Paper Multiple Regression

*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: Sat, 18 Dec 2010 18:58:17 +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/Dec/18/t1292698578yd1rwxof7q9t0zg.htm/, Retrieved Sat, 18 Dec 2010 19:56:18 +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/Dec/18/t1292698578yd1rwxof7q9t0zg.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 «

97.06 21.454 631.923 130.678 97.73 23.899 654.294 120.877 98 24.939 671.833 137.114 97.76 23.580 586.840 134.406 97.48 24.562 600.969 120.262 97.77 24.696 625.568 130.846 97.96 23.785 558.110 120.343 98.22 23.812 630.577 98.881 98.51 21.917 628.654 115.678 98.19 19.713 603.184 120.796 98.37 19.282 656.255 94.261 98.31 18.788 600.730 89.151 98.6 21.453 670.326 119.880 98.96 24.482 678.423 131.468 99.11 27.474 641.502 155.089 99.64 27.264 625.311 149.581 100.02 27.349 628.177 122.788 99.98 30.632 589.767 143.900 100.32 29.429 582.471 112.115 100.44 30.084 636.248 109.600 100.51 26.290 599.885 117.446 101 24.379 621.694 118.456 100.88 23.335 637.406 101.901 100.55 21.346 595.994 89.940 100.82 21.106 696.308 129.143 101.5 24.514 674.201 126.102 102.15 28.353 648.861 143.048 102.39 30.805 649.605 142.258 102.54 31.348 672.392 131.011 102.85 34.556 598.396 146.471 103.47 33.855 613.177 114.073 103.56 34.787 638.104 114.642 103.69 32.529 615.632 118.226 103.49 29.998 634.465 111.338 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werklozen[t] = -229.959847776113 + 9.08718983433678CPI[t] -5.11489725323218vacatures[t] + 0.71790755702576inschrijvingen[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) -229.959847776113 121.51843 -1.8924 0.062057 0.031028 CPI 9.08718983433678 1.272436 7.1416 0 0 vacatures -5.11489725323218 0.76071 -6.7238 0 0 inschrijvingen 0.71790755702576 0.195796 3.6666 0.000441 0.00022 Multiple Linear Regression - Regression Statistics Multiple R 0.639302254189665 R-squared 0.408707372211987 Adjusted R-squared 0.386533898669936 F-TEST (value) 18.4322664393065 F-TEST (DF numerator) 3 F-TEST (DF denominator) 80 p-value 3.48979267705829e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 32.5867879364038 Sum Squared Residuals 84951.8998409722 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 631.923 636.122515610784 -4.19951561078404 2 654.294 622.668797049227 31.6252029507731 3 671.833 631.459510164564 40.3734898354363 4 586.84 634.28563630704 -47.4456363070397 5 600.969 616.564309564179 -15.5953095641790 6 625.568 626.112531967764 -0.544531967764153 7 558.11 624.958586362541 -66.848586362541 8 630.577 611.775421504744 18.8015784952555 9 628.654 636.162130086939 -7.50813008693892 10 603.184 648.201713762933 -45.0177137629327 11 656.255 632.992251623578 23.2627483764221 12 600.73 631.305271860213 -30.5752718602127 13 670.326 642.369937052151 27.9560629478489 14 678.423 638.467414383287 39.9555856167134 15 641.502 641.484414681272 0.017585318727991 16 625.311 643.420518892551 -18.1095188925513 17 628.177 627.203987587683 0.973012412316698 18 589.767 625.204756655877 -35.4377566558765 19 582.471 611.628930895125 -29.1579308951254 20 636.248 607.563598468459 28.684401531541 21 599.885 633.23832462805 -33.3533246280497 22 621.694 648.190702930397 -26.4967029303974 23 637.406 640.55523327609 -3.14923327608991 24 595.994 639.143098977852 -43.1490989778524 25 696.308 670.96834553198 25.3396544680201 26 674.201 657.532907899398 16.6680921006016 27 648.861 655.969152197918 -7.10815219791755 28 649.605 645.041202723183 4.56379727681734 29 672.392 635.55258569596 36.8394143040406 30 598.396 633.059874987853 -34.6638749878532 31 613.177 619.020706627137 -5.84370662713714 32 638.104 615.479958872163 22.6240411278373 33 615.632 630.783712232805 -15.1517122328051 34 634.465 636.967131961075 -2.50213196107487 35 638.686 638.682404801156 0.00359519884371367 36 604.243 623.900181652532 -19.6571816525323 37 706.669 659.987589926673 46.6814100733268 