Meervoudige regressie Happiness 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: Sun, 21 Nov 2010 14:47:58 +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/Nov/21/t1290351422b77egf5t3hcjfaq.htm/, Retrieved Sun, 21 Nov 2010 15:57:05 +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/Nov/21/t1290351422b77egf5t3hcjfaq.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:

» Textbox « » Textfile « » CSV «

15 10 77 15 11 6 4 16 9 20 63 12 26 5 4 24 12 16 73 15 26 20 10 22 15 10 76 12 15 12 6 21 17 8 90 14 10 11 5 23 14 14 67 8 21 12 8 23 9 19 69 11 27 11 9 21 11 23 54 4 21 13 8 22 13 9 54 13 21 9 11 20 16 12 76 19 22 14 6 12 16 14 75 10 29 12 8 23 15 13 76 15 29 18 11 23 10 11 80 6 29 9 5 30 16 11 89 7 30 15 10 22 12 10 73 14 19 12 7 21 15 12 74 16 19 12 7 21 13 18 78 16 22 12 13 15 18 12 76 14 18 15 10 22 13 10 69 15 28 11 8 24 17 15 74 14 17 13 6 23 14 15 82 12 18 10 8 15 13 12 77 9 20 17 7 24 13 9 84 12 16 13 5 24 15 11 75 14 17 17 9 21 15 16 79 14 25 15 11 21 13 17 79 10 22 13 11 18 13 11 88 16 31 17 9 19 16 13 57 10 38 21 7 29 14 9 69 8 18 12 6 20 18 11 86 12 20 15 6 24 9 20 66 8 23 8 5 27 16 8 54 13 12 15 4 28 16 12 85 11 20 16 10 24 17 10 79 12 15 9 8 29 13 11 84 16 21 13 6 24 17 13 70 16 20 11 4 25 15 13 54 13 30 9 9 14 14 13 70 14 22 15 10 22 10 15 54 5 33 9 6 24 13 12 69 14 25 15 9 24 11 13 68 13 20 14 10 24 11 14 66 15 21 14 13 21 15 9 67 11 16 12 8 21 15 9 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 18.471365186477 -0.371850791919597Depression[t] + 0.0345840445486369Belonging[t] -0.055174226725832Popularity[t] -0.044863835273013ConcernOverMistakes[t] + 0.111652445263202ParentalExpectations[t] -0.0792229760776518ParentalCriticism[t] -0.0548112247843105Organization[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) 18.471365186477 2.203709 8.3819 0 0 Depression -0.371850791919597 0.058378 -6.3697 0 0 Belonging 0.0345840445486369 0.017175 2.0136 0.046131 0.023066 Popularity -0.055174226725832 0.06232 -0.8853 0.377623 0.188811 ConcernOverMistakes -0.044863835273013 0.032106 -1.3974 0.164698 0.082349 ParentalExpectations 0.111652445263202 0.06177 1.8076 0.073006 0.036503 ParentalCriticism -0.0792229760776518 0.078443 -1.0099 0.314411 0.157206 Organization -0.0548112247843105 0.04683 -1.1704 0.243988 0.121994 Multiple Linear Regression - Regression Statistics Multiple R 0.589629676961936 R-squared 0.347663155954237 Adjusted R-squared 0.312265032633924 F-TEST (value) 9.8215137793685 F-TEST (DF numerator) 7 F-TEST (DF denominator) 129 p-value 8.89102680368126e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.96609240890953 Sum Squared Residuals 498.651997487945 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 15.5707562793551 -0.570756279355079 2 9 10.3104946440228 -1.31049464402285 3 12 13.2872868490608 -1.28728684906084 4 15 15.7596521693943 -0.759652169394276 5 17 16.9594491810736 0.0405508189263842 6 14 13.6444380943195 0.355561905680517 7 9 11.338393560231 -2.33839356023097 8 11 10.2353489648617 0.764651035138326 9 13 14.3700357514864 -1.3700357514864 10 16 15.0322900651484 0.967709934851635 11 16 13.4518513150728 2.54814868492719 12 15 14.0146607612581 0.985339238741861 13 10 14.4800538394313 -4.48005383943129 14 16 15.4035617678356 0.596438232164388 15 12 15.286873265127 -3.286873265127 16 15 14.4674072723848 0.532592727615223 17 13 12.0935766854827 0.906423314517342 18 18 14.7342648329791 3.26573516702093 19 13 14.3342792470557 -1.33427924705572 20 17 13.6331839923959 3.36681600760409 21 14 13.9204274772932 0.079572522706769 22 13 15.3063437098016 -2.30634370980158 23 13 16.3897532294179 -3.38975322941788 24 15 15.4737345070114 -0.473734507011405 25 15 13.0121552007422 1.98784479925784 26 13 12.9367216053715 0.0632783946285424 27 13 15.2945073884385 -2.29450738843846 28 16 13.5526424224004 2.44735757759957 29 14 