multiple linear 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: Mon, 14 Dec 2009 08:03:46 -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/Dec/14/t1260803101s5l7fbay7kp5700.htm/, Retrieved Mon, 14 Dec 2009 16:05:13 +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/Dec/14/t1260803101s5l7fbay7kp5700.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 «

100 0 97.56592292 0 93.71196755 0 92.69776876 0 89.65517241 0 89.04665314 0 98.98580122 0 105.6795132 0 101.6227181 0 98.37728195 0 94.11764706 0 93.30628803 0 94.72616633 0 93.30628803 0 90.87221095 0 89.85801217 0 88.43813387 0 87.42393509 0 98.17444219 0 103.4482759 0 104.0567951 0 102.0283976 0 95.53752535 0 95.53752535 0 96.55172414 0 96.34888438 0 95.3346856 0 93.50912779 0 92.29208925 0 92.49492901 0 104.8681542 0 106.4908722 0 106.0851927 0 105.2738337 0 103.2454361 0 103.8539554 0 105.2738337 0 104.8681542 0 103.4482759 0 103.2454361 0 101.6227181 0 102.8397566 0 115.4158215 0 117.6470588 0 117.2413793 0 114.6044625 0 110.9533469 0 112.5760649 0 113.9959432 0 113.7931034 0 112.5760649 0 110.3448276 0 108.9249493 0 110.1419878 0 120.4868154 0 123.9350913 0 124.3407708 0 123.9350913 0 120.4868154 0 120.6896552 0 119.8782961 0 119.4726166 0 118.4584178 0 116.2271805 0 115.010142 0 115.4158215 0 125.9634888 0 127.5862069 0 127.3833671 0 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation TW[t] = + 108.225040679560 -4.21597254897221DUM[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) 108.225040679560 1.101285 98.2716 0 0 DUM -4.21597254897221 2.775793 -1.5188 0.131781 0.065891 Multiple Linear Regression - Regression Statistics Multiple R 0.145942818764672 R-squared 0.021299306348978 Adjusted R-squared 0.0120662809371759 F-TEST (value) 2.30686101239927 F-TEST (DF numerator) 1 F-TEST (DF denominator) 106 p-value 0.131781018435829 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.5055873700832 Sum Squared Residuals 11698.9407949879 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 108.225040679560 -8.22504067956022 2 97.56592292 108.225040679560 -10.6591177595604 3 93.71196755 108.225040679560 -14.5130731295604 4 92.69776876 108.225040679560 -15.5272719195604 5 89.65517241 108.225040679560 -18.5698682695604 6 89.04665314 108.225040679560 -19.1783875395604 7 98.98580122 108.225040679560 -9.23923945956044 8 105.6795132 108.225040679560 -2.54552747956044 9 101.6227181 108.225040679560 -6.60232257956044 10 98.37728195 108.225040679560 -9.84775872956044 11 94.11764706 108.225040679560 -14.1073936195604 12 93.30628803 108.225040679560 -14.9187526495604 13 94.72616633 108.225040679560 -13.4988743495604 14 93.30628803 108.225040679560 -14.9187526495604 15 90.87221095 108.225040679560 -17.3528297295604 16 89.85801217 108.225040679560 -18.3670285095604 17 88.43813387 108.225040679560 -19.7869068095604 18 87.42393509 108.225040679560 -20.8011055895604 19 98.17444219 108.225040679560 -10.0505984895604 20 103.4482759 108.225040679560 -4.77676477956044 21 104.0567951 108.225040679560 -4.16824557956044 22 102.0283976 108.225040679560 -6.19664307956044 23 95.53752535 108.225040679560 -12.6875153295604 24 95.53752535 108.225040679560 -12.6875153295604 25 96.55172414 108.225040679560 -11.6733165395604 26 96.34888438 108.225040679560 -11.8761562995604 27 95.3346856 108.225040679560 -12.8903550795604 28 93.50912779 108.225040679560 -14.7159128895604 29 92.29208925 