Aantal openstaande VDAB-vacatures: Regressie Analyse

*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, 27 Dec 2010 18:00:51 +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/27/t12934727937ffnh5oyjup3nwj.htm/, Retrieved Mon, 27 Dec 2010 18:59:56 +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/27/t12934727937ffnh5oyjup3nwj.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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27.951 29.781 32.914 33.488 35.652 36.488 35.387 35.676 34.844 32.447 31.068 29.010 29.812 30.951 32.974 32.936 34.012 32.946 31.948 30.599 27.691 25.073 23.406 22.248 22.896 25.317 26.558 26.471 27.543 26.198 24.725 25.005 23.462 20.780 19.815 19.761 21.454 23.899 24.939 23.580 24.562 24.696 23.785 23.812 21.917 19.713 19.282 18.788 21.453 24.482 27.474 27.264 27.349 30.632 29.429 30.084 26.290 24.379 23.335 21.346 21.106 24.514 28.353 30.805 31.348 34.556 33.855 34.787 32.529 29.998 29.257 28.155 30.466 35.704 39.327 39.351 42.234 43.630 43.722 43.121 37.985 37.135 34.646 33.026 35.087 38.846 42.013 43.908 42.868 44.423 44.167 43.636 44.382 42.142 43.452 36.912 42.413 45.344 44.873 47.510 49.554 47.369 45.998 48.140 48.441 44.928 40.454 38.661 37.246 36.843 36.424 37.594 38.144 38.737 34.560 36.080 33.508 35.462 33.374 32.110 35.533 35.532 37.903 36.763 40.399 44.164 44.496 43.110 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation VDAB-vacatures[t] = + 19.0922638297872 + 2.25662092198582M1[t] + 4.46672037395229M2[t] + 6.38072891682785M3[t] + 6.78373745970341M4[t] + 7.92101872985171M5[t] + 8.71093636363636M6[t] + 7.50621763378466M7[t] + 7.5510443584784M8[t] + 5.67778017408124M9[t] + 3.8207887169568M10[t] + 2.45206998710509M11[t] + 0.134991457124436t + 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) 19.0922638297872 2.053491 9.2975 0 0 M1 2.25662092198582 2.555017 0.8832 0.378918 0.189459 M2 4.46672037395229 2.554694 1.7484 0.082989 0.041495 M3 6.38072891682785 2.554442 2.4979 0.013871 0.006935 M4 6.78373745970341 2.554262 2.6558 0.009004 0.004502 M5 7.92101872985171 2.554154 3.1012 0.002411 0.001206 M6 8.71093636363636 2.554119 3.4105 0.000888 0.000444 M7 7.50621763378466 2.554154 2.9388 0.003964 0.001982 M8 7.5510443584784 2.554262 2.9563 0.003762 0.001881 M9 5.67778017408124 2.554442 2.2227 0.028141 0.01407 M10 3.8207887169568 2.554694 1.4956 0.137429 0.068714 M11 2.45206998710509 2.555017 0.9597 0.339165 0.169582 t 0.134991457124436 0.01355 9.9627 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.716794881754803 R-squared 0.513794902509882 Adjusted R-squared 0.464350316324447 F-TEST (value) 10.3913277903259 F-TEST (DF numerator) 12 F-TEST (DF denominator) 118 p-value 9.84767822842514e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.84558181029365 Sum Squared Residuals 4032.15755069865 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 27.951 21.4838762088975 6.46712379110254 2 29.781 23.8289671179884 5.95203288201161 3 32.914 25.8779671179884 7.03603288201161 4 33.488 26.4159671179884 7.0720328820116 5 35.652 27.6882398452611 7.96376015473888 6 36.488 28.6131489361702 7.87485106382979 7 35.387 27.5434216634429 7.84357833655706 8 35.676 27.7232398452611 7.95276015473888 9 34.844 25.9849671179884 8.8590328820116 10 32.447 24.2629671179884 8.1840328820116 11 31.068 23.0292398452611 8.03876015473888 12 29.01 20.7121613152805 8.29783868471954 13 29.812 23.1037736943907 6.70822630560929 14 30.951 25.4488646034816 5.50213539651837 15 32.974 27.4978646034816 5.47613539651837 16 32.936 28.0358646034816 4.90013539651838 17 34.012 29.3081373307544 4.70386266924565 18 32.946 30.2330464216634 2.71295357833655 19 31.948 29.1633191489362 2.78468085106383 20 30.599 29.3431373307544 1.25586266924565 21 27.691 27.6048646034816 