16.030331275342 -2.03033127534201 30 18 15.6798463080327 2.32015369196734 31 9 10.8608358757501 -1.86083587575015 32 16 15.9316467667114 0.0683532332886011 33 16 15.1233462392428 0.87665376075715 34 17 14.9315112168208 2.06848878317921 35 13 15.1218125862326 -2.12181258623264 36 17 13.8191280508301 3.18087194916987 37 15 12.9661713672121 2.03382863278794 38 14 13.9754544326756 0.0245455673244031 39 10 12.2188287717503 -2.21882877175033 40 13 14.1477302007365 -1.14773020073655 41 11 13.8299133460184 -2.82991334601836 42 11 13.1604469223968 -2.16044692239679 43 15 15.6721109996737 -0.672110999673656 44 15 15.4522148076881 -0.452214807688086 45 12 12.7666518496131 -0.766651849613136 46 17 15.0257319155093 1.97426808449065 47 15 13.5365404694218 1.46345953057819 48 16 15.3471296410326 0.652870358967448 49 14 13.8361917074415 0.163808292558475 50 17 14.7708232574782 2.22917674252177 51 10 10.1667732862057 -0.166773286205738 52 11 14.3742513001651 -3.37425130016508 53 15 13.9650213652633 1.03497863473669 54 15 14.8739562956362 0.126043704363799 55 7 10.8213126923863 -3.82131269238629 56 17 15.0204441000745 1.97955589992549 57 14 12.5899954853358 1.41000451466421 58 18 16.2121583093109 1.78784169068912 59 14 13.8022396066041 0.197760393395936 60 14 15.1021069058429 -1.1021069058429 61 9 15.5227535945348 -6.52275359453481 62 14 14.4643505477734 -0.464350547773396 63 11 12.4456382773282 -1.44563827732819 64 16 13.8024934184012 2.19750658159875 65 17 15.2301181212198 1.7698818787802 66 12 15.2717598774344 -3.27175987743435 67 15 13.7623377654847 1.23766223451528 68 15 15.3076249728366 -0.30762497283657 69 16 16.18366555304 -0.183665553040031 70 16 16.3694197038323 -0.369419703832316 71 11 12.7190564215049 -1.71905642150492 72 12 12.8950548880976 -0.8950548880976 73 14 14.109227977714 -0.109227977713956 74 15 15.6425332638628 -0.642533263862847 75 17 15.4105009188413 1.58949908115866 76 19 14.711572918669 4.288427081331 77 15 13.9436180294779 1.05638197052208 78 16 13.2126280746209 2.78737192537908 79 14 14.4413997796972 -0.441399779697186 80 16 11.3168274791738 4.68317252082618 81 15 14.7454605985579 0.254539401442148 82 17 14.6290650709815 2.37093492901845 83 12 14.2089367767596 -2.20893677675965 84 18 14.8776549168948 3.12234508310518 85 13 14.4011155726877 -1.40111557268766 86 14 12.8606483847811 1.13935161521892 87 14 14.0491281612205 -0.0491281612205215 88 14 13.9519378920334 0.0480621079665583 89 12 14.1331993767643 -2.13319937676426 90 14 12.9474394971082 1.05256050289177 91 12 13.6058479814505 -1.60584798145048 92 15 14.5151070508531 0.484892949146905 93 11 12.3297013138244 -1.3297013138244 94 15 15.1267383115133 -0.12673831151334 95 14 14.2524491044696 -0.252449104469553 96 15 13.7132596746007 1.28674032539932 97 16 14.4006607685608 1.59933923143919 98 14 11.3082983628279 2.69170163717206 99 18 16.0653976837292 1.93460231627078 100 14 15.1794376843252 -1.17943768432516 101 13 12.4563851303547 0.543614869645329 102 14 12.7252032024807 1.27479679751933 103 14 14.5040805009936 -0.504080500993641 104 17 15.461191467573 1.53880853242703 105 12 12.9555294479144 -0.955529447914446 106 16 13.6300280235476 2.36997197645239 107 10 12.3794950924766 -2.37949509247661 108 13 14.4168279574073 -1.41682795740732 109 15 15.1445316493511 -0.144531649351138 110 16 15.2859758777211 0.714024122278923 111 14 13.0353068514408 0.96469314855916 112 13 12.6952172676047 0.30478273239527 113 17 14.95902685886 2.04097314113998 114 14 13.8979020363115 0.102097963688514 115 16 13.4668499087546 2.53315009124543 116 12 14.099801047829 -2.09980104782901 117 16 13.5077902144144 2.49220978558559 118 8 10.5537521962536 -2.55375219625362 119 9 12.507835916133 -3.50783591613298 120 13 12.3707429819561 0.629257018043905 121 19 15.3553962924165 3.64460370758352 122 11 12.5044640495103 -1.50446404951033 123 15 