Meervoudige regressie Happiness 3

*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 15:35:45 +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/t1290353670wnn84qrk7nj7j93.htm/, Retrieved Sun, 21 Nov 2010 16:34:30 +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/t1290353670wnn84qrk7nj7j93.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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15 10 77 11 6 9 20 63 26 5 12 16 73 26 20 15 10 76 15 12 17 8 90 10 11 14 14 67 21 12 9 19 69 27 11 11 23 54 21 13 13 9 54 21 9 16 12 76 22 14 16 14 75 29 12 15 13 76 29 18 10 11 80 29 9 16 11 89 30 15 12 10 73 19 12 15 12 74 19 12 13 18 78 22 12 18 12 76 18 15 13 10 69 28 11 17 15 74 17 13 14 15 82 18 10 13 12 77 20 17 13 9 84 16 13 15 11 75 17 17 15 16 79 25 15 13 17 79 22 13 13 11 88 31 17 16 13 57 38 21 14 9 69 18 12 18 11 86 20 15 9 20 66 23 8 16 8 54 12 15 16 12 85 20 16 17 10 79 15 9 13 11 84 21 13 17 13 70 20 11 15 13 54 30 9 14 13 70 22 15 10 15 54 33 9 13 12 69 25 15 11 13 68 20 14 11 14 66 21 14 15 9 67 16 12 15 9 71 23 15 12 15 54 25 11 17 10 76 18 11 15 13 77 33 9 16 8 71 18 8 14 15 69 18 13 17 13 73 13 12 10 24 46 24 24 11 11 66 19 11 15 13 77 20 11 15 12 77 21 16 7 22 70 18 12 17 11 86 29 18 14 15 38 13 12 18 7 66 26 14 14 14 75 22 16 14 10 64 28 24 9 9 80 28 13 14 12 86 23 11 11 16 54 22 14 16 13 74 28 12 17 11 88 31 21 12 11 63 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 16.295785836819 -0.352003748191891Depression[t] + 0.0327823330796020Belonging[t] -0.0508921511278503ConcernOverMistakes[t] + 0.0834010628945067ParentalExpectations[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) 16.295785836819 1.664632 9.7894 0 0 Depression -0.352003748191891 0.055802 -6.308 0 0 Belonging 0.0327823330796020 0.016405 1.9983 0.047742 0.023871 ConcernOverMistakes -0.0508921511278503 0.031365 -1.6226 0.107066 0.053533 ParentalExpectations 0.0834010628945067 0.051379 1.6233 0.106919 0.053459 Multiple Linear Regression - Regression Statistics Multiple R 0.577439805903951 R-squared 0.333436729442392 Adjusted R-squared 0.313237842455798 F-TEST (value) 16.5076783519652 F-TEST (DF numerator) 4 F-TEST (DF denominator) 132 p-value 5.43735056979244e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.96470138552339 Sum Squared Residuals 509.526802524634 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 15.2405807169901 -0.240580716990116 2 9 10.4148072421445 -1.41480724214455 3 12 13.4016615091257 -1.40166150912573 4 15 15.5046361567662 -0.504636156766184 5 17 16.8386560090091 0.161343990990863 6 14 13.4962272595151 0.503772740484899 7 9 11.4130192150532 -2.41301921505324 8 11 9.98542425864777 1.01457574135223 9 13 14.5798724817562 -1.57987248175621 10 16 14.6111857282765 1.38881427172354 11 16 13.3513487151291 2.64865128487089 12 15 14.2365411737676 0.763458826232353 13 10 14.3210684364193 -4.32106843641928 14 16 15.0656236603749 0.934376339625117 15 12 15.2027205530160 -3.20272055301598 16 15 14.5314953897118 0.468504610288203 17 13 12.3979257794953 0.602074220504689 18 18 14.8981553956824 3.10184460431763 19 13 14.5301607976524 -1.53016079765241 20 17 13.6606695102863 3.33933048971367 21 14 13.6218328351118 0.378167164888222 22 13 14.9959555522953 -1.99595555229529 23 13 16.1514074813615 -3.15140748136155 24 15 15.4350710877115 -0.435071087711524 25 15 13.2322423442587 1.76775765574134 26 13 12.8661129236613 0.133887076338690 27 13 15.1487513019564 -2.14875130195644 28 16 13.4058506737881 2.59414932621192 29 14 15.4744871200173 -1.47448712001731 30 18 15.4761981724146 2.52380182758542 31 9 10.9160338834504 -1.91603388345043 32 16 15.8903119674658 0.109688032534207 33 16 15.1748131540376 0.825186845962405 34 17 15.3527799673215 1.64722003267853 35 13 15.1929392293385 -2.19293922933851 36 17 13.9140690951791 