*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 14:21:20 +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/t12926819876o1s6mqxn9ut78d.htm/, Retrieved Sat, 18 Dec 2010 15:19:50 +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/t12926819876o1s6mqxn9ut78d.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 15.234 736 3.547 6.789 14.718 878 3.240 6.302 16.961 916 3.708 6.961 13.945 724 3.337 6.162 15.876 841 4.104 7.534 16.226 1.028 4.846 7.462 18.316 994 4.590 8.894 16.748 855 3.917 7.734 17.904 889 4.376 8.968 17.209 1.117 4.312 8.383 18.950 1.132 4.941 9.790 17.225 899 4.659 9.656 18.710 944 5.227 10.440 17.236 1.167 4.933 9.820 18.687 1.089 5.381 10.947 17.580 970 5.472 10.439 19.568 1.151 6.405 12.289 17.381 1.246 5.622 11.303 19.580 1.583 6.229 12.240 17.260 1.120 5.671 11.392 18.661 1.063 5.606 11.120 15.658 1.015 4.516 9.597 18.674 1.175 5.483 10.692 15.908 882 4.985 9.217 17.475 911 5.332 9.371 17.725 1.076 5.377 9.526 19.562 1.147 5.948 10.837 16.368 946 5.308 9.749 19.555 1.032 6.721 9.939 17.743 1.090 5.840 9.309 19.867 1.131 6.152 10.316 15.703 870 5.184 8.546 19.324 1.113 6.610 9.885 18.162 1.172 6.417 9.266 19.074 1.147 6.529 9.978 15.323 891 5.412 8.685 19.704 1.036 6.807 10.066 18.375 1.204 6.817 9.668 18.352 1.055 6.582 9.562 13.927 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation huis[t] = + 7.5369819904055 -7.66779956071819e-05villa[t] + 0.574876723257612app[t] + 0.751657733709431grond[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) 7.5369819904055 0.985884 7.6449 0 0 villa -7.66779956071819e-05 0.000371 -0.2069 0.836537 0.418268 app 0.574876723257612 0.080052 7.1813 0 0 grond 0.751657733709431 0.063023 11.9267 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.78754451930136 R-squared 0.62022636988161 Adjusted R-squared 0.60871807805984 F-TEST (value) 53.8938688284192 F-TEST (DF numerator) 3 F-TEST (DF denominator) 99 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.994976243304446 Sum Squared Residuals 98.0077947492827 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13.193 13.7210271919081 -0.52802719190811 2 15.234 14.6226390771867 0.611360922813311 3 14.718 14.0692063314539 0.648793668546111 4 16.961 14.8306773206199 2.1303226793801 5 13.945 14.0315457022141 -0.0865457022140636 6 15.876 15.494779234116 0.381220765884049 7 16.226 15.9316257752722 0.294374224727824 8 18.316 16.7846921061361 1.53130789386392 9 16.748 15.5365353416702 1.21146465832984 10 17.904 16.7253423491922 1.1786576508078 11 17.209 16.3169115534574 0.892088446542613 12 18.95 17.7360902935457 1.21390970645434 13 17.225 17.4044062027101 -0.17940620271012 14 18.71 18.3167853349463 0.393214665053685 15 17.236 17.754038328041 -0.518038328041036 16 18.687 18.8587073468346 -0.171707346834631 17 17.58 18.4548848465249 -0.874884846524936 18 19.568 20.4561010360528 -0.888101036052755 19 17.381 19.264830751895 -1.88383075189496 20 19.58 20.3180583789136 -0.738058378913554 21 17.26 19.3599069110622 -2.09990691106217 22 18.661 19.1180933911272 -0.457093391127211 23 15.658 17.3467067148807 -1.68870671488074 24 18.674 18.7256654562034 -0.0516654562033823 25 15.908 17.263141795319 -1.35514179531899 26 17.475 17.576155647408 -0.101155647408019 27 17.725 17.7883031971544 -0.063303197154442 28 19.562 19.1019756508899 0.460024349110085 29 16.368 17.8438014995458 -1.47580149954575 30 19.555 18.8713755310665 0.683624468933524 31 17.743 17.8913603183158 -0.148360318315834 32 19.867 18.8276380500198 1.03936194998021 33 15.703 16.8741000598755 -1.17110005987551 34 19.324 18.7669684862469 0.557031513753073 35 18.162 18.1907366174903 -0.0287366174903309 36 19.074 18.7903050338462 0.283694966153814 37 15.323 17.1080421398561 -1.7850421398561 38 19.704 19.0162751547357 0.687724845264253 39 18.375 18.7228512620487 -0.347851262048706 40 18.352 18.5080909373313 -0.156090937331313 41 13.927 16.2967556797246 -2.36975567972456 42 17.795 16.8518787083162 0.943121291683846 43 16.761 16.358192275199 0.402807724800996 44 18.902 17.5036616960251 1.39833830397487 45 16.239 17.0682678847899 -0.829267884789924 46 19.158 18.536356087295 0.621643912704998 47 18.279 17.7095609107417 0.569439089258335 48 15.698 16.4751340883821 -0.777134088382074 49 16.239 17.0682678847899 -0.829267884789924 50 18.431 18.0692146722449 0.361785327755084 51 18.414 17.6995530855548 0.714446914445182 52 19.801 18.9437277761743 0.85727222382573 53 14.995 16.4834349509333 -1.48843495093329 54 18.706 17.551744134725 1.15425586527503 55 18.232 17.2154817107282 