blog

*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: Mon, 01 Dec 2008 08:44:12 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj.htm/, Retrieved Mon, 01 Dec 2008 15:45:11 +0000

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/2008/Dec/01/t122814631110pf5rdjgsuv7rj.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

blog

Dataseries X:

» Textbox « » Textfile « » CSV «

14929387,5 0 14717825,3 0 15826281,2 0 16301309,6 0 15033016,9 0 16998460,6 0 14066462,7 0 13328937,3 0 17319718,2 0 17586426,8 0 15887037,4 0 17935679,1 0 15869489 0 15892510,9 0 17556558,1 0 16791643 0 15953688,5 0 18144913,6 0 14390881 0 13885708,7 0 17332571,5 0 17152595,8 0 16003877,1 0 16841467,1 0 14783398,1 0 14667847,5 0 17714362,2 0 16282088 1 15014866,2 1 17722582,4 1 13876509,4 1 15495489,6 1 17799521,1 1 17920079,1 1 17248022,4 1 18813782,4 1 16249688,3 1 17823358,5 1 20424438,3 1 17814218,7 1 19699959,6 1 19776328,1 1 15679833,1 1 17119266,5 1 20092613 1 20863688,3 1 20925203,1 1 21032593 1 20664684,3 1 19711511,4 1 22553293,4 1 19498332,9 1 20722827,8 1 21321275 1 17960847,7 1 17789654,9 1 20003708,5 1 21169851,7 1 20422839,4 1 19810562,3 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation x[t] = + 15453593.0116667 -187164.427777778y[t] -1341566.15125000M1[t] -1376771.60527778M2[t] + 777117.580694444M3[t] -761404.467777779M4[t] -912537.841805555M5[t] + 496815.564166667M6[t] -3199476.32986111M7[t] -2969058.44388889M8[t] -81730.1179166674M9[t] + 248685.028055555M10[t] -690934.165972223M11[t] + 98486.7340277778t + 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) 15453593.0116667 576154.599446 26.822 0 0 y -187164.427777778 554405.021808 -0.3376 0.737204 0.368602 M1 -1341566.15125000 672370.86634 -1.9953 0.051955 0.025978 M2 -1376771.60527778 670654.415434 -2.0529 0.045798 0.022899 M3 777117.580694444 669316.354843 1.1611 0.251607 0.125803 M4 -761404.467777779 677494.127493 -1.1239 0.266906 0.133453 M5 -912537.841805555 674652.674481 -1.3526 0.182794 0.091397 M6 496815.564166667 672180.36602 0.7391 0.463595 0.231797 M7 -3199476.32986111 670081.288069 -4.7748 1.9e-05 9e-06 M8 -2969058.44388889 668358.957192 -4.4423 5.6e-05 2.8e-05 M9 -81730.1179166674 667016.291839 -0.1225 0.903012 0.451506 M10 248685.028055555 666055.588053 0.3734 0.710588 0.355294 M11 -690934.165972223 665478.500071 -1.0383 0.30458 0.15229 t 98486.7340277778 16004.294429 6.1538 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.912059686768993 R-squared 0.831852872229153 Adjusted R-squared 0.784333031772175 F-TEST (value) 17.5053801576262 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.29785071578681e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1051909.56908919 Sum Squared Residuals 50899632110904.9 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14929387.5 14210513.5944445 718873.905555543 2 14717825.3 14273794.8744444 444030.425555557 3 15826281.2 16526170.7944444 -699889.594444444 4 16301309.6 15086135.48 1215174.12 5 15033016.9 15033488.84 -471.939999998604 6 16998460.6 16541328.98 457131.620000001 7 14066462.7 12943523.82 1122938.88 8 13328937.3 13272428.44 56508.860000001 9 17319718.2 16258243.5 1061474.70000000 10 17586426.8 16687145.38 899281.42 11 15887037.4 15846012.92 41024.480000001 12 17935679.1 16635433.82 1300245.28000000 13 15869489 15392354.4027778 477134.597222224 14 15892510.9 15455635.6827778 436875.217222221 15 17556558.1 17708011.6027778 -151453.502777776 16 16791643 16267976.2883333 523666.711666667 17 15953688.5 16215329.6483333 -261641.148333334 18 18144913.6 17723169.7883333 421743.811666667 19 14390881 14125364.6283333 265516.371666667 20 13885708.7 14454269.2483333 -568560.548333334 21 17332571.5 17440084.3083333 -107512.808333333 22 17152595.8 17868986.1883333 -716390.388333333 23 16003877.1 17027853.7283333 -1023976.62833333 24 16841467.1 17817274.6283333 -975807.528333332 25 14783398.1 16574195.2111111 -1790797.11111111 26 14667847.5 16637476.4911111 -1969628.99111111 27 17714362.2 18889852.4111111 -1175490.21111111 28 16282088 17262652.6688889 -980564.668888888 29 15014866.2 17210006.0288889 -2195139.82888889 30 17722582.4 18717846.1688889 -995263.76888889 31 13876509.4 15120041.0088889 -1243531.60888889 32 15495489.6 15448945.6288889 46543.971111111 33 17799521.1 18434760.6888889 -635239.588888887 34 17920079.1 