Workshop 7

*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: Fri, 20 Nov 2009 08:45:42 -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/20/t12587320215dmk3y7hl3iu5a9.htm/, Retrieved Fri, 20 Nov 2009 16:47:14 +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/20/t12587320215dmk3y7hl3iu5a9.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 «

33 62 39 64 45 62 46 64 45 64 45 69 49 69 50 65 54 56 59 58 58 53 56 62 48 55 50 60 52 59 53 58 55 53 43 57 42 57 38 53 41 54 41 53 39 57 34 57 27 55 15 49 14 50 31 49 41 54 43 58 46 58 42 52 45 56 45 52 40 59 35 53 36 52 38 53 39 51 32 50 24 56 21 52 12 46 29 48 36 46 31 48 28 48 30 49 38 53 27 48 40 51 40 48 44 50 47 55 45 52 42 53 38 52 46 55 37 53 41 53 40 56 33 54 34 52 36 55 36 54 38 59 42 56 35 56 25 51 24 53 22 52 27 51 17 46 30 49 30 46 34 55 37 57 36 53 33 52 33 53 33 50 37 54 40 53 35 50 37 51 43 52 42 47 33 51 39 49 40 53 37 52 44 45 42 53 43 51 40 48 30 48 30 48 31 48 18 40 24 43 22 40 26 39 28 39 23 36 17 41 12 39 9 40 19 39 21 46 18 40 18 37 15 37 24 44 18 41 19 40 30 36 33 38 35 43 36 42 47 45 46 46

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 Spaar[t] = + 35.2908642381885 + 0.435286320955128Alg_E[t] + 1.76715415124219M1[t] + 2.30585896286539M2[t] -0.242370093898710M3[t] + 0.735286320955132M4[t] + 0.834127782758336M5[t] + 2.76941410371346M6[t] + 1.44352863209552M7[t] -1.52234358514615M8[t] -1.43528632095512M9[t] -0.92704400981474M10[t] + 0.0176431604775675M11[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) 35.2908642381885 2.323913 15.186 0 0 Alg_E 0.435286320955128 0.045622 9.5412 0 0 M1 1.76715415124219 2.309845 0.7651 0.445909 0.222954 M2 2.30585896286539 2.367137 0.9741 0.332177 0.166089 M3 -0.242370093898710 2.365307 -0.1025 0.918575 0.459287 M4 0.735286320955132 2.363617 0.3111 0.756336 0.378168 M5 0.834127782758336 2.363921 0.3529 0.724883 0.362441 M6 2.76941410371346 2.363217 1.1719 0.243823 0.121911 M7 1.44352863209552 2.363181 0.6108 0.542588 0.271294 M8 -1.52234358514615 2.363811 -0.644 0.520927 0.260464 M9 -1.43528632095512 2.363617 -0.6072 0.544965 0.272483 M10 -0.92704400981474 2.364766 -0.392 0.695813 0.347907 M11 0.0176431604775675 2.363287 0.0075 0.994057 0.497029 Multiple Linear Regression - Regression Statistics Multiple R 0.687725442575715 R-squared 0.472966284365964 Adjusted R-squared 0.414406982628848 F-TEST (value) 8.07670635297544 F-TEST (DF numerator) 12 F-TEST (DF denominator) 108 p-value 1.25376264925592e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.28422435604139 Sum Squared Residuals 3015.68692085796 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 62 51.4224669809499 10.5775330190501 2 64 54.5728897183039 9.42711028169615 3 62 54.6363785872705 7.36362141272949 4 64 56.0493213230795 7.95067867692052 5 64 55.7128764639276 8.28712353607242 6 69 57.6481627848827 11.3518372151173 7 69 58.0634225970853 10.9365774029147 8 65 55.5328367007987 9.46716329920128 9 56 57.3610392488103 -1.36103924881026 10 58 60.0457131647263 -2.04571316472628 11 53 60.5551140140635 -7.55511401406346 12 62 59.6668982116756 2.33310178832436 13 55 57.9517617952768 -2.95176179527681 14 60 59.3610392488103 0.638960751189743 15 59 57.6833828339564 1.31661716604359 16 58 59.0963255697654 -1.09632556976538 