3.08593090482086 37 15 12.713828128838 2.28617187116201 38 14 14.1458890445015 -0.145889044501468 39 10 11.8571441790707 -1.85714417907066 40 13 14.3124340062302 -1.31243400623021 41 11 14.0987076177035 -3.09870761770346 42 11 13.6302470522245 -2.63024705222451 43 15 15.5107067561138 -0.510706756113806 44 15 15.5357942192208 -0.535794219220782 45 12 12.4310835138825 -0.431083513882478 46 17 15.2685586404881 1.73144135951187 47 15 13.3151453362853 1.68485466371471 48 16 15.5584512827904 0.441548717209623 49 14 13.4458656937605 0.554134306239527 50 17 14.4520622152074 2.54793778479259 51 10 10.1358970842751 -0.135897084275084 52 11 14.5378394103724 -3.53783941037237 53 15 14.1435454267364 0.856454573263645 54 15 14.8616623382729 0.138337661727071 55 7 10.9312207266023 -3.93122072660233 56 17 15.2683720009474 1.73162799905255 57 14 12.6006730610376 1.39932693896244 58 18 15.8398125339285 2.16018746607150 59 14 14.0411980246021 -0.0411980246020936 60 14 15.450462949883 -1.45046294988299 61 9 15.4095723355089 -6.40957233550893 62 14 14.6379137192611 -0.637913719261112 63 11 12.4819594077577 -1.48195940775766 64 16 13.7214622813693 2.27853771863075 65 17 15.4823555535345 1.51764444646553 66 12 14.6430610156450 -2.64306101564496 67 15 14.1121302002215 0.887869799778519 68 15 15.2496285585010 -0.249628558501041 69 16 15.5155816902150 0.484418309784973 70 16 15.9792541033650 0.0207458966349813 71 11 12.8087378250599 -1.80873782505990 72 12 13.3139323547559 -1.31393235475590 73 14 13.8329953927971 0.167004607202922 74 15 15.2409409200754 -0.24094092007536 75 17 15.1766424436822 1.82335755631784 76 19 14.9786822554385 4.02131774456146 77 15 14.3316375095302 0.668362490469764 78 16 13.8683415567181 2.13165844328187 79 14 14.5093689115154 -0.509368911515423 80 16 11.1604398940993 4.83956010590073 81 15 14.3683877310000 0.631612269000046 82 17 14.4188685930322 2.58113140696781 83 12 14.5309485470859 -2.53094854708591 84 18 14.7135168731581 3.28648312684194 85 13 14.6911657454067 -1.69116574540674 86 14 12.8009236588097 1.19907634119033 87 14 14.2921346954522 -0.292134695452183 88 14 14.2303837926478 -0.230383792647757 89 12 14.3319109308432 -2.33191093084318 90 14 12.7376494021790 1.26235059782103 91 12 13.5245111188314 -1.52451111883137 92 15 14.4260003631263 0.573999636873744 93 11 12.1422682995793 -1.14226829957934 94 15 14.9611192800162 0.0388807199838129 95 14 14.0141271468152 -0.0141271468152189 96 15 13.7683702868543 1.23162971314568 97 16 14.8981716529350 1.10182834706496 98 14 10.4671902325933 3.53280976740666 99 18 16.1804628328586 1.81953716714136 100 14 15.4175081122892 -1.41750811228917 101 13 12.9951547432446 0.00484525675543712 102 14 12.9693663124764 1.03063368752364 103 14 14.9275166829977 -0.927516682997748 104 17 15.2722693648180 1.72773063518203 105 12 13.4324593684951 -1.43245936849512 106 16 13.780136084242 2.21986391575800 107 10 12.9081459950452 -2.90814599504515 108 13 14.6985871940664 -1.69858719406643 109 15 15.01083096412 -0.0108309641199875 110 16 14.9762377208747 1.02376227912530 111 14 13.1534905068801 0.846509493119878 112 13 12.7830710048217 0.216928995178302 113 17 14.9335872823454 2.06641271765463 114 14 13.7370245258287 0.262975474171276 115 16 13.1576993020778 2.84230069792216 116 12 14.5951623639331 -2.59516236393305 117 16 13.7096791676986 2.29032083230141 118 8 10.5082467927768 -2.50824679277680 119 9 12.1718704937023 -3.17187049370232 120 13 12.5990498494429 0.400950150557129 121 19 15.3531391113764 3.64686088862363 122 11 12.4399425936265 -1.43994259362648 123 15 15.1675783449186 -0.167578344918592 124 11 13.9316320706015 -2.9316320706015 125 15 15.6892909365432 -0.68929093654317 126 16 15.647274122412 0.352725877588004 127 15 13.0229947989599 1.97700520104013 128 12 13.1447540966964 -1.14475409669643 129 16 14.1142166539263 1.88578334607368 130 15 13.7653605301857 1.23463946981429 131 13 14.6876254033649 -1.68762540336491 132 14 14.4955329176756 -0.495532917675576 133 11 13.3142925578411 -2.31429255784111 134 15 14.1872732932648 0.8127267067352 135 14 12.5795186575630 1.42048134243703 136 13 15.6456335945343 -2.64563359453432 137 15 16.1068593508943 -1.10685935089426 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0732841630127285 0.146568326025457 0.926715836987272 9 0.0413584146224721 0.0827168292449442 0.958641585377528 10 0.181623260368474 0.363246520736949 0.818376739631526 11 0.479599440400899 0.959198880801798 0.520400559599101 12 0.363089465915866 0.726178931831731 0.636910534084134 13 0.585625709398543 0.828748581202914 0.414374290601457 14 0.536707821802155 0.92658435639569 0.463292178197845 15 0.623805106967883 0.752389786064234 0.376194893032117 16 0.546800243505382 0.906399512989237 0.453199756494618 17 0.459870221212666 0.919740442425332 0.540129778787334 18 0.526222067849517 0.947555864300965 0.473777932150483 19 0.449589374433823 0.899178748867647 0.550410625566177 20 0.510097440916511 0.979805118166978 0.489902559083489 21 0.432525774054861 0.865051548109721 0.567474225945139 22 0.515737820999333 0.968524358001333 0.484262179000667 23 0.62944895768676 0.74110208462648 0.37055104231324 24 0.570708460295176 0.858583079409647 0.429291539704824 25 0.53575239661337 0.928495206773259 0.464247603386630 26 0.471269115158822 0.942538230317645 0.528730884841178 27 0.452756220029514 0.905512440059029 0.547243779970486 28 0.520055938184432 0.959888123631135 0.479944061815568 29 0.471813535241607 0.943627070483214 0.528186464758393 30 0.512832754785012 0.974334490429975 0.487167245214988 31 0.496174036446225 0.99234807289245 0.503825963553775 32 0.434160999169758 0.868321998339516 0.565839000830242 33 0.377969013896649 0.755938027793298 0.622030986103351 34 0.402880894462131 0.805761788924262 0.597119105537869 35 0.406780348526123 0.813560697052247 0.593219651473877 36 0.518169439522365 0.963661120955269 0.481830560477635 37 0.588799113936846 0.822401772126308 0.411200886063154 38 0.535052506701184 0.929894986597633 0.464947493298816 39 0.510669284505735 0.97866143098853 0.489330715494265 40 0.4813400144886 0.9626800289772 0.5186599855114 41 0.575654128332074 0.848691743335852 0.424345871667926 42 0.622263335303044 0.755473329393911 0.377736664696956 43 0.57058584864392 0.85882830271216 0.42941415135608 44 0.518282124291765 0.96343575141647 0.481717875708235 45 0.465570133825168 0.931140267650335 0.534429866174832 46 0.468942269884758 0.937884539769515 0.531057730115242 47 0.480621713446976 0.961243426893951 0.519378286553025 48 0.439631908280094 0.879263816560188 0.560368091719906 49 0.390919647308881 0.781839294617761 0.60908035269112 50 0.420242228811847 0.840484457623694 0.579757771188153 51 0.380393346052336 0.760786692104672 0.619606653947664 52 0.479790002388065 0.95958000477613 0.520209997611935 53 0.438148094573044 0.876296189146089 0.561851905426956 54 0.387688051977723 0.775376103955446 0.612311948022277 55 0.539735762637849 0.920528474724303 0.460264237362151 56 0.521304635542008 0.957390728915984 0.478695364457992 57 0.505515460524605 0.98896907895079 0.494484539475395 58 0.515009960856588 0.969980078286824 0.484990039143412 59 0.464209765559933 0.928419531119867 0.535790234440066 60 0.447954860009965 0.895909720019929 0.552045139990035 61 0.848327156520388 0.303345686959224 0.151672843479612 62 0.821389668195662 0.357220663608676 0.178610331804338 63 0.80489744225482 0.390205115490361 0.195102557745181 64 0.81671215117164 0.366575697656721 0.183287848828361 65 0.802065876860183 0.395868246279634 0.197934123139817 66 0.826184732149248 0.347630535701505 0.173815267850752 