18863662.5688889 -943583.468888888 35 17248022.4 18022530.1088889 -774507.70888889 36 18813782.4 18811951.0088889 1831.39111110945 37 16249688.3 17568871.5916667 -1319183.29166666 38 17823358.5 17632152.8716667 191205.628333334 39 20424438.3 19884528.7916667 539909.508333334 40 17814218.7 18444493.4772222 -630274.777222223 41 19699959.6 18391846.8372222 1308112.76277778 42 19776328.1 19899686.9772222 -123358.877222221 43 15679833.1 16301881.8172222 -622048.717222223 44 17119266.5 16630786.4372222 488480.062777778 45 20092613 19616601.4972222 476011.502777778 46 20863688.3 20045503.3772222 818184.922777778 47 20925203.1 19204370.9172222 1720832.18277778 48 21032593 19993791.8172222 1038801.18277778 49 20664684.3 18750712.4 1913971.90000000 50 19711511.4 18813993.68 897517.719999999 51 22553293.4 21066369.6 1486923.80000000 52 19498332.9 19626334.2855556 -128001.385555557 53 20722827.8 19573687.6455556 1149140.15444444 54 21321275 21081527.7855556 239747.214444443 55 17960847.7 17483722.6255556 477125.074444444 56 17789654.9 17812627.2455556 -22972.3455555567 57 20003708.5 20798442.3055556 -794733.805555556 58 21169851.7 21227344.1855556 -57492.4855555578 59 20422839.4 20386211.7255556 36627.6744444429 60 19810562.3 21175632.6255556 -1365070.32555556 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0581132481284161 0.116226496256832 0.941886751871584 18 0.0190011991930094 0.0380023983860187 0.98099880080699 19 0.0175609907939334 0.0351219815878669 0.982439009206067 20 0.00741377419899348 0.0148275483979870 0.992586225801007 21 0.0101940725904030 0.0203881451808061 0.989805927409597 22 0.0210595168495868 0.0421190336991735 0.978940483150413 23 0.0123715776226931 0.0247431552453862 0.987628422377307 24 0.0458858165704375 0.091771633140875 0.954114183429563 25 0.0586508895534601 0.117301779106920 0.94134911044654 26 0.0546060245222275 0.109212049044455 0.945393975477772 27 0.0381342948273146 0.0762685896546292 0.961865705172685 28 0.0204851455471440 0.0409702910942880 0.979514854452856 29 0.0373527762883056 0.0747055525766113 0.962647223711694 30 0.0239948004123721 0.0479896008247441 0.976005199587628 31 0.014340403862491 0.028680807724982 0.985659596137509 32 0.0407117387977818 0.0814234775955636 0.959288261202218 33 0.0233406692795251 0.0466813385590501 0.976659330720475 34 0.0160214013849640 0.0320428027699279 0.983978598615036 35 0.0214551762281633 0.0429103524563266 0.978544823771837 36 0.0157479141248936 0.0314958282497872 0.984252085875106 37 0.115266829398621 0.230533658797243 0.884733170601379 38 0.201143585953122 0.402287171906245 0.798856414046877 39 0.377388421942960 0.754776843885919 0.62261157805704 40 0.339315483703882 0.678630967407765 0.660684516296118 41 0.44604189277225 0.8920837855445 0.55395810722775 42 0.405019662119811 0.810039324239623 0.594980337880189 43 0.731620453926686 0.536759092146628 0.268379546073314 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 13 0.481481481481481 NOK 10% type I error level 17 0.62962962962963 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/10fx171228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/10fx171228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/1yxfd1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/1yxfd1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/2of9x1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/2of9x1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/3x7xz1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/3x7xz1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/4luvo1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/4luvo1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/53ckp1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/53ckp1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/68vl11228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/68vl11228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/7dq001228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/7dq001228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/8obbo1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/8obbo1228146239.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/9q8lw1228146239.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t122814631110pf5rdjgsuv7rj/9q8lw1228146239.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