0.0861353965183724 22 25.073 25.8828646034816 -0.809864603481627 23 23.406 24.6491373307544 -1.24313733075436 24 22.248 22.3320588007737 -0.0840588007736956 25 22.896 24.7236711798839 -1.82767117988395 26 25.317 27.0687620889749 -1.75176208897486 27 26.558 29.1177620889749 -2.55976208897485 28 26.471 29.6557620889749 -3.18476208897486 29 27.543 30.9280348162476 -3.38503481624758 30 26.198 31.8529439071567 -5.65494390715667 31 24.725 30.7832166344294 -6.0582166344294 32 25.005 30.9630348162476 -5.95803481624759 33 23.462 29.2247620889749 -5.76276208897485 34 20.78 27.5027620889749 -6.72276208897486 35 19.815 26.2690348162476 -6.45403481624758 36 19.761 23.9519562862669 -4.19095628626692 37 21.454 26.3435686653772 -4.88956866537718 38 23.899 28.6886595744681 -4.78965957446808 39 24.939 30.7376595744681 -5.79865957446809 40 23.58 31.2756595744681 -7.69565957446808 41 24.562 32.5479323017408 -7.98593230174081 42 24.696 33.4728413926499 -8.7768413926499 43 23.785 32.4031141199226 -8.61811411992263 44 23.812 32.5829323017408 -8.77093230174082 45 21.917 30.8446595744681 -8.92765957446809 46 19.713 29.1226595744681 -9.4096595744681 47 19.282 27.8889323017408 -8.60693230174082 48 18.788 25.5718537717602 -6.78385377176016 49 21.453 27.9634661508704 -6.51046615087041 50 24.482 30.3085570599613 -5.82655705996132 51 27.474 32.3575570599613 -4.88355705996131 52 27.264 32.8955570599613 -5.63155705996132 53 27.349 34.167829787234 -6.81882978723405 54 30.632 35.0927388781431 -4.46073887814313 55 29.429 34.0230116054159 -4.59401160541586 56 30.084 34.202829787234 -4.11882978723404 57 26.29 32.4645570599613 -6.17455705996132 58 24.379 30.7425570599613 -6.36355705996132 59 23.335 29.508829787234 -6.17382978723404 60 21.346 27.1917512572534 -5.84575125725338 61 21.106 29.5833636363636 -8.47736363636364 62 24.514 31.9284545454545 -7.41445454545455 63 28.353 33.9774545454545 -5.62445454545454 64 30.805 34.5154545454545 -3.71045454545454 65 31.348 35.7877272727273 -4.43972727272727 66 34.556 36.7126363636364 -2.15663636363637 67 33.855 35.6429090909091 -1.7879090909091 68 34.787 35.8227272727273 -1.03572727272727 69 32.529 34.0844545454545 -1.55545454545454 70 29.998 32.3624545454545 -2.36445454545455 71 29.257 31.1287272727273 -1.87172727272727 72 28.155 28.8116487427466 -0.656648742746615 73 30.466 31.2032611218569 -0.73726112185687 74 35.704 33.5483520309478 2.15564796905222 75 39.327 35.5973520309478 3.72964796905222 76 39.351 36.1353520309478 3.21564796905222 77 42.234 37.4076247582205 4.8263752417795 78 43.63 38.3325338491296 5.29746615087041 79 43.722 37.2628065764023 6.45919342359768 80 43.121 37.4426247582205 5.6783752417795 81 37.985 35.7043520309478 2.28064796905222 82 37.135 33.9823520309478 3.15264796905222 83 34.646 32.7486247582205 1.8973752417795 84 33.026 30.4315462282398 2.59445377176016 85 35.087 32.8231586073501 2.2638413926499 86 38.846 35.168249516441 3.67775048355899 87 42.013 37.217249516441 4.79575048355899 88 43.908 37.755249516441 6.152750483559 89 42.868 39.0275222437137 3.84047775628627 90 44.423 39.9524313346228 4.47056866537718 91 44.167 38.8827040618956 5.28429593810445 92 43.636 39.0625222437137 4.57347775628627 93 44.382 37.324249516441 7.057750483559 94 42.142 35.602249516441 6.539750483559 95 43.452 34.3685222437137 9.08347775628627 96 36.912 32.0514437137331 4.86055628626692 97 42.413 34.4430560928433 7.96994390715667 98 45.344 36.7881470019342 8.55585299806577 99 44.873 38.8371470019342 6.03585299806576 100 47.51 39.3751470019342 8.13485299806577 101 49.554 40.647419729207 8.90658027079304 102 47.369 41.5723288201161 5.79667117988395 103 45.998 40.5026015473888 5.49539845261121 104 48.14 40.682419729207 7.45758027079304 105 48.441 38.9441470019342 9.49685299806577 106 44.928 37.2221470019342 