17 53 60.0657396734789 -7.06573967347885 18 57 56.7775901429724 0.222409857027564 19 57 55.0164183503994 1.98358164960064 20 53 50.3094008493372 2.69059915066282 21 54 51.7023170763936 2.29768292360641 22 53 52.210559387534 0.789440612466026 23 57 52.284673915916 4.71532608408397 24 57 50.0905991506628 6.90940084933718 25 55 48.8107490552191 6.18925094478089 26 49 44.1260180153808 4.87398198461923 27 50 41.1425026376615 8.85749736233846 28 49 49.5200265087526 -0.520026508752561 29 54 53.971731180107 0.0282688198929497 30 58 56.7775901429724 1.22240985702756 31 58 56.7575636342199 1.24243636578013 32 52 52.0505461331577 -0.0505461331576936 33 56 53.4434623602141 2.55653763978590 34 52 53.9517046713545 -1.95170467135449 35 59 52.7199602368712 6.28003976312884 36 53 50.525885471618 2.47411452838206 37 52 52.7283259438153 -0.728325943815265 38 53 54.1376033973487 -1.13760339734872 39 51 52.0246606615397 -1.02466066153974 40 50 49.9553128297077 0.0446871702923097 41 56 46.5718637238699 9.42813627613013 42 52 47.2012910819596 4.79870891804039 43 46 41.9578287217455 4.04217127825449 44 48 46.391823960741 1.60817603925898 45 46 49.525885471618 -3.52588547161795 46 48 47.8576961779827 0.142303822017309 47 48 47.4965243854096 0.503475614590386 48 49 48.3494538668423 0.650546133157697 49 53 53.5988985857255 -0.598898585725522 50 48 49.3494538668423 -1.34945386684231 51 51 52.4599469824949 -1.45994698249487 52 48 53.4376033973487 -5.43760339734872 53 50 55.2775901429724 -5.27759014297243 54 55 58.518735426793 -3.51873542679295 55 52 56.3222773132647 -4.32227731326474 56 53 52.0505461331577 0.949453866842307 57 52 50.3964581135282 1.60354188647180 58 55 54.3869909923096 0.613009007690384 59 53 51.4141012740058 1.58589872599423 60 53 53.1376033973487 -0.137603397348714 61 56 54.4694712276358 1.53052877236422 62 54 51.9611717925731 2.03882820742692 63 52 49.8482290567641 2.15177094323590 64 55 51.6964581135282 3.3035418864718 65 54 51.7952995753314 2.20470042466859 66 59 54.6011585381968 4.3988414618032 67 56 55.0164183503994 0.98358164960064 68 56 49.0035418864718 6.9964581135282 69 51 44.7377359411115 6.26226405888846 70 53 44.8106919312968 8.1893080687032 71 52 44.8848064596788 7.11519354032115 72 51 47.0435949039769 3.95640509602308 73 46 44.4578858456678 1.54211415433218 74 49 50.6553128297077 -1.65531282970769 75 46 48.1070837729436 -2.10708377294359 76 55 50.825885471618 4.17411452838205 77 57 52.2305858962865 4.76941410371346 78 53 53.7305858962865 -0.730585896286538 79 52 51.0988414618032 0.901158538196794 80 53 48.1329692445615 4.86703075543846 81 50 48.2200265087526 1.77997349124744 82 54 50.4694141037135 3.53058589628654 83 53 52.7199602368712 0.280039763128846 84 50 50.525885471618 -0.525885471617945 85 51 53.1636122647704 -2.16361226477039 86 52 56.3140350021244 -4.31403500212436 87 47 53.3305196244051 -6.33051962440513 88 51 50.3905991506628 0.609400849337182 89 49 53.1011585381968 -4.10115853819679 90 53 55.471731180107 -2.47173118010705 91 52 52.8399867456237 -0.839986745623718 92 45 52.921118775068 -7.92111877506795 93 53 52.1376033973487 0.862396602651282 94 51 53.0811320294442 -2.08113202944423 95 48 52.7199602368712 -4.71996023687116 96 48 48.3494538668423 -0.349453866842303 97 48 