38 677.185 630.331340942343 46.8536590576569 39 644.328 632.512192752115 11.8158072478854 40 664.825 615.870037958228 48.9549620417723 41 605.707 597.503075793434 8.20392420656637 42 600.136 599.424713135498 0.711286864501609 43 612.166 589.502055295395 22.6639447046054 44 599.659 584.31983047839 15.3391695216096 45 634.21 613.243785053041 20.9662149469592 46 618.234 612.361379586719 5.87262041328069 47 613.576 625.868452505915 -12.2924525059149 48 627.2 612.349654212365 14.8503457876349 49 668.973 645.93683460439 23.0361653956104 50 651.479 624.320016753782 27.1589832462179 51 619.661 628.826006643577 -9.16500664357696 52 644.26 606.731268105075 37.5287318949249 53 579.936 618.233004382386 -38.2970043823858 54 601.752 616.375394581715 -14.6233945817146 55 595.376 606.97366150835 -11.5976615083504 56 588.902 595.874600082814 -6.97260008281352 57 634.341 573.27540142612 61.0655985738805 58 594.305 619.530048105428 -25.2250481054281 59 606.2 606.941170898473 -0.741170898473318 60 610.926 622.801254113527 -11.8752541135269 61 633.685 641.422577762173 -7.73757776217325 62 639.696 637.797551023606 1.89844897639414 63 659.451 647.07510917297 12.3758908270298 64 593.248 647.527074099121 -54.2790740991208 65 606.677 629.448264196804 -22.7712641968042 66 599.434 654.436775282043 -55.0027752820429 67 569.578 650.657821385403 -81.0798213854033 68 629.873 619.00160858707 10.8713914129302 69 613.438 631.181538683885 -17.7435386838851 70 604.172 651.381733389248 -47.2097333892476 71 658.328 644.071466561392 14.2565334386082 72 612.633 644.676481964009 -32.0434819640092 73 707.372 678.706635167931 28.6653648320686 74 739.77 686.786154599285 52.9838454007151 75 777.535 697.403444977011 80.1315550229886 76 685.03 691.45657114709 -6.42657114709059 77 730.234 675.060831847406 55.1731681525944 78 714.154 684.616813117607 29.5371868823935 79 630.872 691.535247981449 -60.6632479814492 80 719.492 675.43336482083 44.0586351791699 81 677.023 695.348851702982 -18.3258517029816 82 679.272 685.977410237784 -6.70541023778377 83 718.317 686.652402397938 31.6645976020622 84 645.672 684.386741572752 -38.7147415727519 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.90410130303499 0.191797393930019 0.0958986969650096 8 0.922829973568107 0.154340052863787 0.0771700264318933 9 0.8683591881383 0.263281623723398 0.131640811861699 10 0.814819350280804 0.370361299438393 0.185180649719196 11 0.810493891699103 0.379012216601793 0.189506108300897 12 0.773785114081295 0.452429771837411 0.226214885918705 13 0.76933601653897 0.461327966922059 0.230663983461030 14 0.717578306667703 0.564843386664594 0.282421693332297 15 0.687440322906146 0.625119354187707 0.312559677093854 16 0.680879656682283 0.638240686635434 0.319120343317717 17 0.61506978864384 0.76986042271232 0.38493021135616 18 0.64454594821843 0.710908103563139 0.355454051781570 19 0.612089564571423 0.775820870857154 0.387910435428577 20 0.599551084313305 0.800897831373391 0.400448915686695 21 0.587755751039009 0.824488497921982 0.412244248960991 22 0.539504057549454 0.920991884901092 0.460495942450546 23 0.474302600943533 0.948605201887067 0.525697399056467 24 0.513389696146849 0.973220607706301 0.486610303853151 25 0.536119016355027 0.927761967289945 0.463880983644973 26 0.49125876910262 0.98251753820524 0.50874123089738 27 0.434062291727917 0.868124583455835 0.565937708272083 28 0.372557658702777 0.745115317405553 0.627442341297223 29 0.375690092855118 0.751380185710235 0.624309907144882 30 0.417403452060477 0.834806904120955 0.582596547939523 31 0.361276028486682 0.722552056973363 0.638723971513318 32 0.325588646941813 0.651177293883626 0.674411353058187 33 0.297272835408469 0.594545670816939 0.70272716459153 34 0.255506891174179 0.511013782348358 0.744493108825821 35 0.21960839703603 0.43921679407206 0.78039160296397 36 0.236294082076383 0.472588164152766 0.763705917923617 37 0.246421912262525 