7.70585299806576 107 40.454 35.988419729207 4.46558027079303 108 38.661 33.6713411992263 4.9896588007737 109 37.246 36.0629535783366 1.18304642166344 110 36.843 38.4080444874275 -1.56504448742746 111 36.424 40.4570444874275 -4.03304448742746 112 37.594 40.9950444874275 -3.40104448742746 113 38.144 42.2673172147002 -4.12331721470019 114 38.737 43.1922263056093 -4.45522630560928 115 34.56 42.122499032882 -7.56249903288201 116 36.08 42.3023172147002 -6.2223172147002 117 33.508 40.5640444874275 -7.05604448742747 118 35.462 38.8420444874275 -3.38004448742746 119 33.374 37.6083172147002 -4.23431721470019 120 32.11 35.2912386847195 -3.18123868471954 121 35.533 37.6828510638298 -2.14985106382979 122 35.532 40.0279419729207 -4.4959419729207 123 37.903 42.0769419729207 -4.17394197292069 124 36.763 42.6149419729207 -5.8519419729207 125 40.399 43.8872147001934 -3.48821470019342 126 44.164 44.8121237911025 -0.648123791102515 127 44.496 43.7423965183752 0.753603481624759 128 43.11 43.9222147001934 -0.812214700193427 129 43.88 42.1839419729207 1.69605802707931 130 43.93 40.4619419729207 3.4680580270793 131 44.327 39.2282147001934 5.09878529980657 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00469531525251385 0.0093906305050277 0.995304684747486 17 0.00280325949891358 0.00560651899782716 0.997196740501086 18 0.00442510215152771 0.00885020430305542 0.995574897848472 19 0.00293842977451971 0.00587685954903943 0.99706157022548 20 0.00355527074316024 0.00711054148632047 0.99644472925684 21 0.00745539473496915 0.0149107894699383 0.99254460526503 22 0.00934211823241927 0.0186842364648385 0.99065788176758 23 0.00977927081496118 0.0195585416299224 0.990220729185039 24 0.00715194397352384 0.0143038879470477 0.992848056026476 25 0.00348326627336476 0.00696653254672953 0.996516733726635 26 0.0015883240735687 0.0031766481471374 0.998411675926431 27 0.000742309765745696 0.00148461953149139 0.999257690234254 28 0.00034481678425491 0.00068963356850982 0.999655183215745 29 0.000167916413059483 0.000335832826118966 0.99983208358694 30 0.00010359413862204 0.00020718827724408 0.999896405861378 31 6.69557557392967e-05 0.000133911511478593 0.99993304424426 32 3.23421093144452e-05 6.46842186288904e-05 0.999967657890686 33 1.4080559200877e-05 2.8161118401754e-05 0.9999859194408 34 6.16445084039201e-06 1.2328901680784e-05 0.99999383554916 35 2.40847501663382e-06 4.81695003326764e-06 0.999997591524983 36 9.1703094812339e-07 1.83406189624678e-06 0.999999082969052 37 7.76748682885126e-07 1.55349736577025e-06 0.999999223251317 38 7.37546973762461e-07 1.47509394752492e-06 0.999999262453026 39 3.84767832148696e-07 7.69535664297392e-07 0.999999615232168 40 1.49890267202105e-07 2.9978053440421e-07 0.999999850109733 41 5.69099199198933e-08 1.13819839839787e-07 0.99999994309008 42 2.36774710957823e-08 4.73549421915646e-08 0.999999976322529 43 1.01060159686703e-08 2.02120319373407e-08 0.999999989893984 44 4.62217639580791e-09 9.24435279161583e-09 0.999999995377824 45 2.10113403164327e-09 4.20226806328654e-09 0.999999997898866 46 1.12157557881827e-09 2.24315115763654e-09 0.999999998878424 47 7.422095125092e-10 1.4844190250184e-09 0.99999999925779 48 5.21957580190219e-10 1.04391516038044e-09 0.999999999478042 49 1.83818552444284e-09 3.67637104888568e-09 0.999999998161815 50 8.50716871561119e-09 1.70143374312224e-08 0.999999991492831 51 4.73569876256369e-08 9.47139752512737e-08 0.999999952643012 52 1.56694745934738e-07 3.13389491869475e-07 0.999999843305254 53 2.23862112344181e-07 4.47724224688362e-07 0.999999776137888 54 1.50585961547965e-06 3.01171923095929e-06 0.999998494140385 55 5.4507034690268e-06 1.09014069380536e-05 0.99999454929653 56 1.91872657578322e-05 3.83745315156645e-05 0.999980812734242 57 3.09771607813497e-05 