W8 - Yt-5 en Yt-6

*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: Sun, 28 Nov 2010 13:13:37 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht.htm/, Retrieved Sun, 28 Nov 2010 14:13:20 +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/2010/Nov/28/t1290949989zdpv2krt4jy0wht.htm/}, year = {2010}, } @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 = {2010}, 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 «

9081 9700 9084 9081 9743 9084 8587 9743 9731 8587 9563 9731 9998 9563 9437 9998 10038 9437 9918 10038 9252 9918 9737 9252 9035 9737 9133 9035 9487 9133 8700 9487 9627 8700 8947 9627 9283 8947 8829 9283 9947 8829 9628 9947 9318 9628 9605 9318 8640 9605 9214 8640 9567 9214 8547 9567 9185 8547 9470 9185 9123 9470 9278 9123 10170 9278 9434 10170 9655 9434 9429 9655 8739 9429 9552 8739 9687 9552 9019 9687 9672 9019 9206 9672 9069 9206 9788 9069 10312 9788 10105 10312 9863 10105 9656 9863 9295 9656 9946 9295 9701 9946 9049 9701 10190 9049 9706 10190 9765 9706 9893 9765 9994 9893 10433 9994 10073 10433 10112 10073 9266 10112 9820 9266 10097 9820 9115 10097 10411 9115 9678 10411 10408 9678 10153 10408 10368 10153

