*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, 23 Nov 2010 13:01:30 +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/23/t1290517233c2qf0vauesx37dq.htm/, Retrieved Tue, 23 Nov 2010 14:00:33 +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/23/t1290517233c2qf0vauesx37dq.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 «

38 23 10 11 35 37 12 36 15 10 11 35 37 12 23 25 10 11 35 37 12 30 18 10 11 35 37 12 26 21 10 11 35 37 12 26 19 10 11 35 37 12 30 15 13 12 38 34 12 27 22 10 11 35 37 12 34 19 10 11 35 37 14 28 20 13 9 34 32 12 36 26 10 11 35 37 12 42 26 10 11 35 37 12 31 21 10 11 35 37 14 26 19 10 11 35 37 12 16 19 13 12 38 34 12 23 19 10 11 35 37 14 45 28 10 11 35 37 12 30 27 10 11 35 37 15 45 18 10 11 35 37 12 30 19 10 11 35 37 15 24 24 10 11 35 37 12 29 21 13 12 38 34 12 30 22 13 9 34 32 12 31 25 10 11 35 37 14 34 15 10 11 35 37 14 41 34 10 11 35 37 12 37 23 10 11 35 37 12 33 19 10 11 35 37 12 48 15 10 11 35 37 14 44 15 10 11 35 37 15 29 17 10 11 35 37 14 44 30 13 9 34 32 12 43 28 10 11 35 37 14 31 23 10 11 35 37 14 28 23 10 11 35 37 12 26 21 10 11 35 37 14 30 18 10 11 35 37 12 27 19 15 11 33 36 12 34 24 10 11 35 37 12 47 15 10 11 35 37 12 37 24 13 16 34 36 12 27 20 10 11 35 37 12 30 20 10 11 35 37 12 36 44 10 11 35 37 14 39 20 10 11 35 37 12 32 20 10 11 35 37 12 25 20 10 11 35 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 12 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation CM+D[t] = + 44.1744764200473 + 0.355225335188637`PE+PC`[t] -0.223616457775059happiness[t] + 0.253261494075167depression[t] -0.457390995302612connected[t] -0.134163985832665separated[t] + 0.0731026269579095populariteit[t] + 0.00173563261092779t + 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) 44.1744764200473 50.90141 0.8678 0.387241 0.193621 `PE+PC` 0.355225335188637 0.112263 3.1642 0.001978 0.000989 happiness -0.223616457775059 1.298405 -0.1722 0.863557 0.431778 depression 0.253261494075167 0.599177 0.4227 0.673296 0.336648 connected -0.457390995302612 0.676092 -0.6765 0.500033 0.250017 separated -0.134163985832665 1.036453 -0.1294 0.897226 0.448613 populariteit 0.0731026269579095 0.598537 0.1221 0.902999 0.4515 t 0.00173563261092779 0.017724 0.0979 0.922159 0.46108 Multiple Linear Regression - Regression Statistics Multiple R 0.293957093886829 R-squared 0.08641077304639 Adjusted R-squared 0.0322148019559215 F-TEST (value) 1.59441322496364 F-TEST (DF numerator) 7 F-TEST (DF denominator) 118 p-value 0.143625117302426 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.94948698655691 Sum Squared Residuals 5698.85358640621 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 38 32.800585831168 5.19941416883201 2 36 29.9605187822698 6.03948121773016 3 23 33.5145077667671 -10.5145077667671 4 30 31.0296660530576 -1.02966605305760 5 26 32.0970776912344 -6.09707769123444 6 26 31.3883626534681 -5.3883626534681 7 30 28.5819280376646 1.41807196233537 8 27 32.4575099242559 -5.45750992425586 9 34 31.5397748052167 2.46022519478330 10 28 31.7013690820909 -3.70136908209087 11 36 33.8836181628432 2.11638183715681 12 42 33.8853537954541 8.11464620454588 13 31 32.2571680060377 -1.25716800603768 14 26 31.4022477143555 -5.40224771435552 15 16 30.0167144393066 -14.0167144393066 16 23 31.5519242334932 -8.5519242334932 17 45 34.604482628886 10.3955173711140 18 30 34.4703008071820 -4.47030080718205 19 45 31.0557005422215 13.9442994577785 20 30 31.6319693908948 -1.63196939089481 21 24 33.1905238185752 -9.1905238185752 22 29 