151 -0.5188 0.605615 0.302808 Multiple Linear Regression - Regression Statistics Multiple R 0.062784242555626 R-squared 0.00394186111328369 Adjusted R-squared -0.0107060526938738 F-TEST (value) 0.269107339460007 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 0.605615346457656 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5869.4414133563 Sum Squared Residuals 2342623290.3279 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 28029 23361.2391304348 4667.76086956518 2 29383 23361.2391304348 6021.76086956522 3 36438 23361.2391304348 13076.7608695652 4 32034 23361.2391304348 8672.76086956522 5 22679 23361.2391304348 -682.239130434782 6 24319 23361.2391304348 957.760869565218 7 18004 23361.2391304348 -5357.23913043478 8 17537 23361.2391304348 -5824.23913043478 9 20366 23361.2391304348 -2995.23913043478 10 22782 23361.2391304348 -579.239130434782 11 19169 23361.2391304348 -4192.23913043478 12 13807 23361.2391304348 -9554.23913043478 13 29743 23361.2391304348 6381.76086956522 14 25591 23361.2391304348 2229.76086956522 15 29096 23361.2391304348 5734.76086956522 16 26482 23361.2391304348 3120.76086956522 17 22405 23361.2391304348 -956.239130434782 18 27044 23361.2391304348 3682.76086956522 19 17970 23361.2391304348 -5391.23913043478 20 18730 23361.2391304348 -4631.23913043478 21 19684 23361.2391304348 -3677.23913043478 22 19785 23361.2391304348 -3576.23913043478 23 18479 23361.2391304348 -4882.23913043478 24 10698 23361.2391304348 -12663.2391304348 25 31956 23361.2391304348 8594.76086956522 26 29506 23361.2391304348 6144.76086956522 27 34506 23361.2391304348 11144.7608695652 28 27165 23361.2391304348 3803.76086956522 29 26736 23361.2391304348 3374.76086956522 30 23691 23361.2391304348 329.760869565218 31 18157 23361.2391304348 -5204.23913043478 32 17328 23361.2391304348 -6033.23913043478 33 18205 23361.2391304348 -5156.23913043478 34 20995 23361.2391304348 -2366.23913043478 35 17382 23361.2391304348 -5979.23913043478 36 9367 23361.2391304348 -13994.2391304348 37 31124 23361.2391304348 7762.76086956522 38 26551 23361.2391304348 3189.76086956522 39 30651 23361.2391304348 7289.76086956522 40 25859 23361.2391304348 2497.76086956522 41 25100 23361.2391304348 1738.76086956522 42 25778 23361.2391304348 2416.76086956522 43 20418 23361.2391304348 -2943.23913043478 44 18688 23361.2391304348 -4673.23913043478 45 20424 23361.2391304348 -2937.23913043478 46 24776 23361.2391304348 1414.76086956522 47 19814 22594.5416666667 -2780.54166666667 48 12738 22594.5416666667 -9856.54166666667 49 31566 22594.5416666667 8971.45833333333 50 30111 22594.5416666667 7516.45833333333 51 30019 22594.5416666667 7424.45833333333 52 31934 22594.5416666667 9339.45833333333 53 25826 22594.5416666667 3231.45833333333 54 26835 22594.5416666667 4240.45833333333 55 20205 22594.5416666667 -2389.54166666667 56 17789 22594.5416666667 -4805.54166666667 57 20520 22594.5416666667 -2074.54166666667 58 22518 22594.5416666667 -76.5416666666662 59 15572 22594.5416666667 -7022.54166666667 60 11509 22594.5416666667 -11085.5416666667 61 25447 22594.5416666667 2852.45833333333 62 24090 22594.5416666667 1495.45833333333 63 27786 22594.5416666667 5191.45833333333 64 26195 22594.5416666667 3600.45833333333 65 20516 22594.5416666667 -2078.54166666667 66 22759 22594.5416666667 164.458333333334 67 19028 22594.5416666667 -3566.54166666667 68 16971 22594.5416666667 -5623.54166666667 69 20036 22594.5416666667 -2558.54166666667 70 22485 22594.5416666667 -109.541666666666 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.633712977293058 0.732574045413884 0.366287022706942 6 0.575675957218891 0.848648085562218 0.424324042781109 7 0.761164977409992 0.477670045180016 0.238835022590008 8 0.831766822392944 0.336466355214113 0.168233177607056 9 0.801031453544653 0.397937092910694 0.198968546455347 10 0.728172099378876 0.543655801242249 0.271827900621124 11 0.70597508871469 0.588049822570621 0.294024911285310 12 0.822259692941054 0.355480614117893 0.177740307058946 13 0.814242343761209 0.371515312477583 0.185757656238791 14 0.752602356573554 0.494795286852891 0.247397643426446 15 0.728124741802121 0.543750516395758 0.271875258197879 16 0.664353528594528 0.671292942810943 0.335646471405472 17 0.592507945259546 0.814984109480907 