1.01651828927179 56 19.409 17.9163131009185 1.49268689908154 57 16.263 16.5724024246338 -0.309402424633834 58 19.017 17.7761957237201 1.24080427627991 59 20.298 18.6127099950022 1.6852900049978 60 19.891 18.6312927752621 1.25970722473791 61 15.203 16.0171494195329 -0.814149419532942 62 17.845 17.1284352463267 0.716564753673265 63 17.502 17.138662001035 0.363337998965024 64 18.532 17.5626894516316 0.96931054836836 65 15.737 16.6622291499918 -0.925229149991802 66 17.77 17.1610070417449 0.608992958255065 67 17.224 16.8121652872368 0.411834712763178 68 17.601 16.4264323555716 1.1745676444284 69 14.94 15.6740323239188 -0.734032323918753 70 18.507 17.1204019612274 1.38659803877263 71 17.635 16.546141036782 1.088858963218 72 19.392 17.0011444945187 2.39085550548131 73 15.699 15.7550358183819 -0.0560358183818946 74 17.661 16.6193167336885 1.04168326631147 75 18.243 16.9026804381046 1.34031956189538 76 19.643 18.1135518043631 1.52944819563692 77 15.77 16.5425886454639 -0.772588645463865 78 17.344 17.2669446883967 0.0770553116032564 79 17.229 17.4039745187523 -0.174974518752279 80 17.322 17.7005744783444 -0.378574478344372 81 16.152 16.3705647427662 -0.21856474276615 82 17.919 17.7428872421956 0.176112757804387 83 16.918 16.7332971667615 0.184702833238464 84 18.114 18.4507621683366 -0.336762168336618 85 16.308 17.5281350613931 -1.2201350613931 86 17.759 17.9450220636125 -0.186022063612544 87 16.021 16.9018163201246 -0.880816320124557 88 17.952 18.1330629882577 -0.181062988257747 89 15.954 17.0857381312189 -1.13173813121887 90 17.762 18.0998406993002 -0.337840699300196 91 16.61 17.208643613665 -0.598643613664969 92 17.751 18.0222099834571 -0.271209983457059 93 15.458 16.7629955523422 -1.30499555234222 94 18.106 18.386366457434 -0.280366457433979 95 15.99 16.6072805661297 -0.617280566129674 96 15.349 16.3516670990596 -1.00266709905959 97 13.185 14.9032523680076 -1.71825236800763 98 15.409 15.961811941391 -0.552811941391041 99 16.007 16.1368844795053 -0.129884479505346 100 16.633 17.2886202684069 -0.655620268406852 101 14.8 16.1132612724296 -1.31326127242963 102 15.974 16.7565411971138 -0.782541197113842 103 15.693 16.0916577998029 -0.398657799802882 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.455202755621226 0.910405511242452 0.544797244378774 8 0.302921168476067 0.605842336952133 0.697078831523933 9 0.231429183941703 0.462858367883406 0.768570816058297 10 0.149838646304053 0.299677292608106 0.850161353695947 11 0.160880169930428 0.321760339860856 0.839119830069572 12 0.104578840425341 0.209157680850683 0.895421159574659 13 0.323764309444038 0.647528618888076 0.676235690555962 14 0.31072506292771 0.621450125855419 0.68927493707229 15 0.333415439135591 0.666830878271183 0.666584560864409 16 0.270051165921519 0.540102331843038 0.729948834078481 17 0.394741837126881 0.789483674253762 0.605258162873119 18 0.368801815878871 0.737603631757742 0.631198184121129 19 0.546992530450097 0.906014939099806 0.453007469549903 20 0.496308193502336 0.992616387004673 0.503691806497664 21 0.686560149531721 0.626879700936559 0.313439850468279 22 0.638087853336472 0.723824293327056 0.361912146663528 23 0.704377960433406 0.591244079133188 0.295622039566594 24 0.666947797223635 0.66610440555273 0.333052202776365 25 0.776085797533603 0.447828404932795 0.223914202466397 26 0.729414486426641 0.541171027146717 0.270585513573359 27 0.675689460405022 0.648621079189957 0.324310539594978 28 0.668532398035107 0.662935203929785 0.331467601964893 29 0.744256104350643 0.511487791298714 0.255743895649357 30 0.707835740848636 0.584328518302728 0.292164259151364 31 0.660073509602664 0.679852980794671 0.339926490397336 32 0.663454862487216 0.673090275025569 0.336545137512784 33 0.721327720563971 0.557344558872057 0.278672279436029 34 0.67234343518416 0.65531312963168 0.32765656481584 35 0.627786102538095 0.74442779492381 0.372213897461905 36 0.57530369266569 0.84939261466862 0.42469630733431 37 0.735497361218448 0.529005277563104 0.264502638781552 38 0.700765600146156 0.598468799707689 0.299234399853844 39 0.694285661664346 0.611428676671308 0.305714338335654 40 0.672454128300879 0.655091743398242 0.327545871699121 41 0.915444096383558 0.169111807232885 0.0845559036164423 42 0.902221153232336 0.195557693535328 0.097778846767664 43 0.976159448289574 0.0476811034208517 0.0238405517104258 44 0.97852948268173 0.0429410346365412 0.0214705173182706 45 0.98408825613347 0.0318234877330595 0.0159117438665298 46 0.97955691329754 0.0408861734049187 0.0204430867024594 47 