30.7393145379604 -1.73931453796036 23 30 32.4343829764102 -2.43438297641020 24 31 33.6971613055124 -2.69716130551243 25 34 30.146643586237 3.85335641376300 26 41 36.7514553335162 4.2485446664838 27 37 32.8457122790521 4.15428772094788 28 33 31.4265465709085 1.57345342909149 29 48 30.1535861166807 17.8464138833193 30 44 30.2284243762495 13.7715756237505 31 29 30.8675080522798 -1.86750805227983 32 44 35.2918063514176 8.70819364858235 33 43 34.7784580045767 8.2215419954233 34 31 33.0040669612444 -2.00406696124444 35 28 32.8595973399395 -4.85959733993954 36 26 32.297087556089 -6.29708755608902 37 30 31.0869419292182 -1.08694192921822 38 27 31.3747665845804 -4.37476658458038 39 34 33.2217652055719 0.778234794428106 40 47 30.0264728214851 16.9735271785149 41 37 34.4122495489797 2.58775045102032 42 27 31.8060707626501 -4.80607076265013 43 30 31.8078063952611 -1.80780639526106 44 36 40.4811553263151 -4.48115532631508 45 39 31.8112776604829 7.18872233951709 46 32 31.8130132930938 0.186986706906158 47 25 31.8147489257048 -6.81474892570477 48 19 28.6194565416180 -9.61945654161797 49 29 32.1734455261153 -3.17344552611526 50 26 32.1260197217166 -6.12601972171661 51 31 30.0791972133 0.920802786700005 52 31 32.1786524239480 -1.17865242394805 53 31 30.9787945966782 0.0212054033218439 54 39 31.4716730187926 7.52832698120737 55 28 32.1838593217808 -4.18385932178083 56 22 30.4094682784486 -8.40946827844857 57 31 31.4768799166254 -0.476879916625412 58 36 31.6248208031522 4.37517919684784 59 28 30.4146751762814 -2.41467517628136 60 39 33.2582134904014 5.74178650959862 61 35 32.1942731174464 2.80572688255360 62 33 31.8407834148687 1.15921658513131 63 27 31.487293712291 -4.48729371229098 64 33 32.9099306856565 0.0900693143435477 65 31 31.1355396423242 -0.135539642324197 66 39 31.6387058640396 7.36129413596042 67 37 33.0613428374051 3.93865716259494 68 24 31.7152797562193 -7.71527975621935 69 28 32.5970159466172 -4.59701594661715 70 37 27.9808222217758 9.0191777782242 71 32 33.0682853678488 -1.06828536784877 72 31 29.0499694925636 1.95003050743643 73 29 29.4069304603631 -0.406930460363133 74 40 35.4138643580862 4.58613564191381 75 40 32.7200025631038 7.27999743689616 76 15 29.7337302602899 -14.7337302602899 77 27 29.6863044558912 -2.68630445589121 78 32 32.1746174348226 -0.174617434822594 79 28 32.2255145044431 -4.22551450444309 80 41 36.6361594132335 4.36384058676652 81 47 32.9394364400422 14.0605635599578 82 42 35.4277494189736 6.57225058102639 83 32 33.3393465179255 -1.33934651792547 84 33 33.874401889126 -0.87440188912601 85 29 35.0777309816178 -6.07773098161775 86 37 32.3838691866354 4.61613081336459 87 39 30.6825807702611 8.31741922973894 88 29 30.8202338571869 -1.8202338571869 89 33 32.9533215009296 0.0466784990703529 90 31 31.1297690205878 -0.129769020587818 91 21 31.7551993062707 -10.7551993062707 92 36 34.8808603286214 1.11913967137857 93 32 35.237821296421 -3.23782129642100 94 15 31.5410983232297 -16.5410983232297 95 25 33.6250245299629 -8.62502452996291 96 28 30.1236682476971 -2.12366824769705 97 39 33.3224318970057 5.6775681029943 98 31 29.0123020703434 1.98769792965658 99 40 28.7079738047753 11.2920261952247 100 25 31.5515121188953 -6.5515121188953 101 36 33.8308050165539 2.16919498344613 102 23 29.2146112917125 -6.21461129171252 103 39 30.1358176759735 8.86418232402646 104 31 33.8360119143867 -2.83601191438666 105 23 29.7840636060068 -6.78406360600676 106 31 31.7081311684767 -0.708131168476692 107 28 32.9354014509168 -4.93540145091677 