Multiple regression analysis: # lags = 2

*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: Tue, 17 Nov 2009 11:03:48 -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/17/t1258481132l8mt8sy16f6uiig.htm/, Retrieved Tue, 17 Nov 2009 19:05:44 +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/17/t1258481132l8mt8sy16f6uiig.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 «

8,4 98.6 8,4 8,4 8,6 98.5 8,4 8,4 8,9 98.9 8,6 8,4 8,8 99.4 8,9 8,6 8,3 99.8 8,8 8,9 7,5 99.9 8,3 8,8 7,2 100 7,5 8,3 7,4 100.1 7,2 7,5 8,8 100.1 7,4 7,2 9,3 100.2 8,8 7,4 9,3 100.3 9,3 8,8 8,7 100 9,3 9,3 8,2 99.9 8,7 9,3 8,3 99.4 8,2 8,7 8,5 99.8 8,3 8,2 8,6 99.6 8,5 8,3 8,5 100 8,6 8,5 8,2 99.9 8,5 8,6 8,1 100.3 8,2 8,5 7,9 100.6 8,1 8,2 8,6 100.7 7,9 8,1 8,7 100.8 8,6 7,9 8,7 100.8 8,7 8,6 8,5 100.6 8,7 8,7 8,4 101.1 8,5 8,7 8,5 101.1 8,4 8,5 8,7 100.9 8,5 8,4 8,7 101.1 8,7 8,5 8,6 101.2 8,7 8,7 8,5 101.4 8,6 8,7 8,3 101.9 8,5 8,6 8 102.1 8,3 8,5 8,2 102.1 8 8,3 8,1 103 8,2 8 8,1 103.4 8,1 8,2 8 103.2 8,1 8,1 7,9 103.1 8 8,1 7,9 103 7,9 8 8 103.7 7,9 7,9 8 103.4 8 7,9 7,9 103.5 8 8 8 103.8 7,9 8 7,7 104 8 7,9 7,2 104.2 7,7 8 7,5 104.4 7,2 7,7 7,3 104.4 7,5 7,2 7 104.9 7,3 7,5 7 105.3 7 7,3 7 105.2 7 7 7,2 105.4 7 7 7,3 105.4 7,2 7 7,1 105.5 7,3 7,2 6,8 105.7 7,1 7,3 6,4 105.6 6,8 7,1 6,1 105.8 6,4 6,8 6,5 105.4 6,1 6,4 7,7 105.5 6,5 6,1 7,9 105.8 7, etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 26.3106019568154 -0.232618213537436X[t] + 1.31669964010823Y1[t] -0.717070137986305Y2[t] + 0.0457433670576203M1[t] + 0.193077155005919M2[t] + 0.213469490726063M3[t] -0.0394471352569176M4[t] -0.00298783299766659M5[t] -0.0692285262308117M6[t] -0.0376573725565226M7[t] + 0.044262574759517M8[t] + 0.664799937984984M9[t] -0.203343875163899M10[t] + 0.078324253693556M11[t] + 0.0195867962194246t + 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) 26.3106019568154 5.272524 4.9901 6e-06 3e-06 X -0.232618213537436 0.04956 -4.6937 1.8e-05 9e-06 Y1 1.31669964010823 0.0903 14.5814 0 0 Y2 -0.717070137986305 0.087976 -8.1507 0 0 M1 0.0457433670576203 0.104131 0.4393 0.662175 0.331087 M2 0.193077155005919 0.109927 1.7564 0.084585 0.042292 M3 0.213469490726063 0.108866 1.9609 0.054968 0.027484 M4 -0.0394471352569176 0.108836 -0.3624 0.718408 0.359204 M5 -0.00298783299766659 0.101962 -0.0293 0.976729 0.488364 M6 -0.0692285262308117 0.102329 -0.6765 0.501541 0.250771 M7 -0.0376573725565226 0.104668 -0.3598 0.720389 0.360194 M8 0.044262574759517 0.109779 0.4032 0.688366 0.344183 M9 0.664799937984984 0.113401 5.8624 0 0 M10 -0.203343875163899 0.127001 -1.6011 0.115079 0.057539 M11 0.078324253693556 0.103561 0.7563 0.452691 0.226346 t 0.0195867962194246 0.005247 3.7329 0.000451 0.000225 Multiple Linear Regression - Regression Statistics Multiple R 0.977494074989697 R-squared 0.955494666639963 Adjusted R-squared 0.943356848450863 F-TEST (value) 78.7204629163045 F-TEST (DF numerator) 15 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.166646632358458 Sum Squared Residuals 1.52741050420283 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.4 8.47666408312547 -0.0766640831254709 2 8.6 8.66684648864691 -0.0668464886469137 3 8.9 8.87711826319315 0.0228817368068482 4 8.8 8.77907519109609 0.020924808903915 5 8.3 8.39528299875307 -0.0952829987530732 6 7.5 7.73872447413012 -0.238724474130125 7 7.2 7.07179595957667 0.128204040423333 8 7.4 7.32868710011497 0.071312899885034 9 8.8 8.4472722289774 0.352727771022606 10 9.3 9.27541885924845 0.0245811407515536 11 9.3 9.20786358984487 0.0921364101551311 12 8.7 8.86037652743881 -0.160376527438816 13 8.2 8.15894872800467 0.0410512719953339 14 8.3 8.21407068167878 0.0859293183212242 15 8.5 8.65120756120735 -0.151207561207351 16 8.6 8.6560342883743 -0.0560342883742954 17 8.5 8.60728903785156 -0.107289037851557 18 8.2 8.38051998438213 -0.180519984382127 19 8.1 8.01532777062703 0.0846722293729708 20 7.9 8.13050012748633 -0.230500127486333 21 8.6 8.55572955135447 0.0442704486455342 22 8.7 8.74901448874428 -0.049014488744283 23 8.7 8.67999028124157 0.0200097187584285 24 8.5 8.5960694526763 -0.0960694526762968 25 8.4 8.28175058116298 0.118249418837021 26 8.5 8.46041522891714 0.0395847710828592 27 8.7 8.75029498137365 -0.0502949813736477 28 8.7 8.66207442312562 0.0379255768743791 29 8.6 8.55144467265329 0.0485553273467096 30 8.5 8.32659716892126 0.173402831078740 31 8.3 8.20148306183406 0.0985169381659373 32 8 8.06483324843903 -0.064833248439029 33 8.2 8.45336154344871 -0.253361543448714 34 8.1 7.8739091037531 0.226090896246903 35 8.1 7.80703275180692 0.292967248193081 36 8 7.8665259508389 0.133474049161095 37 7.9 7.82344797145887 0.0765520285411274 38 7.9 7.95366742676815 -0.0536674267681458 39 8 7.90252082303014 0.09747917696986 40 8 7.87064642133864 0.129353578661364 