0.492843824525049 0.753578087737475 38 0.257407989991928 0.514815979983857 0.742592010008072 39 0.207720317026389 0.415440634052779 0.79227968297361 40 0.222347611901117 0.444695223802235 0.777652388098883 41 0.177498714957938 0.354997429915876 0.822501285042062 42 0.142643594519405 0.285287189038811 0.857356405480595 43 0.116576941367062 0.233153882734123 0.883423058632938 44 0.0897143052075147 0.179428610415029 0.910285694792485 45 0.067947163259248 0.135894326518496 0.932052836740752 46 0.0494790317659736 0.0989580635319472 0.950520968234026 47 0.0449507747227705 0.089901549445541 0.95504922527723 48 0.0341844309072910 0.0683688618145821 0.965815569092709 49 0.0237558334159300 0.0475116668318601 0.97624416658407 50 0.0169677382578425 0.0339354765156850 0.983032261742158 51 0.0142664232354669 0.0285328464709339 0.985733576764533 52 0.0136591632384271 0.0273183264768541 0.986340836761573 53 0.0231855008118649 0.0463710016237298 0.976814499188135 54 0.0194633177434051 0.0389266354868102 0.980536682256595 55 0.0151592405492589 0.0303184810985178 0.984840759450741 56 0.0110177648752359 0.0220355297504717 0.988982235124764 57 0.0269439848514480 0.0538879697028959 0.973056015148552 58 0.026138578292036 0.052277156584072 0.973861421707964 59 0.017852233133316 0.035704466266632 0.982147766866684 60 0.0133898988017587 0.0267797976035175 0.986610101198241 61 0.00988980023745972 0.0197796004749194 0.99011019976254 62 0.00614593354396835 0.0122918670879367 0.993854066456032 63 0.00380435823656271 0.00760871647312542 0.996195641763437 64 0.0117354459472962 0.0234708918945923 0.988264554052704 65 0.00922723137802669 0.0184544627560534 0.990772768621973 66 0.0119795375177933 0.0239590750355867 0.988020462482207 67 0.0271098981445647 0.0542197962891295 0.972890101855435 68 0.0209701446270546 0.0419402892541093 0.979029855372945 69 0.0127909330847355 0.025581866169471 0.987209066915265 70 0.130602667387896 0.261205334775791 0.869397332612104 71 0.0956700329424083 0.191340065884817 0.904329967057592 72 0.0940966977777455 0.188193395555491 0.905903302222254 73 0.0751802579464391 0.150360515892878 0.92481974205356 74 0.0626332042075158 0.125266408415032 0.937366795792484 75 0.472613381142657 0.945226762285314 0.527386618857343 76 0.383224402790821 0.766448805581642 0.616775597209179 77 0.25722485480794 0.51444970961588 0.74277514519206 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0140845070422535 NOK 5% type I error level 18 0.253521126760563 NOK 10% type I error level 24 0.338028169014085 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/10wfwo1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/10wfwo1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/1inyf1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/1inyf1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/2inyf1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/2inyf1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/3inyf1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/3inyf1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/4bwf01292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/4bwf01292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/5bwf01292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/5bwf01292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/6bwf01292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/6bwf01292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/74oxl1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/74oxl1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/8wfwo1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/8wfwo1292698687.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/9wfwo1292698687.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292698578yd1rwxof7q9t0zg/9wfwo1292698687.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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