108.225040679560 -15.9329514295604 30 92.49492901 108.225040679560 -15.7301116695604 31 104.8681542 108.225040679560 -3.35688647956043 32 106.4908722 108.225040679560 -1.73416847956044 33 106.0851927 108.225040679560 -2.13984797956045 34 105.2738337 108.225040679560 -2.95120697956044 35 103.2454361 108.225040679560 -4.97960457956043 36 103.8539554 108.225040679560 -4.37108527956044 37 105.2738337 108.225040679560 -2.95120697956044 38 104.8681542 108.225040679560 -3.35688647956043 39 103.4482759 108.225040679560 -4.77676477956044 40 103.2454361 108.225040679560 -4.97960457956043 41 101.6227181 108.225040679560 -6.60232257956044 42 102.8397566 108.225040679560 -5.38528407956044 43 115.4158215 108.225040679560 7.19078082043957 44 117.6470588 108.225040679560 9.42201812043955 45 117.2413793 108.225040679560 9.01633862043956 46 114.6044625 108.225040679560 6.37942182043956 47 110.9533469 108.225040679560 2.72830622043956 48 112.5760649 108.225040679560 4.35102422043957 49 113.9959432 108.225040679560 5.77090252043956 50 113.7931034 108.225040679560 5.56806272043957 51 112.5760649 108.225040679560 4.35102422043957 52 110.3448276 108.225040679560 2.11978692043956 53 108.9249493 108.225040679560 0.699908620439554 54 110.1419878 108.225040679560 1.91694712043956 55 120.4868154 108.225040679560 12.2617747204396 56 123.9350913 108.225040679560 15.7100506204396 57 124.3407708 108.225040679560 16.1157301204396 58 123.9350913 108.225040679560 15.7100506204396 59 120.4868154 108.225040679560 12.2617747204396 60 120.6896552 108.225040679560 12.4646145204396 61 119.8782961 108.225040679560 11.6532554204396 62 119.4726166 108.225040679560 11.2475759204396 63 118.4584178 108.225040679560 10.2333771204396 64 116.2271805 108.225040679560 8.00213982043956 65 115.010142 108.225040679560 6.78510132043956 66 115.4158215 108.225040679560 7.19078082043957 67 125.9634888 108.225040679560 17.7384481204395 68 127.5862069 108.225040679560 19.3611662204396 69 127.3833671 108.225040679560 19.1583264204396 70 124.137931 108.225040679560 15.9128903204396 71 120.6896552 108.225040679560 12.4646145204396 72 121.0953347 108.225040679560 12.8702940204396 73 120.2839757 108.225040679560 12.0589350204396 74 119.6754564 108.225040679560 11.4504157204396 75 117.6470588 108.225040679560 9.42201812043955 76 116.4300203 108.225040679560 8.20497962043955 77 116.2271805 108.225040679560 8.00213982043956 78 116.2271805 108.225040679560 8.00213982043956 79 125.7606491 108.225040679560 17.5356084204396 80 126.9776876 108.225040679560 18.7526469204396 81 125.7606491 108.225040679560 17.5356084204396 82 119.2697769 108.225040679560 11.0447362204396 83 114.8073022 108.225040679560 6.58226152043955 84 112.9817444 108.225040679560 4.75670372043956 85 113.7931034 108.225040679560 5.56806272043957 86 111.3590264 108.225040679560 3.13398572043956 87 107.9107505 108.225040679560 -0.314290179560434 88 106.693712 108.225040679560 -1.53132867956044 89 103.6511156 108.225040679560 -4.57392507956044 90 101.2170385 108.225040679560 -7.00800217956044 91 112.5760649 108.225040679560 4.35102422043957 92 114.6044625 104.009068130588 10.5953943694118 93 109.9391481 104.009068130588 5.93007996941176 94 106.8965517 104.009068130588 2.88748356941177 95 103.4482759 104.009068130588 -0.560792230588236 96 104.2596349 104.009068130588 0.25056676941176 97 104.8681542 104.009068130588 0.859086069411772 98 103.0425963 