41 7.9 7.83172368466494 0.0682763153350611 42 8 7.58361435957917 0.416385640420833 43 7.7 7.79162564457484 -0.0916256445748446 44 7.2 7.37989183957172 -0.179891839571723 45 7.5 7.5302635776509 -0.0302635776509058 46 7.3 7.43525152174707 -0.135251521747067 47 7 7.1417363706377 -0.141736370637692 48 7 6.73835576331338 0.261644236686618 49 7 7.04206878934006 -0.0420687893400604 50 7.2 7.1624657308003 0.0375342691997049 51 7.3 7.46578479076151 -0.16578479076151 52 7.1 7.19744907605777 -0.0974490760577728 53 6.8 6.87192459000868 -0.071924590008685 54 6.4 6.5969366499135 -0.196936649913503 55 6.1 6.29001214245233 -0.190012142452331 56 6.5 6.37638433456482 0.123615665435180 57 7.7 7.73504757009515 -0.0350475700951527 58 7.9 8.10991660203981 -0.209916602039813 59 7.5 7.74424182549354 -0.244241825493542 60 6.9 7.0386723057326 -0.138672305732600 61 6.6 6.71711984690795 -0.117119846907951 62 6.9 6.94253444318873 -0.0425344431887289 63 7.7 7.4530735804342 0.246926419565800 64 8 8.0347206000076 -0.03472060000759 65 8 7.84233501606846 0.157664983931544 66 7.7 7.67360736307382 0.0263926369261807 67 7.3 7.32975542093507 -0.0297554209350657 68 7.4 7.11970334982313 0.280296650176870 69 8.1 8.17832552847337 -0.078325528473368 70 8.3 8.1564894244673 0.143510575532707 71 8.2 8.2191351809754 -0.0191351809754070 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0708558767589133 0.141711753517827 0.929144123241087 20 0.284203277795618 0.568406555591235 0.715796722204382 21 0.321253293316592 0.642506586633184 0.678746706683408 22 0.205007919205787 0.410015838411574 0.794992080794213 23 0.129879369746952 0.259758739493904 0.870120630253048 24 0.0838253068968954 0.167650613793791 0.916174693103105 25 0.0458643231207299 0.0917286462414598 0.95413567687927 26 0.0285659189647745 0.057131837929549 0.971434081035226 27 0.0165374856663118 0.0330749713326236 0.983462514333688 28 0.00829558692536685 0.0165911738507337 0.991704413074633 29 0.00778600474340086 0.0155720094868017 0.9922139952566 30 0.0338425849261087 0.0676851698522173 0.966157415073891 31 0.0216544274312260 0.0433088548624520 0.978345572568774 32 0.0154049450760976 0.0308098901521953 0.984595054923902 33 0.121377779994997 0.242755559989994 0.878622220005003 34 0.113450290826440 0.226900581652881 0.88654970917356 35 0.164728129558683 0.329456259117366 0.835271870441317 36 0.126474779702595 0.252949559405189 0.873525220297405 37 0.123087043958946 0.246174087917891 0.876912956041054 38 0.150386893103997 0.300773786207994 0.849613106896003 39 0.102728783865495 0.205457567730991 0.897271216134505 40 0.0697409270202287 0.139481854040457 0.930259072979771 41 0.0553428565095962 0.110685713019192 0.944657143490404 42 0.212577491039251 0.425154982078503 0.787422508960749 43 0.240879624810273 0.481759249620545 0.759120375189727 44 0.376424973087706 0.752849946175411 0.623575026912294 45 0.330503536377131 0.661007072754263 0.669496463622869 46 0.334930401836525 0.66986080367305 0.665069598163475 47 0.351560570863202 0.703121141726404 0.648439429136798 48 0.685083054644922 0.629833890710155 0.314916945355077 49 0.691944717129526 0.616110565740947 0.308055282870474 50 0.838060747705872 0.323878504588255 0.161939252294128 51 0.799369259536431 0.401261480927138 0.200630740463569 52 0.755898563837323 0.488202872325355 0.244101436162677 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 5 0.147058823529412 NOK 10% type I error level 8 0.235294117647059 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/103z5x1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/103z5x1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/1738v1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/1738v1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/2jgny1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/2jgny1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/3z6sj1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/3z6sj1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/4sx611258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/4sx611258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/54cpf1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/54cpf1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/6smc41258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/6smc41258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/7f8nx1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/7f8nx1258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/8lg061258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/8lg061258481021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/9ha2h1258481021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481132l8mt8sy16f6uiig/9ha2h1258481021.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