paper - multiple regression - leermogelijkheden

*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, 19 Dec 2010 10:19:28 +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/19/t1292753853a220imer49u1pb6.htm/, Retrieved Sun, 19 Dec 2010 11:17:33 +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/19/t1292753853a220imer49u1pb6.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:

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46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Leermogelijkheden[t] = + 10.4030996633557 + 0.271109839294935Carrièremogelijkheden[t] -0.0155622357020903Geen_Motivatie[t] + 0.622975722980002Persoonlijke_redenen[t] -0.0921577738086978Ouders[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) 10.4030996633557 5.990108 1.7367 0.084622 0.042311 Carrièremogelijkheden 0.271109839294935 0.101645 2.6672 0.008543 0.004271 Geen_Motivatie -0.0155622357020903 0.208191 -0.0747 0.94052 0.47026 Persoonlijke_redenen 0.622975722980002 0.099982 6.2309 0 0 Ouders -0.0921577738086978 0.104029 -0.8859 0.377188 0.188594 Multiple Linear Regression - Regression Statistics Multiple R 0.545772649084669 R-squared 0.297867784488897 Adjusted R-squared 0.277949140077234 F-TEST (value) 14.9542196915014 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 3.27302074332181e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.65177907022895 Sum Squared Residuals 6238.70923667816 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 52 36.7807076780796 15.2192923219204 2 39 36.3994611740857 2.60053882591434 3 42 36.7734664549446 5.22653354505537 4 35 38.3622880068664 -3.36228800686641 5 32 38.7376321145449 -6.73763211454485 6 49 46.6998906486214 2.30010935137861 7 33 40.1661572180902 -7.1661572180902 8 47 37.7825132498654 9.2174867501346 9 46 37.3396925682193 8.66030743178073 10 40 37.7424056652941 2.25759433470587 11 33 41.6132001267104 -8.61320012671036 12 39 39.9501889883772 -0.950188988377244 13 37 35.5079281179708 1.49207188202915 14 56 47.5898157046128 8.4101842953872 15 36 38.7315938672575 -2.73159386725745 16 24 39.4923469462766 -15.4923469462766 17 56 43.0830357906746 12.9169642093254 18 32 43.679047548534 -11.6790475485340 19 41 39.0033784717086 1.99662152829137 20 24 33.3688350481961 -9.36883504819609 21 42 37.1009228407324 4.89907715926764 22 47 40.8403832176726 6.15961678232741 23 25 29.4761709304864 -4.47617093048643 24 33 36.8516699931746 -3.85166999317464 25 43 40.1864188742602 2.81358112573976 26 45 44.7274039764541 0.272596023545876 27 44 38.8536731366227 5.14632686337731 28 46 36.4949668769866 9.50503312301338 29 31 32.9840901385186 -1.98409013851858 30 31 38.9380507731119 -7.93805077311194 31 42 39.283348237755 2.71665176224497 32 28 41.3133844322627 -13.3133844322627 33 38 35.8693179900706 2.1306820099294 34 59 48.911834477988 10.0881655220120 35 43 43.6669710539591 -0.666971053959148 36 29 42.5150551228117 -13.5150551228117 37 38 41.2929869251209 -3.29298692512086 38 39 43.0379677871848 -4.03796778718477 39 50 41.8464724821575 8.15352751784254 40 44 39.0540898341266 4.94591016587341 41 29 34.2606251806352 -5.26062518063517 42 29 30.4880394048713 -1.48803940487125 43 36 41.2791665794508 -5.27916657945082 44 43 37.5281686221202 5.47183137787981 45 28 39.5293611494402 -11.5293611494402 46 39 33.975168746042 5.02483125395801 47 35 32.5808507918155 2.41914920818449 48 43 45.2002693400724 -2.20026934007243 49 28 30.0959300856431 -2.09593008564309 50 49 41.4880294370006 7.51197056299943 51 33 39.3742923322231 -6.37429233222307 52 39 38.686122800555 0.313877199444978 53 36 37.5772612809217 -1.57726128092173 54 24 40.2121905670263 -16.2121905670263 55 47 41.4854876343335 5.51451236566653 56 34 37.8883555183723 -3.88835551837231 57 33 40.1881500607993 -7.18815006079926 58 43 41.4066093309472 1.59339066905278 59 41 42.5585252620946 -1.55852526209465 60 40 35.145579114215 4.85442088578503 61 39 43.6233777282606 -4.6233777282606 62 54 44.1822267674284 9.81777323257164 63 43 38.5353376760371 4.46466232396293 64 45 42.3546228234635 2.64537717653653 65 29 36.0368683261383 -7.03686832613827 66 45 43.2829135739941 1.7170864260059 67 47 41.6508910560933 