50.1166080180845 -2.11660801808450 98 48 51.0905991506628 -3.09059915066282 99 40 42.883647921482 -2.88364792148205 100 43 46.4730222620667 -3.47302226206666 101 40 45.7012910819596 -5.70129108195961 102 39 49.3777226867353 -10.3777226867353 103 39 48.9224098570276 -9.92240985702757 104 36 43.7801060350103 -7.78010603501026 105 41 41.2554453734705 -0.25544537347051 106 39 39.5872560798353 -0.587256079835253 107 40 39.2260842872622 0.773915712737824 108 39 43.5613043363359 -4.56130433633589 109 46 46.1990311294883 -0.199031129488339 110 40 45.4318769782462 -5.43187697824615 111 37 42.883647921482 -5.88364792148205 112 37 42.5554453734705 -5.55544537347051 113 44 46.5718637238699 -2.57186372386987 114 41 45.8954321190942 -4.89543211909423 115 40 45.0048329684314 -5.00483296843141 116 36 46.8271102816962 -10.8271102816962 117 38 48.2200265087526 -10.2200265087526 118 43 49.5988414618032 -6.5988414618032 119 42 50.9788149530506 -8.97881495305064 120 45 55.7493213230795 -10.7493213230795 121 46 57.0811891533665 -11.0811891533665 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0215281595872369 0.0430563191744739 0.978471840412763 17 0.0753649118657835 0.150729823731567 0.924635088134216 18 0.537080962044183 0.925838075911634 0.462919037955817 19 0.846534212955477 0.306931574089046 0.153465787044523 20 0.941634075933623 0.116731848132755 0.0583659240663773 21 0.911518476789634 0.176963046420733 0.0884815232103664 22 0.883332295600077 0.233335408799845 0.116667704399923 23 0.850367863755797 0.299264272488406 0.149632136244203 24 0.830686058751619 0.338627882496762 0.169313941248381 25 0.798725423309124 0.402549153381753 0.201274576690876 26 0.846187029933002 0.307625940133996 0.153812970066998 27 0.842303785643096 0.315392428713809 0.157696214356904 28 0.848258329066683 0.303483341866635 0.151741670933317 29 0.803440160880296 0.393119678239407 0.196559839119704 30 0.778175876571309 0.443648246857383 0.221824123428691 31 0.75800148853567 0.483997022928661 0.241998511464330 32 0.750358392675863 0.499283214648273 0.249641607324136 33 0.700676018486608 0.598647963026784 0.299323981513392 34 0.644710410473142 0.710579179053716 0.355289589526858 35 0.660919993860654 0.678160012278692 0.339080006139346 36 0.627310578064871 0.745378843870259 0.372689421935129 37 0.605439424340019 0.789121151319962 0.394560575659981 38 0.586797044946525 0.82640591010695 0.413202955053475 39 0.585520784920165 0.82895843015967 0.414479215079835 40 0.544591396899107 0.910817206201785 0.455408603100893 41 0.589094323610838 0.821811352778323 0.410905676389162 42 0.58015280996375 0.839694380072501 0.419847190036250 43 0.595377835379653 0.809244329240694 0.404622164620347 44 0.56966139421668 0.86067721156664 0.43033860578332 45 0.564150190646961 0.871699618706078 0.435849809353039 46 0.5039092655859 0.9921814688282 0.4960907344141 47 0.452861276061377 0.905722552122753 0.547138723938623 48 0.423688923409706 0.847377846819411 0.576311076590294 49 0.384136647218537 0.768273294437074 0.615863352781463 50 0.367662611478668 0.735325222957336 0.632337388521332 51 0.347809023221832 0.695618046443665 0.652190976778168 52 0.369238055865643 0.738476111731285 0.630761944134357 53 0.39221818487116 0.78443636974232 0.60778181512884 54 0.388601248369603 