6.19543215626995e-05 0.999969022839219 58 5.96530114247191e-05 0.000119306022849438 0.999940346988575 59 0.000108445743805156 0.000216891487610312 0.999891554256195 60 0.000136714925019997 0.000273429850039994 0.99986328507498 61 0.000203994582957987 0.000407989165915974 0.999796005417042 62 0.000333455899499254 0.000666911798998508 0.9996665441005 63 0.000576197003364854 0.00115239400672971 0.999423802996635 64 0.00146507077635992 0.00293014155271983 0.99853492922364 65 0.00322713916820042 0.00645427833640084 0.9967728608318 66 0.00911874248962296 0.0182374849792459 0.990881257510377 67 0.0213269835876653 0.0426539671753306 0.978673016412335 68 0.045309036904641 0.0906180738092819 0.95469096309536 69 0.0873398587302893 0.174679717460579 0.91266014126971 70 0.169897691084843 0.339795382169687 0.830102308915157 71 0.290958102849959 0.581916205699918 0.709041897150041 72 0.378786662282695 0.757573324565391 0.621213337717305 73 0.49065014270819 0.98130028541638 0.50934985729181 74 0.592059386409939 0.815881227180122 0.407940613590061 75 0.671665177225944 0.656669645548113 0.328334822774056 76 0.729970004247863 0.540059991504275 0.270029995752137 77 0.786803112708075 0.42639377458385 0.213196887291925 78 0.827805121808176 0.344389756383648 0.172194878191824 79 0.861817916501631 0.276364166996737 0.138182083498369 80 0.877948419897603 0.244103160204795 0.122051580102397 81 0.896639135431296 0.206721729137408 0.103360864568704 82 0.920137769402561 0.159724461194878 0.079862230597439 83 0.946700761830403 0.106598476339193 0.0532992381695965 84 0.949616181280732 0.100767637438537 0.0503838187192684 85 0.955085710128038 0.0898285797439238 0.0449142898719619 86 0.951772017893277 0.0964559642134462 0.0482279821067231 87 0.943362085182401 0.113275829635197 0.0566379148175987 88 0.936016459901916 0.127967080196168 0.0639835400980841 89 0.927103854809047 0.145792290381905 0.0728961451909527 90 0.915483736531509 0.169032526936983 0.0845162634684913 91 0.899255468238788 0.201489063522423 0.100744531761212 92 0.882781741842266 0.234436516315468 0.117218258157734 93 0.870108489033867 0.259783021932266 0.129891510966133 94 0.863548913869966 0.272902172260069 0.136451086130034 95 0.851751090344524 0.296497819310952 0.148248909655476 96 0.82025774164353 0.359484516712939 0.179742258356469 97 0.793710875463388 0.412578249073224 0.206289124536612 98 0.788131398027251 0.423737203945498 0.211868601972749 99 0.75973268561837 0.480534628763259 0.240267314381629 100 0.775853103774931 0.448293792450138 0.224146896225069 101 0.806262675642326 0.387474648715349 0.193737324357674 102 0.7768148964333 0.4463702071334 0.2231851035667 103 0.757092776612015 0.485814446775969 0.242907223387985 104 0.795344223752346 0.409311552495308 0.204655776247654 105 0.89667840728065 0.2066431854387 0.10332159271935 106 0.928929528249322 0.142140943501357 0.0710704717506783 107 0.931949506573689 0.136100986852622 0.068050493426311 108 0.970308056523485 0.0593838869530308 0.0296919434765154 109 0.972213498717203 0.0555730025655934 0.0277865012827967 110 0.978070512110099 0.043858975779803 0.0219294878899015 111 0.972841585774507 0.054316828450987 0.0271584142254935 112 0.991373311419847 0.0172533771603058 0.0086266885801529 113 0.99719313015231 0.00561373969537926 0.00280686984768963 114 0.997606491735448 0.00478701652910367 0.00239350826455184 115 0.98754272276539 0.0249145544692185 0.0124572772346093 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 48 0.48 NOK 5% type I error level 57 0.57 NOK 10% type I error level 63 0.63 NOK

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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Software written by Ed van Stee & Patrick Wessa

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