14.947035987839 0.0529640121609898 124 11 13.773210223737 -2.77321022373699 125 15 16.0277814283964 -1.0277814283964 126 16 15.8196278046792 0.18037219532082 127 15 12.8976148830332 2.10238511696677 128 12 12.8995675520869 -0.899567552086856 129 16 14.8933794716469 1.10662052835308 130 15 13.4283449652334 1.57165503476662 131 13 14.6943120567262 -1.69431205672624 132 14 14.2004979440111 -0.200497944011147 133 11 12.9876136044835 -1.98761360448346 134 15 14.5003007559447 0.499699244055297 135 14 12.7727428883851 1.22725711161494 136 13 15.9077684621914 -2.90776846219141 137 15 15.8460007999241 -0.84600079992406 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.267906305087992 0.535812610175984 0.732093694912008 12 0.221244775804694 0.442489551609388 0.778755224195306 13 0.7308467126837 0.5383065746326 0.2691532873163 14 0.619263802395307 0.761472395209386 0.380736197604693 15 0.657639606945299 0.684720786109402 0.342360393054701 16 0.60224114395102 0.79551771209796 0.39775885604898 17 0.520363099415867 0.959273801168266 0.479636900584133 18 0.646598862653734 0.706802274692533 0.353401137346266 19 0.566288537947937 0.867422924104127 0.433711462052063 20 0.684409144382555 0.63118171123489 0.315590855617445 21 0.613016803502164 0.773966392995672 0.386983196497836 22 0.651369188998589 0.697261622002822 0.348630811001411 23 0.709534693158385 0.58093061368323 0.290465306841615 24 0.651542321018505 0.69691535796299 0.348457678981495 25 0.604558351347433 0.790883297305135 0.395441648652567 26 0.549754419831305 0.90049116033739 0.450245580168695 27 0.546052932472836 0.907894135054328 0.453947067527164 28 0.658252262437962 0.683495475124076 0.341747737562038 29 0.618286768404771 0.763426463190457 0.381713231595229 30 0.664834520661414 0.670330958677172 0.335165479338586 31 0.63489507380949 0.730209852381019 0.365104926190509 32 0.572279928189967 0.855440143620066 0.427720071810033 33 0.514426987626673 0.971146024746654 0.485573012373327 34 0.536378975033684 0.927242049932633 0.463621024966316 35 0.539832066588601 0.920335866822799 0.460167933411399 36 0.642739305432647 0.714521389134706 0.357260694567353 37 0.689409922252719 0.621180155494563 0.310590077747281 38 0.642095936594981 0.715808126810038 0.357904063405019 39 0.622372622364435 0.75525475527113 0.377627377635565 40 0.596323436769128 0.807353126461744 0.403676563230872 41 0.678792808767049 0.642414382465902 0.321207191232951 42 0.705380014511917 0.589239970976166 0.294619985488083 43 0.657802725307396 0.684394549385208 0.342197274692604 44 0.605495286632869 0.789009426734261 0.394504713367131 45 0.5623772787866 0.8752454424268 0.4376227212134 46 0.580980905003055 0.83803818999389 0.419019094996945 47 0.583028088141574 0.833943823716852 0.416971911858426 48 0.548285696036745 0.90342860792651 0.451714303963255 49 0.494460683298863 0.988921366597726 0.505539316701137 50 0.514129282017002 0.971741435965996 0.485870717982998 51 0.468659360351272 0.937318720702545 0.531340639648728 52 0.546287926467576 0.907424147064848 0.453712073532424 53 0.509044306244229 0.98191138751154 0.49095569375577 54 0.456195980212432 0.912391960424864 0.543804019787568 55 0.605761758962616 0.788476482074768 0.394238241037384 56 0.59891177445751 0.80217645108498 0.40108822554249 57 0.585346924409466 0.829306151181067 0.414653075590533 58 0.583203335352553 0.833593329294894 0.416796664647447 59 0.53341217694313 0.933175646113739 0.466587823056869 60 0.50082169265435 0.9983566146913 0.49917830734565 61 0.890056061770577 0.219887876458845 0.109943938229423 62 0.866091844477667 0.267816311044666 0.133908155522333 63 0.850296273540294 0.299407452919413 0.149703726459706 64 0.856356346542124 0.287287306915752 0.143643653457876 65 0.850487017156492 0.299025965687017 0.149512982843508 66 0.903513510033248 0.192972979933503 0.0964864899667516 67 0.887872848752004 