*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 13:40:17 +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/t1292679544phh683zhimcijod.htm/, Retrieved Sat, 18 Dec 2010 14:39:16 +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/t1292679544phh683zhimcijod.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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13.193 651 3.063 5.951 22.858 15.234 736 3.547 6.789 26.306 14.718 878 3.240 6.302 25.138 16.961 916 3.708 6.961 28.546 13.945 724 3.337 6.162 24.168 15.876 841 4.104 7.534 28.355 16.226 1.028 4.846 7.462 29.562 18.316 994 4.590 8.894 32.794 16.748 855 3.917 7.734 29.254 17.904 889 4.376 8.968 32.137 17.209 1.117 4.312 8.383 31.021 18.950 1.132 4.941 9.790 34.813 17.225 899 4.659 9.656 32.439 18.710 944 5.227 10.440 35.321 17.236 1.167 4.933 9.820 33.156 18.687 1.089 5.381 10.947 36.104 17.580 970 5.472 10.439 34.461 19.568 1.151 6.405 12.289 39.413 17.381 1.246 5.622 11.303 35.552 19.580 1.583 6.229 12.240 39.632 17.260 1.120 5.671 11.392 35.443 18.661 1.063 5.606 11.120 36.450 15.658 1.015 4.516 9.597 30.786 18.674 1.175 5.483 10.692 36.024 15.908 882 4.985 9.217 30.992 17.475 911 5.332 9.371 33.089 17.725 1.076 5.377 9.526 33.704 19.562 1.147 5.948 10.837 37.494 16.368 946 5.308 9.749 32.371 19.555 1.032 6.721 9.939 37.247 17.743 1.090 5.840 9.309 33.982 19.867 1.131 6.152 10 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation huis[t] = + 1.32830870700764 -0.000125396004891801villa[t] -1.14040585220238app[t] -0.5865527085729grond[t] + 0.83838563386305totaal[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) 1.32830870700764 0.697729 1.9038 0.059875 0.029938 villa -0.000125396004891801 0.000209 -0.6014 0.548958 0.274479 app -1.14040585220238 0.125362 -9.0969 0 0 grond -0.5865527085729 0.097919 -5.9902 0 0 totaal 0.83838563386305 0.057185 14.6609 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.938653826466801 R-squared 0.881071005940768 Adjusted R-squared 0.87621676128529 F-TEST (value) 181.505273935117 F-TEST (DF numerator) 4 F-TEST (DF denominator) 98 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.559627104580471 Sum Squared Residuals 30.6918846257499 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13.193 13.4268564326514 -0.233856432651418 2 15.234 15.2634638355454 -0.0294638355453961 3 14.718 14.9021789481999 -0.184178948199862 4 16.961 16.834383966439 0.126616033561010 5 13.945 14.0797538796426 -0.134753879642615 6 15.876 15.8959615912536 -0.0199615912536226 7 16.226 16.2092728370304 0.0167271629696149 8 18.316 18.2464209033938 0.0695790966062449 9 16.748 16.7438600846753 0.00413991532471486 10 17.904 17.9094100743963 -0.00541007439628647 11 17.209 17.5012279970726 -0.292227997072571 12 18.95 19.1377894977438 -0.18778949774382 13 17.225 17.4350654561026 -0.21006545610258 14 18.71 18.7380421851037 -0.0280421851036539 15 17.236 17.740106779133 -0.504106779133001 16 18.687 19.0397306843013 -0.352730684301330 17 17.58 17.7349573627732 -0.154957362773234 18 19.568 19.8590116446418 -0.291011644641790 19 17.381 18.0932715526034 -0.712271552603434 20 19.58 20.2720164400914 -0.692016440091378 21 17.26 17.8938202405881 -0.633820240588071 22 18.661 18.9717504385854 -0.310750438585430 23 15.658 16.3595023814505 -0.701502381450468 24 18.674 19.0058985932973 -0.331898593297315 25 15.908 16.1097775072314 -0.201777507231434 26 17.475 17.3781857494659 0.0968142505340673 27 17.725 17.8656598154690 -0.140659815468974 28 19.562 19.6229901221470 -0.0609901221469549 29 16.368 16.5774888207934 -0.209488820793357 30 19.555 19.0611138996694 0.493886100330632 31 17.743 17.6980032943294 0.0449967056705482 32 19.867 19.6724684980521 0.194531501947936 33 15.703 15.7002706604226 0.00272933957736373 34 19.324 18.9552711637835 0.368728836216488 35 18.162 17.9329297326532 0.229070267346830 36 19.074 18.8220597031424 0.251940296857603 37 15.323 15.3628010685970 -0.0398010685970356 38 19.704 