5.34910894390667 68 38 34.4982065578938 3.50179344210616 69 52 46.2570699669753 5.74293003302474 70 34 36.4537921675396 -2.45379216753955 71 56 46.8131206947282 9.1868793052718 72 26 34.7176712322271 -8.7176712322271 73 42 44.0869882767681 -2.08698827676815 74 32 31.6532348064411 0.346765193558874 75 39 40.4682556778175 -1.46825567781749 76 37 41.1894274218936 -4.18942742189359 77 37 41.5430351956107 -4.54303519561071 78 52 47.3880601802894 4.61193981971056 79 31 34.8775626563278 -3.87756265632776 80 34 29.0729296227202 4.92707037727982 81 38 41.0415871632553 -3.04158716325533 82 29 33.0993332090246 -4.09933320902455 83 52 39.0277899306692 12.9722100693308 84 40 39.5527161870179 0.447283812982083 85 47 38.6975245299736 8.30247547002641 86 34 42.6544385179111 -8.6544385179111 87 37 42.479108044524 -5.47910804452398 88 43 39.3198254877973 3.68017451220266 89 37 37.0924659771935 -0.0924659771934753 90 55 43.9769855019777 11.0230144980223 91 36 39.9459052956781 -3.9459052956781 92 28 35.5059145259951 -7.50591452599514 93 47 42.5712765218257 4.42872347817429 94 38 40.7931575963819 -2.7931575963819 95 37 39.0295318207012 -2.02953182070121 96 32 35.6423403906586 -3.64234039065863 97 47 38.3126564338805 8.68734356611954 98 40 34.0992616073829 5.90073839261705 99 45 41.9643912452392 3.03560875476082 100 37 45.2481590230263 -8.24815902302633 101 38 42.6041321797689 -4.6041321797689 102 37 33.8607483886438 3.13925161135624 103 35 37.6405880520989 -2.64058805209892 104 50 44.7616091647098 5.23839083529018 105 32 31.0470242950108 0.952975704989153 106 32 41.830382035764 -9.83038203576398 107 38 40.3468511736085 -2.34685117360849 108 31 37.4448707750630 -6.44487077506296 109 27 36.2552532110395 -9.25525321103953 110 34 27.8362324242944 6.16376757570562 111 43 42.2486447039847 0.751355296015272 112 28 38.8004092680446 -10.8004092680446 113 44 49.014719216059 -5.01471921605904 114 43 40.4594064545591 2.54059354544095 115 53 44.3916285391625 8.60837146083747 116 33 38.6490959328353 -5.64909593283527 117 36 36.5179188513518 -0.517918851351792 118 46 41.4443002603302 4.55569973966981 119 36 38.2204986600718 -2.22049866007176 120 24 32.5658274702978 -8.56582747029783 121 50 45.1828293633665 4.81717063663355 122 40 31.433634281136 8.56636571886398 123 40 44.9972981753452 -4.99729817534521 124 32 40.4443724295484 -8.44437242954835 125 49 45.2223980337533 3.77760196624669 126 47 41.7387524726467 5.26124752735333 127 28 32.7963015143301 -4.79630151433012 128 41 40.8022765605208 0.197723439479178 129 25 41.1685017040603 -16.1685017040603 130 46 33.7872335673887 12.2127664326113 131 53 41.5623529133206 11.4376470866794 132 34 35.6459619827577 -1.64596198275774 133 40 39.263086581585 0.736913418415 134 46 36.1069081097591 9.89309189024088 135 38 40.2367145089094 -2.23671450890941 136 51 42.7656442685492 8.23435573145083 137 38 44.6317604615181 -6.63176046151815 138 45 41.7119243584979 3.28807564150215 139 41 38.1169391568445 2.8830608431555 140 42 38.3019294696181 3.69807053038186 141 36 41.9816827064171 -5.98168270641711 142 41 35.8907826220883 5.10921737791174 143 35 32.4903117216232 2.50968827837676 144 42 34.3120132692726 7.68798673072744 145 35 30.5257410377473 4.47425896225274 146 32 38.2055985249697 -6.2055985249697 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.839112873939943 0.321774252120114 0.160887126060057 9 0.844127202537948 0.311745594924104 0.155872797462052 10 0.776027391841235 0.447945216317531 0.223972608158765 11 0.90314079270318 0.193718414593638 0.0968592072968192 12 0.849358386979387 0.301283226041226 0.150641613020613 13 0.781836059205658 0.436327881588683 0.218163940794342 14 0.85796674335162 0.284066513296759 0.142033256648379 15 0.834027683800096 0.331944632399808 0.165972316199904 16 0.907601203417603 0.184797593164794 0.0923987965823972 17 0.952268642408198 0.0954627151836038 0.0477313575918019 18 0.982348159137068 0.0353036817258641 0.0176518408629321 19 0.972995275203906 0.0540094495921886 0.0270047247960943 20 0.97496654583718 0.0500669083256407 0.0250334541628204 21 0.981819586298998 0.0363608274020049 0.0181804137010024 22 0.980507967554925 0.038984064890151 