104.009068130588 -0.966471830588235 99 100 104.009068130588 -4.00906813058823 100 99.39148073 104.009068130588 -4.61758740058824 101 95.13184584 104.009068130588 -8.87722229058824 102 96.95740365 104.009068130588 -7.05166448058823 103 107.0993915 104.009068130588 3.09032336941176 104 108.31643 104.009068130588 4.30736186941176 105 105.0709939 104.009068130588 1.06192576941177 106 102.6369168 104.009068130588 -1.37215133058824 107 101.8255578 104.009068130588 -2.18351033058824 108 104.6653144 104.009068130588 0.656246269411765 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.111150607187025 0.222301214374049 0.888849392812975 6 0.0778754645022301 0.15575092900446 0.92212453549777 7 0.0503524420776683 0.100704884155337 0.949647557922332 8 0.0996394316392779 0.199278863278556 0.900360568360722 9 0.0715043679577753 0.143008735915551 0.928495632042225 10 0.0396082358399511 0.0792164716799022 0.96039176416005 11 0.0230992723935830 0.0461985447871661 0.976900727606417 12 0.0141682668788155 0.028336533757631 0.985831733121185 13 0.00770494772899934 0.0154098954579987 0.992295052271 14 0.00465269433413644 0.00930538866827289 0.995347305665863 15 0.00397383302117515 0.0079476660423503 0.996026166978825 16 0.00406932376963677 0.00813864753927354 0.995930676230363 17 0.00549260576802562 0.0109852115360512 0.994507394231974 18 0.00891187315704231 0.0178237463140846 0.991088126842958 19 0.00671138170869376 0.0134227634173875 0.993288618291306 20 0.00968530216294463 0.0193706043258893 0.990314697837055 21 0.0133148162368308 0.0266296324736616 0.98668518376317 22 0.0129075379515245 0.0258150759030489 0.987092462048476 23 0.0101497623588406 0.0202995247176812 0.98985023764116 24 0.00822520404895581 0.0164504080979116 0.991774795951044 25 0.00667663910052197 0.0133532782010439 0.993323360899478 26 0.00564764869354987 0.0112952973870997 0.99435235130645 27 0.00525056839587225 0.0105011367917445 0.994749431604128 28 0.00614408439958262 0.0122881687991652 0.993855915600417 29 0.00930735260828972 0.0186147052165794 0.99069264739171 30 0.0154233037448002 0.0308466074896005 0.9845766962552 31 0.0279179803957637 0.0558359607915275 0.972082019604236 32 0.0530047726210713 0.106009545242143 0.946995227378929 33 0.0821215713845322 0.164243142769064 0.917878428615468 34 0.109824620856651 0.219649241713302 0.890175379143349 35 0.130618188396735 0.261236376793470 0.869381811603265 36 0.157274489073621 0.314548978147243 0.842725510926379 37 0.194809455266842 0.389618910533684 0.805190544733158 38 0.233974354611730 0.467948709223461 0.76602564538827 39 0.275443846271394 0.550887692542788 0.724556153728606 40 0.326670748102415 0.65334149620483 0.673329251897585 41 0.401041036473719 0.802082072947438 0.598958963526281 42 0.486093278533802 0.972186557067605 0.513906721466198 43 0.700221851025056 0.599556297949887 0.299778148974944 44 0.862519111531388 0.274961776937224 0.137480888468612 45 0.932858468685898 0.134283062628203 0.0671415313141017 46 0.955798771015928 0.088402457968144 0.044201228984072 47 0.964001477642495 0.0719970447150107 0.0359985223575053 48 0.9713606709732 0.0572786580536 0.0286393290268 49 0.97776464664055 0.0444707067188989 0.0222353533594494 50 0.981814284531014 0.0363714309379714 0.0181857154689857 51 0.984032484733653 0.0319350305326932 0.0159675152663466 52 0.985725500988996 0.0285489980220087 0.0142744990110044 53 0.987905516346364 