workshop 7 berekening 2

*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: Tue, 17 Nov 2009 11:53:04 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921.htm/, Retrieved Tue, 17 Nov 2009 19:54:22 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921.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:

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6.70 2.04 6.40 2.16 6.30 2.75 6.80 2.79 7.30 2.88 7.10 3.36 7.00 2.97 6.80 3.10 6.60 2.49 6.30 2.20 6.10 2.25 6.10 2.09 6.30 2.79 6.30 3.14 6.00 2.93 6.20 2.65 6.40 2.67 6.80 2.26 7.50 2.35 7.50 2.13 7.60 2.18 7.60 2.90 7.40 2.63 7.30 2.67 7.10 1.81 6.90 1.33 6.80 0.88 7.50 1.28 7.60 1.26 7.80 1.26 8.00 1.29 8.10 1.10 8.20 1.37 8.30 1.21 8.20 1.74 8.00 1.76 7.90 1.48 7.60 1.04 7.60 1.62 8.30 1.49 8.40 1.79 8.40 1.80 8.40 1.58 8.40 1.86 8.60 1.74 8.90 1.59 8.80 1.26 8.30 1.13 7.50 1.92 7.20 2.61 7.40 2.26 8.80 2.41 9.30 2.26 9.30 2.03 8.70 2.86 8.20 2.55 8.30 2.27 8.50 2.26 8.60 2.57 8.50 3.07 8.20 2.76 8.10 2.51 7.90 2.87 8.60 3.14 8.70 3.11 8.70 3.16 8.50 2.47 8.40 2.57 8.50 2.89 8.70 2.63 8.70 2.38 8.60 1.69 8.50 1.96 8.30 2.19 8.00 1.87 8.20 1.60 8.10 1.63 8.10 1.22 8.00 1.21 7.90 1.49 7.90 1.64 8.00 1.66 8.00 1.77 7.90 1.82 8.00 1.78 7.70 1.28 7.20 1.29 7.50 1.37 7.30 1.12 7.00 1.51 7.00 2.24 7.00 2.94 7.20 3.09 7 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 Y[t] = + 8.2080736047586 -0.227269357735311X[t] -0.285606603475968M1[t] -0.488132148027806M2[t] -0.567930427340223M3[t] + 0.0969684240333393M4[t] + 0.239140339633371M5[t] + 0.204544398757064M6[t] + 0.117423325161279M7[t] -0.0252531392571213M8[t] + 0.0601006132443807M9[t] + 0.224999629350884M10[t] + 0.179040055854871M11[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) 8.2080736047586 0.29607 27.7234 0 0 X -0.227269357735311 0.071332 -3.1861 0.001952 0.000976 M1 -0.285606603475968 0.359081 -0.7954 0.428374 0.214187 M2 -0.488132148027806 0.359332 -1.3584 0.17754 0.08877 M3 -0.567930427340223 0.3597 -1.5789 0.117683 0.058841 M4 0.0969684240333393 0.359907 0.2694 0.788186 0.394093 M5 0.239140339633371 0.359617 0.665 0.507671 0.253835 M6 0.204544398757064 0.359592 0.5688 0.570819 0.285409 M7 0.117423325161279 0.359422 0.3267 0.744613 0.372307 M8 -0.0252531392571213 0.359132 -0.0703 0.944089 0.472045 M9 0.0601006132443807 0.358995 0.1674 0.867401 0.4337 M10 0.224999629350884 0.358983 0.6268 0.532314 0.266157 M11 0.179040055854871 0.358973 0.4988 0.619104 0.309552 Multiple Linear Regression - Regression Statistics Multiple R 0.44743687259078 R-squared 0.200199754953818 Adjusted R-squared 0.0991723555795636 F-TEST (value) 1.9816382109588 F-TEST (DF numerator) 12 F-TEST (DF denominator) 95 p-value 0.034235096894344 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.76133582037846 Sum Squared Residuals 55.0650619821776 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.7 7.4588375115025 -0.758837511502503 2 6.4 7.22903964402252 -0.82903964402252 3 6.3 7.01515244364627 -0.71515244364627 4 6.8 7.67096052071042 -0.87096052071042 5 7.3 7.79267819411427 -0.492678194114274 6 7.1 7.64899296152502 -0.548992961525018 7 7 7.650506937446 -0.650506937446003 8 6.8 7.47828545652201 -0.678285456522014 9 6.6 7.70227351724206 -1.10227351724206 10 6.3 7.9330806470918 -1.6330806470918 11 6.1 7.87575760570902 -1.77575760570902 12 6.1 7.7330806470918 -1.6330806470918 