108 47 31.5162357427732 15.4837642572268 109 25 32.141666028456 -7.14166602845602 110 26 30.4540310024291 -4.4540310024291 111 24 32.4053505670329 -8.40535056703291 112 30 32.8573235966661 -2.85732359666608 113 25 33.4715390914006 -8.47153909140057 114 44 31.8714012498333 12.1285987501667 115 38 40.6774878686489 -2.67748786864887 116 36 29.0927048943497 6.9072951056503 117 34 33.3907769823190 0.609223017681041 118 45 32.0593159137007 12.9406840862993 119 29 31.3754690572301 -2.37546905723012 120 25 32.2966754414911 -7.29667544149113 121 30 33.364087079668 -3.36408707966797 122 27 32.237331879356 -5.23733187935599 123 44 33.5137635988056 10.4862364011944 124 31 36.5663219941985 -5.56632199419849 125 35 33.7262549453003 1.27374505469968 126 47 37.2802439297976 9.71975607020239 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.774179551042104 0.451640897915792 0.225820448957896 12 0.814914552090087 0.370170895819826 0.185085447909913 13 0.72975509436928 0.54048981126144 0.27024490563072 14 0.679580019823857 0.640839960352287 0.320419980176143 15 0.760363705884226 0.479272588231548 0.239636294115774 16 0.735212513111103 0.529574973777794 0.264787486888897 17 0.835397848898168 0.329204302203664 0.164602151101832 18 0.780857913752948 0.438284172494104 0.219142086247052 19 0.857893239013864 0.284213521972271 0.142106760986136 20 0.805288854200896 0.389422291598208 0.194711145799104 21 0.876604373702256 0.246791252595489 0.123395626297744 22 0.840928624256653 0.318142751486694 0.159071375743347 23 0.791997253868519 0.416005492262962 0.208002746131481 24 0.738038740143544 0.523922519712913 0.261961259856457 25 0.682355899302825 0.635288201394349 0.317644100697175 26 0.62839773248064 0.74320453503872 0.37160226751936 27 0.560689532339361 0.878620935321277 0.439310467660639 28 0.499372542022045 0.99874508404409 0.500627457977955 29 0.71249463099728 0.57501073800544 0.28750536900272 30 0.758956496259027 0.482087007481946 0.241043503740973 31 0.762808382443775 0.474383235112451 0.237191617556225 32 0.777894568745423 0.444210862509154 0.222105431254577 33 0.76403185000381 0.471936299992379 0.235968149996189 34 0.75439700279998 0.491205994400039 0.245602997200020 35 0.790518038100477 0.418963923799045 0.209481961899523 36 0.815554429497342 0.368891141005316 0.184445570502658 37 0.789637384411293 0.420725231177415 0.210362615588707 38 0.760381215178611 0.479237569642778 0.239618784821389 39 0.714704569009387 0.570590861981226 0.285295430990613 40 0.834593748705746 0.330812502588508 0.165406251294254 41 0.795653793607615 0.40869241278477 0.204346206392385 42 0.810782848086015 0.378434303827969 0.189217151913985 43 0.78789115354617 0.424217692907659 0.212108846453829 44 0.753370967129224 0.493258065741552 0.246629032870776 45 0.735645800668017 0.528708398663965 0.264354199331983 46 0.697571750898562 0.604856498202877 0.302428249101438 47 0.722566127098248 0.554867745803505 0.277433872901752 48 0.791914941272433 0.416170117455135 0.208085058727567 49 0.760261300887548 0.479477398224904 0.239738699112452 50 0.753022635809898 0.493954728380204 0.246977364190102 51 0.720558590613666 0.558882818772668 0.279441409386334 52 0.67454694461552 0.65090611076896 0.32545305538448 53 0.625885026486799 0.748229947026402 0.374114973513201 54 0.625898778105433 0.748202443789135 0.374101221894567 55 0.594685300307455 0.81062939938509 0.405314699692545 56 0.621003818403279 0.757992363193443 0.378996181596721 57 0.568570171014348 0.862859657971304 