67 0.799815293725088 0.400369412549823 0.200184706274912 68 0.763316564996774 0.473366870006451 0.236683435003226 69 0.725450449811901 0.549099100376198 0.274549550188099 70 0.681924442251647 0.636151115496705 0.318075557748353 71 0.683998205190294 0.632003589619413 0.316001794809706 72 0.659624735257503 0.680750529484993 0.340375264742497 73 0.613048843087564 0.773902313824871 0.386951156912436 74 0.56523245494708 0.86953509010584 0.43476754505292 75 0.558743574393523 0.882512851212954 0.441256425606477 76 0.705263351019125 0.58947329796175 0.294736648980875 77 0.665145438731063 0.669709122537874 0.334854561268937 78 0.6701069583107 0.6597860833786 0.3298930416893 79 0.626361622219987 0.747276755560025 0.373638377780013 80 0.850462996831906 0.299074006336188 0.149537003168094 81 0.824003920935552 0.351992158128896 0.175996079064448 82 0.849414255486803 0.301171489026393 0.150585744513197 83 0.869552150226566 0.260895699546867 0.130447849773434 84 0.92326164590972 0.153476708180559 0.0767383540902793 85 0.91763037540818 0.164739249183639 0.0823696245918196 86 0.902902821728002 0.194194356543995 0.0970971782719977 87 0.878001173749225 0.243997652501549 0.121998826250775 88 0.84887026884961 0.302259462300781 0.151129731150390 89 0.86081227043935 0.278375459121302 0.139187729560651 90 0.844645074417291 0.310709851165418 0.155354925582709 91 0.839102760163104 0.321794479673792 0.160897239836896 92 0.80698426574348 0.386031468513041 0.193015734256521 93 0.775266677281568 0.449466645436863 0.224733322718432 94 0.732849387510732 0.534301224978536 0.267150612489268 95 0.699477295347295 0.601045409305411 0.300522704652705 96 0.673627692663029 0.652744614673942 0.326372307336971 97 0.642672767626638 0.714654464746724 0.357327232373362 98 0.833416483087699 0.333167033824602 0.166583516912301 99 0.85965176160142 0.280696476797161 0.140348238398581 100 0.833695115446326 0.332609769107347 0.166304884553674 101 0.793834547035877 0.412330905928247 0.206165452964123 102 0.758560280825116 0.482879438349768 0.241439719174884 103 0.71678279227986 0.566434415440279 0.283217207720140 104 0.704500990924244 0.590998018151511 0.295499009075756 105 0.67664915182558 0.646701696348839 0.323350848174419 106 0.780243678425236 0.439512643149528 0.219756321574764 107 0.813170472681997 0.373659054636006 0.186829527318003 108 0.802927594851541 0.394144810296917 0.197072405148459 109 0.760367988460329 0.479264023079343 0.239632011539671 110 0.726052715566932 0.547894568866137 0.273947284433068 111 0.678223478729696 0.643553042540608 0.321776521270304 112 0.613840172677905 0.77231965464419 0.386159827322095 113 0.610614789885845 0.77877042022831 0.389385210114155 114 0.541787518370017 0.916424963259966 0.458212481629983 115 0.660865630097632 0.678268739804737 0.339134369902368 116 0.730232479766025 0.539535040467949 0.269767520233975 117 0.677534503280607 0.644930993438785 0.322465496719392 118 0.744642015101051 0.510715969797898 0.255357984898949 119 0.739864885143378 0.520270229713244 0.260135114856622 120 0.74661197070844 0.506776058583119 0.253388029291559 121 0.945317135775299 0.109365728449403 0.0546828642247014 122 0.934434685878776 0.131130628242448 0.065565314121224 123 0.891634511376772 0.216730977246456 0.108365488623228 124 0.92814891030849 0.143702179383022 0.0718510896915108 125 0.88214377164656 0.235712456706880 0.117856228353440 126 0.80440866803698 0.391182663926041 0.195591331963020 127 0.70876471306956 0.58247057386088 0.29123528693044 128 0.680948516831039 0.638102966337923 0.319051483168961 129 0.578552934539392 0.842894130921216 0.421447065460608 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 1 0.00819672131147541 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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