0.0194920324450755 23 0.973440290202044 0.0531194195959122 0.0265597097979561 24 0.965004901379928 0.069990197240145 0.0349950986200725 25 0.95234278584376 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0.0812342599991839 43 0.913885196589519 0.172229606820963 0.0861148034104813 44 0.909062541736454 0.181874916527093 0.0909374582635463 45 0.941206637921384 0.117586724157231 0.0587933620786157 46 0.933283214947605 0.133433570104790 0.0667167850523952 47 0.92197906780372 0.156041864392560 0.0780209321962802 48 0.903498040864788 0.193003918270424 0.0965019591352118 49 0.882557198131621 0.234885603736758 0.117442801868379 50 0.893002295323816 0.213995409352367 0.106997704676183 51 0.890784865444523 0.218430269110954 0.109215134555477 52 0.865310459233554 0.269379081532892 0.134689540766446 53 0.839074086708307 0.321851826583387 0.160925913291693 54 0.932201463444628 0.135597073110745 0.0677985365553725 55 0.92454732203224 0.150905355935519 0.0754526779677596 56 0.91199214966418 0.176015700671641 0.0880078503358206 57 0.920799297554525 0.15840140489095 0.079200702445475 58 0.90185498732353 0.19629002535294 0.09814501267647 59 0.8806532598148 0.2386934803704 0.1193467401852 60 0.878743618354252 0.242512763291495 0.121256381645748 61 0.867902191884724 0.264195616230553 0.132097808115276 62 0.893444593208696 0.213110813582608 0.106555406791304 63 0.879130358639363 0.241739282721273 0.120869641360637 64 0.856478519280811 0.287042961438377 0.143521480719189 65 0.861797839755658 0.276404320488685 0.138202160244342 66 0.835078143295423 0.329843713409154 0.164921856704577 67 0.822075814209852 0.355848371580295 0.177924185790148 68 0.798961955466412 0.402076089067176 0.201038044533588 69 0.785644745749706 0.428710508500587 0.214355254250294 70 0.752833225658372 0.494333548683256 0.247166774341628 71 0.783553431747064 0.432893136505871 0.216446568252935 72 0.804052159735852 0.391895680528295 0.195947840264148 73 0.773499567855797 0.453000864288406 0.226500432144203 74 0.740529199788964 0.518941600422072 0.259470800211036 75 0.702150892085347 0.595698215829305 0.297849107914653 76 0.678934720369498 0.642130559261003 0.321065279630502 77 0.654617846133771 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0.944560496260727 0.527719751869637 113 0.460562072850705 0.92112414570141 0.539437927149295 114 0.405142613933142 0.810285227866284 0.594857386066858 115 0.419596137452642 0.839192274905284 0.580403862547358 116 0.407062606186346 0.814125212372692 0.592937393813654 117 0.362744982667673 0.725489965335347 0.637255017332327 118 0.322415790929765 0.64483158185953 0.677584209070235 119 0.280075610562519 0.560151221125038 0.719924389437481 120 0.332847692496639 0.665695384993278 0.667152307503361 121 0.298748659162372 0.597497318324745 0.701251340837628 122 0.302743048846966 0.605486097693931 0.697256951153034 123 0.262669436012777 0.525338872025554 0.737330563987223 124 0.283237851123804 0.566475702247609 0.716762148876195 125 0.246486626005854 0.492973252011707 0.753513373994146 126 0.218702899792877 0.437405799585753 0.781297100207123 127 0.274153089980625 0.54830617996125 0.725846910019375 128 0.212022980223373 0.424045960446746 0.787977019776627 129 0.706221131091432 0.587557737817136 0.293778868908568 130 0.765217866344997 0.469564267310007 0.234782133655003 131 0.938225787116426 0.123548425767148 0.0617742128835742 132 0.930406273967386 0.139187452065228 0.0695937260326142 133 0.883294593030893 0.233410813938215 0.116705406969107 134 0.917292417997833 0.165415164004333 0.0827075820021667 135 0.972299145623496 0.0554017087530076 0.0277008543765038 136 0.97320148264149 0.0535970347170197 0.0267985173585098 137 0.985067687221811 0.0298646255563772 0.0149323127781886 138 0.949573572588046 0.100852854823907 0.0504264274119537 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 4 0.0305343511450382 OK 10% type I error level 13 0.099236641221374 OK

Charts produced by software:

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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') }

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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