13 6.3 7.28838549320111 -0.988385493201114 14 6.3 7.00631567344192 -0.706315673441916 15 6 6.97424395925391 -0.974243959253914 16 6.2 7.70277823079336 -1.50277823079336 17 6.4 7.84040475923869 -1.44040475923869 18 6.8 7.89898925503386 -1.09898925503386 19 7.5 7.7914139392419 -0.291413939241896 20 7.5 7.69873673352526 -0.198736733525265 21 7.6 7.77272701814 -0.172727018140002 22 7.6 7.77399209667708 -0.173992096677081 23 7.4 7.7893952497696 -0.389395249769600 24 7.3 7.60126441960532 -0.301264419605318 25 7.1 7.51110946378172 -0.411109463781718 26 6.9 7.41767321094283 -0.517673210942828 27 6.8 7.4401461426113 -0.640146142611301 28 7.5 8.01413725089074 -0.514137250890739 29 7.6 8.16085455364548 -0.560854553645478 30 7.8 8.12625861276917 -0.32625861276917 31 8 8.03231945844133 -0.0323194584413256 32 8.1 7.93282417199263 0.167175828007365 33 8.2 7.9568151979056 0.243184802094396 34 8.3 8.15807731124976 0.141922688750245 35 8.2 7.99166497815403 0.208335021845972 36 8 7.80807953514445 0.191920464855549 37 7.9 7.58610835183437 0.31389164816563 38 7.6 7.48358132468607 0.116418675313931 39 7.6 7.27196681788717 0.328033182112829 40 8.3 7.96641068576632 0.333589314233677 41 8.4 8.04040179404576 0.359598205954238 42 8.4 8.0035331595921 0.396466840407898 43 8.4 7.96641134469809 0.433588655301915 44 8.4 7.7600994601138 0.639900539886202 45 8.6 7.87272553554354 0.727274464456462 46 8.9 8.07171495531034 0.828285044689662 47 8.8 8.10075426986698 0.699245730133024 48 8.3 7.9512592305177 0.348740769482304 49 7.5 7.48610983443083 0.0138901655691662 50 7.2 7.12676843304163 0.0732315669583697 51 7.4 7.12651442893657 0.273485571063428 52 8.8 7.75732287664984 1.04267712335016 53 9.3 7.93358519591017 1.36641480408983 54 9.3 7.95126120731298 1.34873879268702 55 8.7 7.67550656679689 1.02449343320311 56 8.2 7.60328360327643 0.596716396723565 57 8.3 7.75227277594382 0.547727224056177 58 8.5 7.91944448562768 0.58055551437232 59 8.6 7.80303141123372 0.79696858876628 60 8.5 7.5103566765112 0.989643323488806 61 8.2 7.29520357393317 0.904796426066827 62 8.1 7.14949536881516 0.950504631184838 63 7.9 6.98788012071803 0.912119879281967 64 8.6 7.59141624550306 1.00858375449694 65 8.7 7.74040624183515 0.959593758164847 66 8.7 7.69444683307208 1.00555316692792 67 8.5 7.76414161631366 0.735858383686341 68 8.4 7.59873821612173 0.801261783878273 69 8.5 7.61136577414793 0.88863422585207 70 8.7 7.83535482326561 0.864645176734385 71 8.7 7.84621258920343 0.85378741079657 72 8.6 7.82398839018592 0.776011609814077 73 8.5 7.47701906012142 1.02298093987858 74 8.3 7.22222156329046 1.07777843670954 75 8 7.21514947845334 0.784850521546657 76 8.2 7.94141105641544 0.258588943584559 77 8.1 8.07676489128341 0.0232351087165872 78 8.1 8.13534938707858 -0.035349387078583 79 8 8.05050100706015 -0.0505010070601506 80 7.9 7.84418912247586 0.0558108775241369 81 7.9 7.89545247131707 0.00454752868293115 82 8 8.05580610026887 -0.0558061002688659 83 8 7.98484689742197 0.0151531025780317 84 7.9 7.79444337368033 0.105556626319668 85 8 7.51792754451378 0.482072455486223 86 7.7 7.42903667882959 0.270963321170406 87 7.2 7.34696570593982 -0.146965705939824 88 7.5 7.99368300869456 -0.493683008694561 89 7.3 8.19267226372842 -0.892672263728421 90 7 8.06944127333534 -1.06944127333534 91 7 7.81641356859278 -0.81641356859278 92 7 7.51464855375966 -0.514648553759663 93 7.2 7.56591190260087 -0.365911902600868 94 7.3 7.6467212563453 -0.346721256345307 95 7.1 7.55985319845694 -0.459853198456938 96 6.8 7.21036112430058 -0.410361124300584 97 6.4 6.97929916668109 -0.57929916668109 98 6.1 6.53586810292982 -0.435868102929823 99 6.5 6.32198090255357 0.178019097446428 100 7.7 6.96188012457625 0.73811987542375 101 7.9 7.22223210619864 0.677767893801356 102 7.5 7.17172731028086 0.328272689719135 103 6.9 7.25278556140921 -0.352785561409210 104 6.6 7.4691946822126 -0.869194682212601 105 6.9 7.67045580715911 -0.770455807159111 106 7.7 7.90580832416356 -0.205808324163561 107 8 7.94848380018432 0.0515161998156816 108 8 8.0671666029627 -0.0671666029627052 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.112096210568758 0.224192421137516 0.887903789431242 17 0.227726635694271 0.455453271388542 0.772273364305729 18 0.1533776320769 0.3067552641538 0.8466223679231 19 0.114571428142383 0.229142856284767 0.885428571857617 20 0.0941265172165358 0.188253034433072 0.905873482783464 21 0.130576270951379 0.261152541902758 0.86942372904862 22 0.296087591625668 0.592175183251336 0.703912408374332 23 0.418156810496677 0.836313620993354 0.581843189503323 24 0.474586345408845 0.94917269081769 0.525413654591155 25 0.46110816648662 0.92221633297324 0.538891833513379 26 0.432023547872671 0.864047095745341 0.567976452127329 27 0.396819890049735 0.793639780099469 0.603180109950265 28 0.404439618861489 0.808879237722978 0.595560381138511 29 0.368717976696082 0.737435953392165 0.631282023303918 30 0.323673715974592 0.647347431949184 0.676326284025408 31 0.265287881399689 0.530575762799378 0.73471211860031 32 0.219710563089314 0.439421126178627 0.780289436910686 33 0.210689584945666 0.421379169891332 0.789310415054334 34 0.207255614784173 0.414511229568345 0.792744385215827 35 0.257568309267135 0.51513661853427 0.742431690732865 36 0.280596972202614 0.561193944405228 0.719403027797386 37 0.280805305672454 0.561610611344908 0.719194694327546 38 0.241124325083247 0.482248650166493 0.758875674916753 39 0.252759431369925 0.50551886273985 0.747240568630075 40 0.278571933651544 0.557143867303087 0.721428066348456 41 0.297312301359284 0.594624602718568 0.702687698640716 42 0.292395138239693 0.584790276479387 0.707604861760307 43 0.256731059791701 0.513462119583402 0.743268940208299 44 0.255108067497261 0.510216134994523 0.744891932502739 45 0.274612713957945 0.54922542791589 0.725387286042055 46 0.318499532822642 0.636999065645285 0.681500467177358 47 0.317623694609016 0.635247389218031 0.682376305390984 48 0.271271108995143 0.542542217990285 0.728728891004857 49 0.236962019209476 0.473924038418952 0.763037980790524 50 0.239007529795077 0.478015059590155 0.760992470204923 51 0.231632718245970 0.463265436491940 0.76836728175403 52 0.378088619421032 0.756177238842064 0.621911380578968 53 0.579380669470945 0.84123866105811 0.420619330529055 54 0.721058601525487 0.557882796949027 0.278941398474513 55 0.802361168295153 0.395277663409694 0.197638831704847 56 0.797953733233307 0.404092533533386 0.202046266766693 57 0.783967580629268 0.432064838741464 0.216032419370732 58 0.77154096528547 0.456918069429061 0.228459034714530 59 0.792623414463063 0.414753171073873 0.207376585536937 60 0.845948158344425 0.308103683311151 0.154051841655575 61 0.861300146343638 