19.1953954428011 0.508604557198939 39 18.375 18.1187589489084 0.256241051091634 40 18.352 18.0188557651176 0.333144234882362 41 13.927 14.0263500701505 -0.0993500701505158 42 17.795 17.1254952608631 0.66950473913688 43 16.761 16.3267260603194 0.434273939680592 44 18.902 18.3892402755144 0.512759724485582 45 16.239 16.0506441232394 0.188355876760567 46 19.158 18.4710263731184 0.686973626881556 47 18.279 18.1737953959952 0.105204604004751 48 15.698 15.3791896322647 0.318810367735342 49 16.239 16.0506441232394 0.188355876760567 50 18.431 18.1017883001137 0.329211699886327 51 18.414 18.1363379793433 0.277662020656694 52 19.801 19.1912088268250 0.609791173174976 53 14.995 14.8888154088271 0.106184591172903 54 18.706 18.0475754040547 0.658424595945346 55 18.232 17.9461397638288 0.285860236171224 56 19.409 18.6849746014272 0.724025398572843 57 16.263 15.6832663128018 0.579733687198208 58 19.017 18.0133823782146 1.00361762178537 59 20.298 19.4663917359586 0.83160826404139 60 19.891 19.3644073476048 0.526592652395192 61 15.203 14.9368005922417 0.266199407758258 62 17.845 17.1661071950283 0.678892804971721 63 17.502 17.2967188341670 0.205281165833015 64 18.532 18.2865247268333 0.245475273166729 65 15.737 15.348201091162 0.388798908837996 66 17.77 17.0938421367633 0.676157863236666 67 17.224 16.7829128627342 0.441087137265839 68 17.601 16.8679326647200 0.733067335279951 69 14.94 14.6140753852438 0.325924614756186 70 18.507 17.6646032289256 0.842396771074423 71 17.635 16.9604476837655 0.674552316234522 72 19.392 18.4504155464415 0.941584453558456 73 15.699 14.9831319404740 0.715868059525957 74 17.661 16.6886004337720 0.972399566228036 75 18.243 17.5032452922481 0.739754707751947 76 19.643 18.9963788508385 0.646621149161541 77 15.77 15.2312404938209 0.538759506179142 78 17.344 16.7984661645291 0.545533835470918 79 17.229 17.1569361399090 0.0720638600909707 80 17.322 16.9796488007875 0.342351199212497 81 16.152 17.4310637736503 -1.27906377365030 82 17.919 19.191027413059 -1.27202741305900 83 16.918 18.1530157890922 -1.23501578909217 84 18.114 19.2343171720424 -1.12031717204238 85 16.308 17.3711989002421 -1.06319890024212 86 17.759 18.6639810380205 -0.904981038020455 87 16.021 17.0476415302056 -1.02664153020557 88 17.952 18.808047509151 -0.856047509151 89 15.954 16.5275234148985 -0.573523414898461 90 17.762 18.1015995591642 -0.339599559164231 91 16.61 17.3921096855893 -0.782109685589286 92 17.751 18.3328349688115 -0.581834968811499 93 15.458 15.7881387183709 -0.330138718370885 94 18.106 18.1481621748182 -0.0421621748182437 95 15.99 16.5505752534558 -0.560575253455763 96 15.349 15.8980145292289 -0.549014529228944 97 13.185 13.6680921740110 -0.483092174010978 98 15.409 15.8155415816334 -0.406541581633428 99 16.007 16.6639150109294 -0.65691501092935 100 16.633 17.2812296787734 -0.64822967877336 101 14.8 15.2960292854121 -0.496029285412116 102 15.974 16.3709342742188 -0.396934274218771 103 15.693 16.3535931278132 -0.660593127813212 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00265013655297554 0.00530027310595108 0.997349863447024 9 0.000267748687373009 0.000535497374746017 0.999732251312627 10 2.47908428068361e-05 4.95816856136721e-05 0.999975209157193 11 6.02088789492952e-06 1.20417757898590e-05 0.999993979112105 12 6.31509794719336e-07 1.26301958943867e-06 0.999999368490205 13 9.68699002885743e-08 1.93739800577149e-07 0.9999999031301 14 8.72023605343533e-09 1.74404721068707e-08 0.999999991279764 15 4.52152766333617e-09 9.04305532667234e-09 0.999999995478472 16 7.12757769627832e-10 1.42551553925566e-09 0.999999999287242 17 1.20243771322455e-10 2.40487542644910e-10 0.999999999879756 18 1.61000479827355e-11 3.22000959654709e-11 0.9999999999839 19 2.69849820553081e-11 5.39699641106162e-11 0.999999999973015 20 1.34491449182851e-09 2.68982898365702e-09 0.999999998655085 21 2.88141808824915e-10 5.76283617649831e-10 0.999999999711858 22 1.38489293577004e-10 2.76978587154009e-10 