0.224254302495991 0.112127151247995 68 0.862415284449876 0.275169431100247 0.137584715550124 69 0.83275437172842 0.33449125654316 0.16724562827158 70 0.802756223504256 0.394487552991488 0.197243776495744 71 0.800008954196511 0.399982091606978 0.199991045803489 72 0.76827845431229 0.46344309137542 0.23172154568771 73 0.726883789841948 0.546232420316103 0.273116210158052 74 0.695887867315551 0.608224265368899 0.304112132684449 75 0.67865465157035 0.6426906968593 0.32134534842965 76 0.8215232250656 0.356953549868798 0.178476774934399 77 0.797684517824846 0.404630964350309 0.202315482175154 78 0.852541376768685 0.29491724646263 0.147458623231315 79 0.822141055237019 0.355717889525962 0.177858944762981 80 0.957440711120714 0.085118577758572 0.042559288879286 81 0.944161381781243 0.111677236437513 0.0558386182187565 82 0.949565398893566 0.100869202212868 0.050434601106434 83 0.952443528639113 0.0951129427217737 0.0475564713608868 84 0.973169238232161 0.0536615235356773 0.0268307617678386 85 0.967985776428156 0.0640284471436882 0.0320142235718441 86 0.959818761238725 0.0803624775225508 0.0401812387612754 87 0.94634638260515 0.107307234789701 0.0536536173948504 88 0.930728254349649 0.138543491300702 0.0692717456503512 89 0.934018407818188 0.131963184363625 0.0659815921818124 90 0.920344838325637 0.159310323348726 0.0796551616743628 91 0.923596019022385 0.15280796195523 0.076403980977615 92 0.90617881461078 0.187642370778441 0.0938211853892204 93 0.889655428231102 0.220689143537797 0.110344571768898 94 0.86005568622936 0.279888627541279 0.139944313770639 95 0.827886083901078 0.344227832197843 0.172113916098922 96 0.804739778181151 0.390520443637697 0.195260221818848 97 0.809230810276744 0.381538379446512 0.190769189723256 98 0.844103643313096 0.311792713373807 0.155896356686904 99 0.8762884191157 0.247423161768599 0.123711580884299 100 0.846417002065755 0.30716599586849 0.153582997934245 101 0.818392150942188 0.363215698115624 0.181607849057812 102 0.797682816737323 0.404634366525355 0.202317183262677 103 0.752120476958767 0.495759046082467 0.247879523041233 104 0.732568375197845 0.53486324960431 0.267431624802155 105 0.690985245134709 0.618029509730583 0.309014754865291 106 0.810567079289447 0.378865841421106 0.189432920710553 107 0.809164599337758 0.381670801324484 0.190835400662242 108 0.777083454341987 0.445833091316026 0.222916545658013 109 0.726264912604441 0.547470174791118 0.273735087395559 110 0.665832686119982 0.668334627760036 0.334167313880018 111 0.609822680300012 0.780354639399976 0.390177319699988 112 0.5438837859433 0.9122324281134 0.4561162140567 113 0.539737075403439 0.920525849193122 0.460262924596561 114 0.461860613461328 0.923721226922657 0.538139386538672 115 0.573600787684625 0.85279842463075 0.426399212315375 116 0.661364236778515 0.67727152644297 0.338635763221485 117 0.611035737132192 0.777928525735615 0.388964262867808 118 0.705526106285814 0.588947787428371 0.294473893714186 119 0.836964953462618 0.326070093074763 0.163035046537382 120 0.787506824552378 0.424986350895243 0.212493175447622 121 0.86578825848455 0.268423483030898 0.134211741515449 122 0.832577113744846 0.334845772510308 0.167422886255154 123 0.738616303892379 0.522767392215241 0.261383696107621 124 0.90254947291138 0.194901054177239 0.0974505270886195 125 0.820856218646843 0.358287562706314 0.179143781353157 126 0.81908140633684 0.36183718732632 0.18091859366316 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 0 0 OK 10% type I error level 5 0.0431034482758621 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

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

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

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Software written by Ed van Stee & Patrick Wessa

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