computation 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: Mon, 13 Dec 2010 09:55:39 +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/13/t1292234033uu7twe2d9n939zg.htm/, Retrieved Mon, 13 Dec 2010 10:54:04 +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/13/t1292234033uu7twe2d9n939zg.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 «

0 1 23 14 11 12 24 26 1 1 25 11 7 8 25 23 1 0 17 6 17 8 30 25 0 1 18 12 10 8 19 23 1 0 16 10 12 7 22 29 1 1 20 10 11 4 25 25 1 1 16 11 11 11 23 21 1 1 18 16 12 7 17 22 1 1 17 11 13 7 21 25 0 1 23 13 14 12 19 24 1 1 30 12 16 10 19 18 1 1 18 12 10 8 16 15 0 1 15 11 11 8 23 22 0 1 12 4 15 4 27 28 1 1 21 9 9 9 22 20 0 1 20 8 17 7 22 24 1 1 27 15 11 9 23 21 0 1 34 16 18 11 21 20 1 1 21 9 14 13 19 21 0 1 31 14 10 8 18 23 0 1 19 11 11 8 20 28 1 1 16 8 15 9 23 24 1 1 20 9 15 6 25 24 0 1 21 9 13 9 19 24 0 1 22 9 16 9 24 23 1 1 17 9 13 6 22 23 0 1 24 10 9 6 25 29 1 1 25 16 18 16 26 24 1 1 26 11 18 5 29 18 1 1 25 8 12 7 32 25 1 1 17 9 17 9 25 21 0 1 32 16 9 6 29 26 0 1 33 11 9 6 28 22 0 0 32 12 18 12 28 22 0 1 25 12 12 7 29 23 0 1 29 14 18 10 26 30 1 1 22 9 14 9 25 23 0 1 18 10 15 8 14 17 1 1 17 9 16 5 25 23 0 1 20 10 10 8 26 23 0 1 15 12 11 8 20 25 1 1 20 14 14 10 18 24 0 1 33 14 9 6 32 24 1 1 23 14 17 7 25 21 0 1 26 16 5 4 23 24 0 1 18 9 12 8 21 24 1 1 20 10 12 8 20 28 1 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation PE[t] = + 7.0858875722877 + 0.183435133232890Gender[t] -0.545038050049476Browser[t] + 0.0988881894907669CM[t] -0.161490160234604D[t] + 0.679434014809388PC[t] + 0.103503327554848PS[t] -0.0975307307421319O[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) 7.0858875722877 2.643741 2.6803 0.008467 0.004234 Gender 0.183435133232890 0.541619 0.3387 0.735485 0.367743 Browser -0.545038050049476 0.933643 -0.5838 0.560545 0.280272 CM 0.0988881894907669 0.057142 1.7306 0.086282 0.043141 D -0.161490160234604 0.10606 -1.5226 0.130671 0.065335 PC 0.679434014809388 0.099678 6.8163 0 0 PS 0.103503327554848 0.072566 1.4263 0.156553 0.078276 O -0.0975307307421319 0.079612 -1.2251 0.223118 0.111559 Multiple Linear Regression - Regression Statistics Multiple R 0.66076995366636 R-squared 0.436616931668244 Adjusted R-squared 0.401405489897509 F-TEST (value) 12.399859526091 F-TEST (DF numerator) 7 F-TEST (DF denominator) 112 p-value 1.12768683280251e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.73031089254118 Sum Squared Residuals 834.914927832051 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11 14.6559046769749 -3.65590467697488 2 7 13.1999461304369 -6.19994613043689 3 17 14.0837846420232 2.91621535797677 4 10 11.5417835452050 -1.54178354520495 5 12 11.4413522533774 0.558647746622649 6 11 9.95419782249585 1.04580217750415 7 11 14.3363092758227 -3.33630927582272 8 12 10.2903480983225 1.70965190167753 9 13 11.1203318279977 1.87966817200229 10 14 14.4949396609196 -0.494939660919589 11 16 14.7583986356565 1.24160136434353 12 10 12.1949545417103 -2.19495454171034 13 11 11.9181531779288 -0.918153177928768 14 15 9.86301259762774 5.13698740237226 15 9 13.7888899173143 -4.78888991731427 16 17 11.9190658022379 5.08093419776209 17 11 13.4192506896640 -2.41925068966396 18 18 15.0159348278830 2.98406517211695 19 14 16.0985852631451 -2.09858526314514 20 10 12.4008463605609 -2.40084636056085 21 11 11.4180115687745 -0.4180115687745 22 15 13.1693195346814 1.83068046531864 23 15 11.5720867430914 3.42791325690865 24 13 12.9048218784483 0.095178121551692 25 16 13.6187574364554 2.38124256354455 26 13 11.0624429226966 1.93755707730336 27 9 11.1350605538763 -2.13506055387627 28 18 17.8339400445517 0.166059955448315 29 18 12.1621992394295 5.83780076057046 30 12 13.5344444277310 -1.53444442773098 31 17 13.6063164112736 3.39368358872639 32 9 11.6638306108406 -2.66383061084057 33 9 12.8567891969180 -3.85678919691803 34 18 17.2180529860985 0.781947013901536 35 12 12.5896001323794 -0.589600132379398 36 18 13.7072495164420 4.29275048355805 37 14 13.9056958972432 0.0943041027568168 38 15 11.9324316123527 3.0675683876473 39 16 10.6935188905518 5.3064811094482 40 10 12.7870635375396 -2.78706353753962 41 11 11.1535608428032 -0.153560842803225 42 14 12.7578487082720 1.24215129172805 43 9 12.5912705649493 -3.59127056494935 44 17 12.0333267174264 4.96667328257358 45 5 9.28567494043237 -4.28567494043237 46 12 12.1357299502763 -0.135729950276316 47 12 11.8618250517328 0.13817494826724 48 6 9.55290805914807 -3.55290805914807 49 24 22.7609630540934 1.23903694590660 50 12 12.2871662874508 -0.287166287450808 51 12 12.7781521267855 -0.778152126785512 52 14 11.3450353355502 2.65496466444982 53 7 8.86102547914969 -1.86102547914969 54 12 12.9292550275173 -0.929255027517349 55 14 11.7195738872323 2.28042611276773 56 8 12.4797133068118 -4.47971330681183 57 11 9.03535630531985 1.96464369468015 58 9 11.3120665608731 -2.31206656087307 59 11 13.6862699014457 -2.68626990144573 60 10 8.9363701884504 1.06362981154960 61 11 13.0554817715483 -2.05548177154827 62 12 12.8816612577715 -0.881661257771504 63 9 11.8361499889327 -2.83614998893272 64 18 14.78083363678 3.21916636321999 65 15 12.0678851492938 2.93211485070618 66 12 12.8412047354000 -0.841204735399983 67 13 9.37743944128856 3.62256055871144 68 14 12.9142965136419 1.08570348635807 69 10 11.4695006604623 -1.46950066046231 70 13 12.2352734000325 0.764726599967456 71 13 13.4864443774018 -0.486444377401791 72 11 12.7395988425588 -1.73959884255884 73 13 11.9359788444194 1.06402115558063 74 16 14.4513129625635 1.54868703743649 75 11 10.9335540850930 0.066445914907022 76 16 17.7162620133827 -1.71626201338274 77 14 11.7053713548100 2.29462864518996 78 8 10.6081455237930 -2.60814552379302 79 9 9.80522667696963 -0.805226676969631 80 15 11.5045507689094 3.49544923109062 81 11 13.3008939771595 -2.30089397715946 82 21 17.1044325875112 3.89556741248882 83 14 12.8727485760813 1.12725142391869 84 18 15.7713027784332 2.22869722156683 85 12 11.5372663345195 0.462733665480453 86 13 12.7303713522838 0.269628647716215 87 12 10.8887632097711 1.11123679022885 88 19 14.5525520104917 4.44744798950828 89 11 12.5904311449104 -1.5904311449104 90 13 14.9421321716421 -1.94213217164207 91 15 14.6383291729810 0.361670827019040 92 12 11.0699106423073 0.930089357692687 93 16 15.8767632152245 0.123236784775516 94 18 18.0494645258542 -0.0494645258542125 95 8 14.8775012544052 -6.8775012544052 96 9 10.7391437368534 -1.73914373685338 97 15 12.8034338179029 2.1965661820971 98 6 10.6240885873558 -4.62408858735575 99 8 9.47425497652523 -1.47425497652523 100 10 10.1147959936383 -0.114795993638284 101 11 12.3094096632018 -1.30940966320181 102 14 12.6420474765997 1.35795252340027 103 11 13.4150230317298 -2.41502303172979 104 12 11.8964642589798 0.103535741020171 105 11 10.0104849099828 0.989515090017181 106 9 9.35216355360232 -0.352163553602315 107 12 11.8509184514819 0.149081548518095 108 20 14.3523209541413 5.64767904585869 109 13 13.5869666557782 -0.586966655778232 110 12 16.4472071788148 -4.44720717881483 111 9 14.5601205335658 -5.56012053356579 112 24 21.6236818273833 2.37631817261671 113 11 11.5386559819757 -0.538655981975724 114 17 15.1858125256286 1.81418747437139 115 11 12.7726684206994 -1.77266842069936 116 11 14.2319338970278 -3.23193389702784 117 16 12.4881258394392 3.51187416056081 118 13 10.9469944015164 2.05300559848364 119 11 12.6276710266755 -1.62767102667551 120 19 17.8858621564712 1.11413784352882 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.872473758852306 0.255052482295388 0.127526241147694 12 0.864900183006599 0.270199633986802 0.135099816993401 13 0.785341844994943 0.429316310010115 0.214658155005057 14 0.749592614640464 0.500814770719073 0.250407385359536 15 0.808894563032368 0.382210873935264 0.191105436967632 16 0.814996778976038 0.370006442047925 0.185003221023963 17 0.761784385763786 0.476431228472429 0.238215614236214 18 0.772454008284135 0.455091983431729 0.227545991715865 19 0.724376589426973 0.551246821146054 0.275623410573027 20 0.84102072986516 0.317958540269679 0.158979270134840 21 0.795986347164465 0.40802730567107 0.204013652835535 22 0.795810556273266 0.408378887453469 0.204189443726734 23 0.802898262019125 0.39420347596175 0.197101737980875 24 0.752027189574107 0.495945620851786 0.247972810425893 25 0.716324950671989 0.567350098656022 0.283675049328011 26 0.66590400844792 0.66819198310416 0.33409599155208 27 0.70512608557399 0.589747828852018 0.294873914426009 28 0.763037884640566 0.473924230718867 0.236962115359434 29 0.834283468388472 0.331433063223055 0.165716531611528 30 0.831833778942917 0.336332442114166 0.168166221057083 31 0.841556510354118 0.316886979291764 0.158443489645882 32 0.842417605265095 0.315164789469809 0.157582394734905 33 0.883227000064518 0.233545999870964 0.116772999935482 34 0.85096949715375 0.298061005692500 0.149030502846250 35 0.815690206348913 0.368619587302173 0.184309793651087 36 0.898876578657252 0.202246842685497 0.101123421342748 37 0.869323941866638 0.261352116266725 0.130676058133362 38 0.862930161700163 0.274139676599674 0.137069838299837 39 0.918875441820078 0.162249116359845 0.0811245581799224 40 0.924849589340555 0.150300821318890 0.0751504106594452 41 0.902573856748767 0.194852286502465 0.0974261432512327 42 0.882758596261451 0.234482807477097 0.117241403738549 43 0.891500815148851 0.216998369702297 0.108499184851149 44 0.94104645467289 0.117907090654221 0.0589535453271104 45 0.963543109147994 0.0729137817040118 0.0364568908520059 46 0.95114391072935 0.0977121785412993 0.0488560892706496 47 0.935675288562908 0.128649422874183 0.0643247114370916 48 0.957556663339735 0.0848866733205304 0.0424433366602652 49 0.946781675174596 0.106436649650808 0.0532183248254042 50 0.92974265062716 0.140514698745680 0.0702573493728398 51 0.911521481902786 0.176957036194427 0.0884785180972135 52 0.909249545230934 0.181500909538133 0.0907504547690663 53 0.89996367007898 0.200072659842041 0.100036329921021 54 0.876956370923149 0.246087258153702 0.123043629076851 55 0.869309521824626 0.261380956350748 