0.99999999986151 23 3.31550256441107e-11 6.63100512882214e-11 0.999999999966845 24 6.72730726867279e-12 1.34546145373456e-11 0.999999999993273 25 1.15277528922252e-12 2.30555057844505e-12 0.999999999998847 26 1.95829737504458e-13 3.91659475008916e-13 0.999999999999804 27 4.01402300330772e-14 8.02804600661544e-14 0.99999999999996 28 1.05356619270860e-14 2.10713238541721e-14 0.99999999999999 29 2.13289431710335e-15 4.2657886342067e-15 0.999999999999998 30 7.45318835396707e-16 1.49063767079341e-15 1 31 1.25643118955377e-16 2.51286237910753e-16 1 32 2.12338596623325e-17 4.24677193246649e-17 1 33 3.60774118078608e-18 7.21548236157216e-18 1 34 5.06218892313168e-19 1.01243778462634e-18 1 35 1.41411544785432e-19 2.82823089570863e-19 1 36 2.01750911459642e-20 4.03501822919284e-20 1 37 3.91523502022285e-21 7.83047004044569e-21 1 38 1.16989528740722e-21 2.33979057481443e-21 1 39 3.52216015766339e-22 7.04432031532679e-22 1 40 5.47805470475872e-23 1.09561094095174e-22 1 41 1.23694831288670e-23 2.47389662577341e-23 1 42 2.17065543284231e-24 4.34131086568462e-24 1 43 1 0 0 44 1 0 0 45 1 0 0 46 1 0 0 47 1 0 0 48 1 0 0 49 1 0 0 50 1 0 0 51 1 0 0 52 1 0 0 53 1 0 0 54 1 0 0 55 1 0 0 56 1 0 0 57 1 0 0 58 1 0 0 59 1 0 0 60 1 0 0 61 1 0 0 62 1 0 0 63 1 0 0 64 1 0 0 65 1 0 0 66 1 0 0 67 1 0 0 68 1 0 0 69 1 0 0 70 1 0 0 71 1 0 0 72 1 0 0 73 1 0 0 74 1 3.92014607661160e-314 1.96007303830580e-314 75 1 6.73157875338134e-315 3.36578937669067e-315 76 1 3.71738165969472e-302 1.85869082984736e-302 77 1 1.96724645667886e-281 9.83623228339429e-282 78 1 1.33377432866553e-271 6.66887164332764e-272 79 1 6.43260082272328e-259 3.21630041136164e-259 80 1 1.03601701531167e-242 5.18008507655833e-243 81 1 9.33279402550087e-236 4.66639701275043e-236 82 1 5.25160055243373e-224 2.62580027621687e-224 83 1 2.57704604058292e-211 1.28852302029146e-211 84 1 1.40183266843630e-191 7.00916334218149e-192 85 1 1.17497487769966e-182 5.8748743884983e-183 86 1 3.15951328071984e-165 1.57975664035992e-165 87 1 1.52798413515819e-150 7.63992067579093e-151 88 1 5.27140623383767e-135 2.63570311691884e-135 89 1 7.27605060704266e-129 3.63802530352133e-129 90 1 1.37429415941067e-112 6.87147079705333e-113 91 1 3.34268964077398e-100 1.67134482038699e-100 92 1 2.07712183613357e-86 1.03856091806678e-86 93 1 5.11925989599226e-72 2.55962994799613e-72 94 1 8.26535932734032e-55 4.13267966367016e-55 95 1 1.38140629261640e-41 6.90703146308201e-42 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 88 1 NOK 5% type I error level 88 1 NOK 10% type I error level 88 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/10j2uw1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/10j2uw1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/1ksyh1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/1ksyh1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/25ae51292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/25ae51292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/35ae51292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/35ae51292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/4ykdq1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/4ykdq1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/5ykdq1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/5ykdq1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/6ykdq1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/6ykdq1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/7rbdt1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/7rbdt1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/8rbdt1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/8rbdt1292679608.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/9j2uw1292679608.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292679544phh683zhimcijod/9j2uw1292679608.ps (open in new window)

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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