0.777202496739205 0.611398751630397 55 0.400568431694039 0.801136863388078 0.599431568305961 56 0.361079214062106 0.722158428124212 0.638920785937894 57 0.311029418272298 0.622058836544595 0.688970581727702 58 0.263410277836013 0.526820555672026 0.736589722163987 59 0.222400584482486 0.444801168964972 0.777599415517514 60 0.190628442031682 0.381256884063364 0.809371557968318 61 0.16377770355597 0.32755540711194 0.83622229644403 62 0.145104542687146 0.290209085374292 0.854895457312854 63 0.134244901699798 0.268489803399595 0.865755098300202 64 0.117475653282304 0.234951306564609 0.882524346717696 65 0.0979778942167155 0.195955788433431 0.902022105783284 66 0.108316652737643 0.216633305475287 0.891683347262357 67 0.095586590916778 0.191173181833556 0.904413409083222 68 0.152704938720622 0.305409877441245 0.847295061279378 69 0.165635005360113 0.331270010720225 0.834364994639887 70 0.21502432175142 0.43004864350284 0.78497567824858 71 0.271624499700861 0.543248999401722 0.728375500299139 72 0.290376050161236 0.580752100322472 0.709623949838764 73 0.283743771838738 0.567487543677476 0.716256228161262 74 0.259178253678667 0.518356507357334 0.740821746321333 75 0.248888279574663 0.497776559149325 0.751111720425337 76 0.261061356270652 0.522122712541304 0.738938643729348 77 0.319179965900614 0.638359931801228 0.680820034099386 78 0.320820220651406 0.641640441302813 0.679179779348594 79 0.32770133396126 0.65540266792252 0.67229866603874 80 0.597990322886762 0.804019354226476 0.402009677113238 81 0.587534034128232 0.824931931743536 0.412465965871768 82 0.631577831669849 0.736844336660302 0.368422168330151 83 0.634405938236264 0.731188123527473 0.365594061763736 84 0.635297807582124 0.729404384835753 0.364702192417876 85 0.59910949022356 0.801781019552879 0.400890509776440 86 0.553680484207917 0.892639031584167 0.446319515792083 87 0.528734672265628 0.942530655468744 0.471265327734372 88 0.55068572423501 0.89862855152998 0.44931427576499 89 0.512009055386089 0.975981889227822 0.487990944613911 90 0.586747295839791 0.826505408320418 0.413252704160209 91 0.712893593687297 0.574212812625406 0.287106406312703 92 0.757642010672073 0.484715978655855 0.242357989327927 93 0.905560761014662 0.188878477970676 0.094439238985338 94 0.943557707096247 0.112884585807506 0.0564422929037531 95 0.952309781637032 0.0953804367259361 0.0476902183629681 96 0.976323927866945 0.0473521442661108 0.0236760721330554 97 0.966631874866897 0.0667362502662068 0.0333681251331034 98 0.984040688913474 0.0319186221730528 0.0159593110865264 99 0.978600886853106 0.0427982262937872 0.0213991131468936 100 0.986402074840578 0.0271958503188436 0.0135979251594218 101 0.98690495270345 0.0261900945930983 0.0130950472965491 102 0.982627543016158 0.0347449139676837 0.0173724569838418 103 0.967811432671227 0.0643771346575469 0.0321885673287735 104 0.927446662384926 0.145106675230148 0.0725533376150739 105 0.937689176896963 0.124621646206074 0.0623108231030372 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 7 0.0777777777777778 NOK 10% type I error level 10 0.111111111111111 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly 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') }

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