0.277399707312725 0.138699853656362 62 0.873239003154756 0.253521993690488 0.126760996845244 63 0.88183264595483 0.236334708090342 0.118167354045171 64 0.898263663984246 0.203472672031508 0.101736336015754 65 0.912997816771831 0.174004366456338 0.0870021832281689 66 0.936343687603695 0.127312624792610 0.0636563123963052 67 0.945522126838527 0.108955746322945 0.0544778731614726 68 0.95930493295550 0.0813901340890021 0.0406950670445010 69 0.973021254137563 0.0539574917248739 0.0269787458624370 70 0.979274601750558 0.0414507964988838 0.0207253982494419 71 0.983206246431753 0.0335875071364939 0.0167937535682469 72 0.985096382359649 0.0298072352807019 0.0149036176403510 73 0.989835044320164 0.0203299113596717 0.0101649556798358 74 0.994594754070177 0.0108104918596467 0.00540524592982337 75 0.994927744011702 0.0101445119765954 0.00507225598829768 76 0.991422058800914 0.0171558823981717 0.00857794119908586 77 0.985606301052472 0.0287873978950561 0.0143936989475281 78 0.979008430528548 0.0419831389429049 0.0209915694714524 79 0.97465388369867 0.0506922326026586 0.0253461163013293 80 0.975267803034587 0.0494643939308263 0.0247321969654132 81 0.97045836363709 0.0590832727258185 0.0295416363629093 82 0.954438768799764 0.091122462400473 0.0455612312002365 83 0.930559759046816 0.138880481906369 0.0694402409531845 84 0.900808696080606 0.198382607838789 0.0991913039193945 85 0.937341852810311 0.125316294379378 0.062658147189689 86 0.969071461790883 0.0618570764182335 0.0309285382091167 87 0.954622718243166 0.0907545635136688 0.0453772817568344 88 0.921545022805878 0.156909954388244 0.0784549771941222 89 0.929249922457842 0.141500155084316 0.070750077542158 90 0.984063421649262 0.0318731567014754 0.0159365783507377 91 0.987452860167305 0.0250942796653907 0.0125471398326954 92 0.969468474849835 0.0610630503003303 0.0305315251501652 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 12 0.155844155844156 NOK 10% type I error level 20 0.259740259740260 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/10a7vm1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/10a7vm1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/1bqp91258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/1bqp91258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/2bwbu1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/2bwbu1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/3dtqe1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/3dtqe1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/429fx1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/429fx1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/5v9os1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/5v9os1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/673hn1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/673hn1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/7a8qo1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/7a8qo1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/8zlnz1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/8zlnz1258483978.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/95byg1258483978.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584840501gnp4he8tozx921/95byg1258483978.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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