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Yt-5[t] = + 7020.47641726063 + 0.250863300117026`Yt-6`[t] -679.47273995534M1[t] -63.2724898724251M2[t] + 72.1050849430458M3[t] -877.017102218858M4[t] + 302.088829651828M5[t] -322.230497226616M6[t] -56.5172825314797M7[t] -153.698715001137M8[t] + 425.146565088753M9[t] + 95.456109910176M10[t] -136.157685669757M11[t] + 7.52661398200408t + 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) 7020.47641726063 1206.106206 5.8208 0 0 `Yt-6` 0.250863300117026 0.129066 1.9437 0.057057 0.028528 M1 -679.47273995534 166.985747 -4.069 0.000152 7.6e-05 M2 -63.2724898724251 182.470663 -0.3468 0.730099 0.36505 M3 72.1050849430458 167.15312 0.4314 0.667884 0.333942 M4 -877.017102218858 166.567529 -5.2652 2e-06 1e-06 M5 302.088829651828 194.598402 1.5524 0.126309 0.063155 M6 -322.230497226616 167.476942 -1.924 0.059532 0.029766 M7 -56.5172825314797 168.305294 -0.3358 0.738298 0.369149 M8 -153.698715001137 166.155366 -0.925 0.358991 0.179496 M9 425.146565088753 166.583286 2.5522 0.013514 0.006757 M10 95.456109910176 184.141959 0.5184 0.60627 0.303135 M11 -136.157685669757 177.169138 -0.7685 0.445467 0.222733 t 7.52661398200408 2.082157 3.6148 0.000653 0.000326 Multiple Linear Regression - Regression Statistics Multiple R 0.855889471630712 R-squared 0.7325467876483 Adjusted R-squared 0.669330573819716 F-TEST (value) 11.5879573179543 F-TEST (DF numerator) 13 F-TEST (DF denominator) 55 p-value 1.81126225129447e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 274.182074467497 Sum Squared Residuals 4134669.5477615 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9081 8781.90430242245 299.095697577554 2 9084 9250.34678371493 -166.346783714931 3 9743 9394.00356241276 348.996437587244 4 8587 8617.72690400998 -30.7269040099772 5 9731 9514.36147492739 216.638525072615 6 9563 9184.55637736483 378.443622635174 7 9998 9415.6511716223 582.348828377696 8 9437 9435.12188868556 1.87811131444258 9 10038 9880.7594713918 157.240528608201 10 9918 9709.36447356556 208.635526434441 11 9252 9455.1736959536 -203.173695953588 12 9737 9431.7830377274 305.216962272592 13 9035 8881.50561231083 153.49438768917 14 9133 9329.1264396936 -196.126439693597 15 9487 9496.61523190254 -9.61523190254031 16 8700 8643.82526696407 56.1747330359326 17 9627 9633.02839562466 -6.02839562465827 18 8947 9248.7859619367 -301.785961936702 19 9283 9351.43874653426 -68.4387465342645 20 8829 9346.07399688593 -517.073996885932 21 9947 9818.5539527047 128.446047295304 22 9628 9776.85528103896 -148.855281038959 23 9318 9472.7427067037 -154.742706703698 24 9605 9538.65938331918 66.3406166808187 25 8640 8938.71102447943 -298.711024479432 26 9214 9320.35480393142 -106.354803931420 27 9567 9607.25452699607 -40.2545269960684 28 8547 8754.21369875748 -207.213698757478 29 9185 9684.9656784908 -499.965678490802 30 9470 9228.22375106902 241.776248930975 31 9123 9572.95962027952 -449.959620279518 32 9278 9396.25523665126 -118.255236651257 33 10170 10021.5109422413 148.489057758711 34 9434 9923.1171647491 -489.117164749105 35 9655 9514.39459426504 140.605405734956 36 9429 9713.51968324267 -284.519683242668 37 8739 8984.87845144288 -245.878451442884 38 9552 9435.50963842705 116.490361572945 39 9687 9782.36569021967 -95.3656902196723 40 9019 8874.63666255557 144.363337444430 41 9672 9893.69252393009 -221.692523930088 42 9206 9440.71354601007 -234.713546010066 43 9069 9597.05107683267 -528.051076832672 44 9788 9473.02798622899 314.972013771014 45 10312 10239.7705930850 72.229406914978 46 10105 10049.0591211498 55.9408788502284 47 9863 9773.04323642762 89.956763572382 48 9656 9856.01861745106 -200.018617451059 49 9295 9132.1437883535 162.856211646502 50 9946 9665.30900107617 280.690998923829 51 9701 9971.52519824983 -270.52519824983 52 9049 8968.46811654126 80.5318834587424 53 10190 9991.53779071765 198.462209282353 54 9706 9660.98010325473 45.0198967452653 55 9765 9812.80209467523 -47.8020946752344 56 9893 9737.94821089448 155.051789105514 57 9994 10356.4306073814 -362.430607381359 58 10433 10059.6039594966 373.396040503394 59 10073 9945.64576665005 127.354233349948 60 10112 9999.01927825968 112.980721740317 61 9266 9336.8568209909 -70.8568209909108 62 9820 9748.35333315683 71.6466668431742 63 10097 10030.2357902191 66.7642097808667 64 9115 9158.12935117165 -43.129351171649 65 10411 10098.4141363094 312.58586369058 66 9678 9806.74026036465 -128.740260364646 67 10408 9896.097290056 511.902709943993 68 10153 9989.57268065378 163.427319346218 69 10368 10511.9744331958 -143.974433195834 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.056504302788464 0.113008605576928 0.943495697211536 18 0.541618437956419 0.916763124087162 0.458381562043581 19 0.554558275205689 0.890883449588623 0.445441724794311 20 0.440854208102566 0.881708416205132 0.559145791897434 21 0.514010457693492 0.971979084613017 0.485989542306508 22 0.415800622900055 0.831601245800111 0.584199377099945 23 0.418875094957492 0.837750189914985 0.581124905042508 24 0.354658928560669 0.709317857121339 0.64534107143933 25 0.291730699564732 0.583461399129465 0.708269300435268 26 0.424691667180891 0.849383334361782 0.575308332819109 27 0.357332958348975 0.71466591669795 0.642667041651025 28 0.276820039233162 0.553640078466324 0.723179960766838 29 0.339184293539972 0.678368587079944 0.660815706460028 30 0.523535319804669 0.95292936039066 0.47646468019533 31 0.517930757554188 0.964138484891624 0.482069242445812 32 0.536436645007689 0.927126709984621 0.463563354992311 33 0.635677050822106 0.728645898355789 0.364322949177894 34 0.660964092878909 0.678071814242183 0.339035907121091 35 0.713933549816912 0.572132900366175 0.286066450183088 36 0.638141390442139 0.723717219115722 0.361858609557861 37 0.57322552397043 0.85354895205914 0.42677447602957 38 0.643488572121076 0.713022855757847 0.356511427878924 39 0.592852013745946 0.814295972508108 0.407147986254054 40 0.671282583456328 0.657434833087344 0.328717416543672 41 0.624604240133178 0.750791519733644 0.375395759866822 42 0.545267198325276 0.909465603349448 0.454732801674724 43 0.881478407423037 0.237043185153925 0.118521592576963 44 0.898133991538457 0.203732016923086 0.101866008461543 45 0.953242268643902 0.0935154627121968 0.0467577313560984 46 0.929938999223243 0.140122001553514 0.0700610007767572 47 0.89104483164016 0.217910336719681 0.108955168359841 48 0.861411332492943 0.277177335014114 0.138588667507057 49 0.815995637529766 0.368008724940467 0.184004362470234 50 0.909134833055171 0.181730333889658 0.0908651669448288 51 0.819259906989893 0.361480186020214 0.180740093010107 52 0.708094131189167 0.583811737621667 0.291905868810833 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 0.0277777777777778 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/10b2u71290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/10b2u71290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/14jfv1290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/14jfv1290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/24jfv1290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/24jfv1290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/3xseg1290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/3xseg1290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/4xseg1290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/4xseg1290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/5xseg1290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/5xseg1290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/6qjv11290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/6qjv11290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/70bu41290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/70bu41290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/80bu41290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/80bu41290950009.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/90bu41290950009.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290949989zdpv2krt4jy0wht/90bu41290950009.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