0.431429828985652 58 0.531804279601989 0.936391440796022 0.468195720398011 59 0.484653100738208 0.969306201476416 0.515346899261792 60 0.465273907045757 0.930547814091514 0.534726092954243 61 0.419216174698232 0.838432349396464 0.580783825301768 62 0.368164539332107 0.736329078664214 0.631835460667893 63 0.338968941226912 0.677937882453824 0.661031058773088 64 0.290778186550027 0.581556373100054 0.709221813449973 65 0.246114912334244 0.492229824668488 0.753885087665756 66 0.246563789046622 0.493127578093244 0.753436210953378 67 0.218550156910042 0.437100313820084 0.781449843089958 68 0.230191679314533 0.460383358629066 0.769808320685467 69 0.216516848130624 0.433033696261248 0.783483151869376 70 0.237642505975714 0.475285011951427 0.762357494024286 71 0.199056421890421 0.398112843780842 0.800943578109579 72 0.165263764180525 0.330527528361049 0.834736235819476 73 0.137853213349985 0.275706426699971 0.862146786650015 74 0.120259306736045 0.240518613472091 0.879740693263955 75 0.12198667259053 0.24397334518106 0.87801332740947 76 0.238592203774678 0.477184407549356 0.761407796225322 77 0.204662099628355 0.40932419925671 0.795337900371645 78 0.168464827229386 0.336929654458771 0.831535172770614 79 0.149403321744132 0.298806643488264 0.850596678255868 80 0.129302220663439 0.258604441326878 0.870697779336561 81 0.229585760662797 0.459171521325595 0.770414239337203 82 0.236292949702404 0.472585899404808 0.763707050297596 83 0.212569128066595 0.42513825613319 0.787430871933405 84 0.179451976060826 0.358903952121651 0.820548023939174 85 0.159692569325329 0.319385138650658 0.840307430674671 86 0.152342608833821 0.304685217667643 0.847657391166179 87 0.203465183526283 0.406930367052566 0.796534816473717 88 0.166326741946095 0.332653483892189 0.833673258053905 89 0.138055697481964 0.276111394963928 0.861944302518036 90 0.107598454406910 0.215196908813819 0.89240154559309 91 0.118636342451963 0.237272684903926 0.881363657548037 92 0.106710675819959 0.213421351639918 0.89328932418004 93 0.0875151326248615 0.175030265249723 0.912484867375138 94 0.188170497945809 0.376340995891617 0.811829502054191 95 0.2102334683117 0.4204669366234 0.7897665316883 96 0.169570136116615 0.339140272233231 0.830429863883385 97 0.159610276331897 0.319220552663793 0.840389723668103 98 0.125918466942368 0.251836933884736 0.874081533057632 99 0.200456288198354 0.400912576396709 0.799543711801646 100 0.171194694427104 0.342389388854208 0.828805305572896 101 0.155642041125033 0.311284082250066 0.844357958874967 102 0.125687219584505 0.251374439169010 0.874312780415495 103 0.204519567438753 0.409039134877506 0.795480432561247 104 0.171336019050186 0.342672038100373 0.828663980949814 105 0.130767535893526 0.261535071787052 0.869232464106474 106 0.121340026922736 0.242680053845471 0.878659973077264 107 0.119035989287344 0.238071978574688 0.880964010712656 108 0.248481430776516 0.496962861553032 0.751518569223484 109 0.188895265581788 0.377790531163575 0.811104734418212 110 0.191228157387280 0.382456314774559 0.80877184261272 111 0.131693393391259 0.263386786782519 0.86830660660874 112 0.086731767012228 0.173463534024456 0.913268232987772 113 0.1730555158978 0.3461110317956 0.8269444841022 114 0.115174046233149 0.230348092466297 0.884825953766851 115 0.0658574566019557 0.131714913203911 0.934142543398044 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 0 0 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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