0.407492054740454 18 0.5312291682486 0.9375416635028 0.4687708317514 19 0.535238866398575 0.929522267202849 0.464761133601425 20 0.513858020776211 0.972283958447578 0.486141979223789 21 0.470491141695251 0.940982283390503 0.529508858304749 22 0.424454698750391 0.848909397500782 0.575545301249609 23 0.401746383093404 0.803492766186808 0.598253616906596 24 0.651340447915586 0.697319104168829 0.348659552084414 25 0.718198815564233 0.563602368871533 0.281801184435767 26 0.719251777665786 0.561496444668428 0.280748222334214 27 0.84389271574219 0.312214568515621 0.156107284257811 28 0.818379070127121 0.363241859745757 0.181620929872879 29 0.787497452312862 0.425005095374275 0.212502547687138 30 0.734783581212937 0.530432837574125 0.265216418787063 31 0.717152555694108 0.565694888611784 0.282847444305892 32 0.713464343938763 0.573071312122475 0.286535656061237 33 0.694252191886285 0.611495616227429 0.305747808113715 34 0.639118260512207 0.721763478975585 0.360881739487793 35 0.637632855983486 0.724734288033028 0.362367144016514 36 0.886798661081946 0.226402677836108 0.113201338918054 37 0.902408532311353 0.195182935377295 0.0975914676886475 38 0.876498233340093 0.247003533319814 0.123501766659907 39 0.894376379541266 0.211247240917468 0.105623620458734 40 0.867240299075699 0.265519401848602 0.132759700924301 41 0.832676596312849 0.334646807374303 0.167323403687151 42 0.803669177085732 0.392661645828536 0.196330822914268 43 0.754924331387386 0.490151337225228 0.245075668612614 44 0.719237303434598 0.561525393130805 0.280762696565402 45 0.671222609219818 0.657554781560365 0.328777390780182 46 0.602109077624787 0.795781844750426 0.397890922375213 47 0.53942189084762 0.92115621830476 0.46057810915238 48 0.638798604442353 0.722402791115294 0.361201395557647 49 0.760950042585583 0.478099914828834 0.239049957414417 50 0.803100473652313 0.393799052695374 0.196899526347687 51 0.842618854711173 0.314762290577653 0.157381145288827 52 0.929595669715096 0.140808660569807 0.0704043302849035 53 0.917848909481461 0.164302181037077 0.0821510905185386 54 0.921056038847458 0.157887922305084 0.0789439611525418 55 0.887136175883834 0.225727648232331 0.112863824116166 56 0.864174237291654 0.271651525416693 0.135825762708346 57 0.807089395291019 0.385821209417962 0.192910604708981 58 0.73502929487281 0.52994141025438 0.26497070512719 59 0.749484658293302 0.501030683413396 0.250515341706698 60 0.945793848284168 0.108412303431664 0.0542061517158322 61 0.921302344474373 0.157395311051253 0.0786976555256266 62 0.869623680525107 0.260752638949786 0.130376319474893 63 0.918228476973352 0.163543046053297 0.0817715230266484 64 0.957279929211847 0.0854401415763055 0.0427200707881527 65 0.884417076426898 0.231165847146204 0.115582923573102 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 0.0163934426229508 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/10fi5c1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/10fi5c1262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/1iad01262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/1iad01262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/2zaoo1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/2zaoo1262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/3guo41262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/3guo41262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/4n9811262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/4n9811262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/5jsha1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/5jsha1262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/6sf7i1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/6sf7i1262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/7s4l61262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/7s4l61262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/81lvv1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/81lvv1262203078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/9tann1262203078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262203161aw48ninigf6ue4n/9tann1262203078.ps (open in new window)

Parameters (Session):

par1 = 0 ; par2 = 0 ;

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