0.130690478175374 56 0.909730381607987 0.180539236784026 0.0902696183920128 57 0.899809586277322 0.200380827445356 0.100190413722678 58 0.893529218126556 0.212941563746888 0.106470781873444 59 0.90711186936597 0.185776261268060 0.0928881306340301 60 0.890181701511862 0.219636596976276 0.109818298488138 61 0.88223082196769 0.235538356064621 0.117769178032310 62 0.860285441532984 0.279429116934032 0.139714558467016 63 0.8605059153217 0.278988169356600 0.139494084678300 64 0.875565879222939 0.248868241554123 0.124434120777061 65 0.87765259931032 0.244694801379358 0.122347400689679 66 0.849931222810305 0.300137554379389 0.150068777189695 67 0.893066823358264 0.213866353283471 0.106933176641736 68 0.875373487764267 0.249253024471465 0.124626512235733 69 0.850543228979088 0.298913542041824 0.149456771020912 70 0.816487915110108 0.367024169779784 0.183512084889892 71 0.777895863398467 0.444208273203067 0.222104136601533 72 0.748623824505962 0.502752350988076 0.251376175494038 73 0.706721129528038 0.586557740943924 0.293278870471962 74 0.684342093208051 0.631315813583897 0.315657906791949 75 0.638056845710568 0.723886308578864 0.361943154289432 76 0.604747015498005 0.790505969003991 0.395252984501995 77 0.579745792959848 0.840508414080305 0.420254207040152 78 0.55445176199903 0.89109647600194 0.44554823800097 79 0.5005686474575 0.998862705085 0.4994313525425 80 0.519204207501453 0.961591584997094 0.480795792498547 81 0.51277883367237 0.97444233265526 0.48722116632763 82 0.581593549169972 0.836812901660056 0.418406450830028 83 0.530432642327094 0.939134715345812 0.469567357672906 84 0.49938207585976 0.99876415171952 0.50061792414024 85 0.438285479032622 0.876570958065243 0.561714520967378 86 0.376698068630504 0.753396137261008 0.623301931369496 87 0.323479985642656 0.646959971285311 0.676520014357344 88 0.451166904534621 0.902333809069243 0.548833095465379 89 0.446942303243259 0.893884606486517 0.553057696756741 90 0.40772823421868 0.81545646843736 0.59227176578132 91 0.346565028028712 0.693130056057424 0.653434971971288 92 0.315208119253900 0.630416238507799 0.6847918807461 93 0.269604991003969 0.539209982007938 0.730395008996031 94 0.229674667550703 0.459349335101407 0.770325332449296 95 0.390483619391126 0.780967238782253 0.609516380608873 96 0.335550420690855 0.671100841381709 0.664449579309145 97 0.280960064811011 0.561920129622021 0.719039935188989 98 0.375202082570063 0.750404165140125 0.624797917429937 99 0.349005315684299 0.698010631368598 0.650994684315701 100 0.280405120513214 0.560810241026427 0.719594879486786 101 0.243500518133061 0.487001036266122 0.756499481866939 102 0.180883817612421 0.361767635224843 0.819116182387579 103 0.16338499638352 0.32676999276704 0.83661500361648 104 0.110128493544983 0.220256987089966 0.889871506455017 105 0.0705787146996912 0.141157429399382 0.929421285300309 106 0.0412857687276057 0.0825715374552114 0.958714231272394 107 0.0246468239679758 0.0492936479359517 0.975353176032024 108 0.0684225553364814 0.136845110672963 0.931577444663519 109 0.0415617413049507 0.0831234826099014 0.95843825869505 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 1 0.0101010101010101 OK 10% type I error level 6 0.0606060606060606 OK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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