0.024188967307272 0.012094483653636 54 0.989498710889224 0.0210025782215515 0.0105012891107757 55 0.993937674958334 0.0121246500833319 0.00606232504166594 56 0.997665948457272 0.00466810308545592 0.00233405154272796 57 0.999083516718713 0.00183296656257407 0.000916483281287037 58 0.999578188869848 0.000843622260303711 0.000421811130151856 59 0.99965213668806 0.000695726623881599 0.000347863311940799 60 0.99970252864884 0.000594942702321754 0.000297471351160877 61 0.99970818406221 0.000583631875577934 0.000291815937788967 62 0.999690366446541 0.000619267106917562 0.000309633553458781 63 0.999633345105493 0.000733309789014665 0.000366654894507333 64 0.999509483762479 0.000981032475042315 0.000490516237521158 65 0.99932733095004 0.00134533809992122 0.000672669049960611 66 0.999077483223382 0.00184503355323628 0.00092251677661814 67 0.999496367404139 0.00100726519172174 0.000503632595860869 68 0.99980506757467 0.000389864850662114 0.000194932425331057 69 0.999927844029219 0.000144311941562936 7.21559707814682e-05 70 0.999950150912164 9.96981756714394e-05 4.98490878357197e-05 71 0.999941288263185 0.000117423473629815 5.87117368149077e-05 72 0.99993465326581 0.000130693468381072 6.5346734190536e-05 73 0.999919858594533 0.000160282810933725 8.01414054668625e-05 74 0.999895399602487 0.000209200795025977 0.000104600397512989 75 0.999836153796936 0.000327692406126952 0.000163846203063476 76 0.999725460549012 0.000549078901976001 0.000274539450988001 77 0.999540882291307 0.00091823541738678 0.00045911770869339 78 0.999242482515125 0.00151503496974899 0.000757517484874493 79 0.999677945667131 0.000644108665737093 0.000322054332868546 80 0.999941182972477 0.000117634055045689 5.88170275228444e-05 81 0.999994126959423 1.17460811541272e-05 5.87304057706359e-06 82 0.999996951546322 6.09690735669494e-06 3.04845367834747e-06 83 0.99999580879846 8.38240308031956e-06 4.19120154015978e-06 84 0.99999270202028 1.45959594400310e-05 7.29797972001548e-06 85 0.999990765799688 1.84684006229333e-05 9.23420031146667e-06 86 0.999983897936872 3.22041262563276e-05 1.61020631281638e-05 87 0.999961052749802 7.78945003962261e-05 3.89472501981131e-05 88 0.9999046186116 0.000190762776802085 9.53813884010426e-05 89 0.999797680353257 0.00040463929348597 0.000202319646742985 90 0.99984125206827 0.000317495863460164 0.000158747931730082 91 0.999612716382154 0.000774567235692873 0.000387283617846436 92 0.999912804561855 0.000174390876289049 8.71954381445244e-05 93 0.999927967866287 0.000144064267425373 7.20321337126865e-05 94 0.999869171637157 0.000261656725685260 0.000130828362842630 95 0.999622766170313 0.00075446765937412 0.00037723382968706 96 0.99900596020185 0.00198807959630227 0.000994039798151136 97 0.997647542031561 0.00470491593687795 0.00235245796843898 98 0.993862306923246 0.0122753861535079 0.00613769307675397 99 0.98597604662599 0.028047906748019 0.0140239533740095 100 0.971366309180996 0.0572673816380082 0.0286336908190041 101 0.980756487359134 0.0384870252817313 0.0192435126408657 102 0.991694752236857 0.0166104955262855 0.00830524776314273 103 0.97398310524252 0.0520337895149612 0.0260168947574806 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 45 0.454545454545455 NOK 5% type I error level 73 0.737373737373737 NOK 10% type I error level 80 0.808080808080808 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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