0.971571999047036 0.0568560019059286 0.0284280009529643 48 0.9777819342666 0.0444361314668018 0.0222180657334009 49 0.984410125905637 0.0311797481887268 0.0155898740943634 50 0.980214119011165 0.0395717619776701 0.0197858809888351 51 0.973050498385925 0.0538990032281505 0.0269495016140753 52 0.969199776477414 0.0616004470451719 0.0308002235225859 53 0.9935013157724 0.0129973684551991 0.00649868422759956 54 0.991245960040869 0.017508079918262 0.008754039959131 55 0.98833275389177 0.0233344922164611 0.0116672461082305 56 0.985913032286985 0.0281739354260294 0.0140869677130147 57 0.987599447384946 0.0248011052301084 0.0124005526150542 58 0.98261242278433 0.0347751544313381 0.017387577215669 59 0.978407076340295 0.0431858473194108 0.0215929236597054 60 0.970463427403662 0.0590731451926758 0.0295365725963379 61 0.982337423128005 0.0353251537439907 0.0176625768719953 62 0.975236467109918 0.0495270657801641 0.0247635328900821 63 0.966677294604218 0.0666454107915638 0.0333227053957819 64 0.953831021981513 0.0923379560369742 0.0461689780184871 65 0.985351708779539 0.0292965824409222 0.0146482912204611 66 0.980248487033632 0.0395030259327359 0.019751512966368 67 0.97283795484642 0.0543240903071618 0.0271620451535809 68 0.96454756579129 0.0709048684174203 0.0354524342087102 69 0.975234981052845 0.0495300378943092 0.0247650189471546 70 0.969845642906597 0.060308714186806 0.030154357093403 71 0.96303002137452 0.073939957250959 0.0369699786254795 72 0.994775444899152 0.0104491102016953 0.00522455510084767 73 0.99241377700481 0.0151724459903781 0.00758622299518907 74 0.99297358968884 0.0140528206223206 0.00702641031116029 75 0.998528389631574 0.00294322073685158 0.00147161036842579 76 0.999951274013229 9.7451973542612e-05 4.8725986771306e-05 77 0.99992700061823 0.000145998763540958 7.29993817704789e-05 78 0.999968439998745 6.3120002509985e-05 3.15600012549925e-05 79 0.999978980261687 4.20394766256171e-05 2.10197383128085e-05 80 0.99999837218468 3.25563064119566e-06 1.62781532059783e-06 81 0.999997775400352 4.44919929534841e-06 2.2245996476742e-06 82 0.99999707901036 5.84197928020656e-06 2.92098964010328e-06 83 0.999999221630155 1.55673968981993e-06 7.78369844909965e-07 84 0.999997645844946 4.70831010798502e-06 2.35415505399251e-06 85 0.999996493910424 7.01217915218956e-06 3.50608957609478e-06 86 0.99999620756137 7.58487726019375e-06 3.79243863009688e-06 87 0.999987266073793 2.54678524142613e-05 1.27339262071306e-05 88 0.999957502263783 8.49954724338374e-05 4.24977362169187e-05 89 0.999872757629799 0.000254484740402111 0.000127242370201055 90 0.999661875579416 0.000676248841167907 0.000338124420583954 91 0.999008552272321 0.00198289545535774 0.000991447727678872 92 0.997143752461156 0.00571249507768799 0.002856247538844 93 0.992255289877736 0.0154894202445278 0.0077447101222639 94 0.993498329821039 0.0130033403579225 0.00650167017896125 95 0.980520358313938 0.0389592833721243 0.0194796416860621 96 0.936476591508596 0.127046816982808 0.0635234084914042 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.2 NOK 5% type I error level 43 0.477777777777778 NOK 10% type I error level 53 0.588888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/10a9t31292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/10a9t31292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/1l8ws1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/1l8ws1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/2dzvv1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/2dzvv1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/3dzvv1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/3dzvv1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/4dzvv1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/4dzvv1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/5o9vy1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/5o9vy1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/6o9vy1292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/6o9vy1292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/7hic11292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/7hic11292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/8hic11292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/8hic11292682070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/9a9t31292682070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926819876o1s6mqxn9ut78d/9a9t31292682070.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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