W7

*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 18:22:31 +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/t1290536452exrwvl6aj8wad8o.htm/, Retrieved Tue, 23 Nov 2010 19:21:03 +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/t1290536452exrwvl6aj8wad8o.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 «

24 0 14 0 11 0 12 0 24 26 0 25 25 11 11 7 7 8 8 25 23 23 17 17 6 6 17 17 8 8 30 25 25 18 0 12 0 10 0 8 0 19 23 0 18 18 8 8 12 12 9 9 22 19 19 16 16 10 10 12 12 7 7 22 29 29 20 20 10 10 11 11 4 4 25 25 25 16 16 11 11 11 11 11 11 23 21 21 18 18 16 16 12 12 7 7 17 22 22 17 17 11 11 13 13 7 7 21 25 25 23 0 13 0 14 0 12 0 19 24 0 30 30 12 12 16 16 10 10 19 18 18 23 23 8 8 11 11 10 10 15 22 22 18 18 12 12 10 10 8 8 16 15 15 15 0 11 0 11 0 8 0 23 22 0 12 0 4 0 15 0 4 0 27 28 0 21 21 9 9 9 9 9 9 22 20 20 15 0 8 0 11 0 8 0 14 12 0 20 0 8 0 17 0 7 0 22 24 0 31 31 14 14 17 17 11 11 23 20 20 27 27 15 15 11 11 9 9 23 21 21 34 0 16 0 18 0 11 0 21 20 0 21 21 9 9 14 14 13 13 19 21 21 31 0 14 0 10 0 8 0 18 23 0 19 0 11 0 11 0 8 0 20 28 0 16 16 8 8 15 15 9 9 23 24 24 20 20 9 9 15 15 6 6 25 24 24 21 0 9 0 13 0 9 0 19 24 0 22 0 9 0 16 0 9 0 24 23 0 17 17 9 9 13 13 6 6 22 23 23 24 0 10 0 9 0 6 0 25 29 0 25 25 16 16 18 18 16 16 26 24 24 26 26 11 11 18 18 5 5 29 18 18 25 25 8 8 12 12 7 7 32 25 25 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation O[t] = + 7.32684596059117 + 0.356979436989242CM[t] -0.0592285666748474`CM*G`[t] -0.483865613410507D[t] + 0.175232812441502`D*G`[t] -0.0310127872102974PE[t] + 0.323284473674036`PE*G`[t] + 0.0927245043282856PC[t] -0.123099907387686`PC*G`[t] + 0.509759645731013PS[t] -0.132008906567604`PS*G`[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) 7.32684596059117 2.279284 3.2145 0.001606 0.000803 CM 0.356979436989242 0.086282 4.1374 5.9e-05 2.9e-05 `CM*G` -0.0592285666748474 0.115217 -0.5141 0.607981 0.303991 D -0.483865613410507 0.16529 -2.9274 0.003963 0.001981 `D*G` 0.175232812441502 0.216479 0.8095 0.419553 0.209777 PE -0.0310127872102974 0.161508 -0.192 0.847992 0.423996 `PE*G` 0.323284473674036 0.203179 1.5911 0.113728 0.056864 PC 0.0927245043282856 0.227294 0.4079 0.683904 0.341952 `PC*G` -0.123099907387686 0.276833 -0.4447 0.65721 0.328605 PS 0.509759645731013 0.103161 4.9414 2e-06 1e-06 `PS*G` -0.132008906567604 0.106177 -1.2433 0.21574 0.10787 Multiple Linear Regression - Regression Statistics Multiple R 0.623397994765618 R-squared 0.388625059877793 Adjusted R-squared 0.347034927896691 F-TEST (value) 9.34416510277905 F-TEST (DF numerator) 10 F-TEST (DF denominator) 147 p-value 6.79212242005178e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.41759903161865 Sum Squared Residuals 1716.95752171535 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 23.1455380422185 0.854461957781518 2 25 21.8668224893213 3.13317751067866 3 30 24.7061978746154 5.29380212538459 4 19 20.1022284798081 -1.10222847980808 5 22 20.6284448726333 1.37155512736673 6 22 23.2539357278194 -1.25393572781936 7 25 22.7327907751378 2.26720922486223 8 23 19.5095237148417 3.49047628515826 9 17 19.3533854884903 -2.35338548849026 10 21 22.0243225269749 -1.02432252697485 11 19 22.1598665655467 -3.15986656554673 12 19 23.7278847161621 -4.72788471616212 13 15 22.9278043521723 -7.92780435217232 14 16 17.3287427422355 -1.32874274223554 15 23 18.9743833493095 4.02561665069047 16 27 23.8541130404471 3.14588695955289 17 22 20.7140003623796 1.28599963762036 18 14 15.3283837322309 -1.32838373223092 19 22 22.9515954383592 -0.95159543835922 20 23 24.4257677462697 -1.42576774626966 21 23 21.6110028905429 1.38899710945714 22 21 22.3792292961033 -1.37922929610334 23 19 22.4316079216241 -3.43160792162414 24 18 23.7752299338472 -5.77522993384719 25 20 23.4608589716526 -3.46085897165258 26 23 22.7985118872127 0.201488112787262 27 25 23.7720087766795 1.22799122332049 28 19 23.1342094194357 -4.13420941943571 29 24 22.8883908490631 1.11160915093695 30 22 21.9164620536454 0.0835379463545565 31 25 26.1159579815043 -1.11595798150433 32 26 23.6733945502657 2.32660544973434 33 29 23.581934424098 5.41806557590197 34 32 25.0399562059333 6.96004379406672 35 25 22.2389211119954 2.76107888800462 36 29 24.5393208597622 4.46067914023782 37 28 25.2765897808799 2.72341021912010 38 17 15.5319101080834 1.46808989191663 39 28 26.3581819802532 1.64181801974678 40 29 22.4463344599839 6.55366554001613 41 26 26.56693529096 -0.566935290959991 42 25 23.6063618825030 1.39363811749705 43 14 17.8563378961915 -3.85633789619151 44 25 22.8236525160961 2.17634748390394 45 26 21.7839185806076 4.21608141939244 46 20 20.0197966730921 -0.0197966730920678 47 18 21.8150714731331 -3.81507147313315 48 32 24.8445122321104 7.15548776788959 49 25 24.8278172038666 0.172182796133373 50 25 22.5430131351555 2.45698686484448 51 23 21.3165270865493 1.68347291345067 52 21 22.00155939135 -1.00155939135 53 20 24.0368130668541 -4.03681306685412 54 15 16.4264490613949 -1.42644906139486 55 30 24.7567213971447 5.24327860285527 56 24 25.2751706249969 -1.27517062499689 57 26 24.2545640683195 1.74543593168049 58 24 20.8614516932321 3.13854830676792 59 22 22.6506184978980 -0.650618497897962 60 14 16.4664066566256 -2.46640665662557 61 24 22.2470641044201 1.75293589557994 62 24 22.3721702930404 1.62782970695965 63 24 23.7367153705886 0.2632846294114 64 24 20.2828725908774 3.71712740912255 65 19 17.90493791052 1.09506208948001 66 31 27.8340246944428 3.16597530555724 67 22 27.1571704728309 -5.15717047283094 68 27 20.7982471039359 6.2017528960641 69 19 17.2396182162998 1.76038178370016 70 25 22.3026563619312 2.69734363806884 71 20 24.7999565993862 -4.79995659938623 72 21 21.3125016453599 -0.312501645359875 73 27 27.4138927489721 -0.413892748972068 74 23 24.9471571086258 -1.94715710862584 75 25 25.9362647377293 -0.936264737729285 76 20 22.2858105036534 -2.28581050365345 77 22 22.7604633484450 -0.760463348444953 78 23 23.5177657911834 -0.517765791183384 79 25 24.1967052860024 0.80329471399764 80 25 23.5493046151028 1.45069538489718 81 17 23.6280693653108 -6.6280693653108 82 19 20.5703687834139 -1.57036878341388 83 25 24.2514116518747 0.748588348125348 84 19 22.7422850822930 -3.74228508229305 85 20 21.8879908223272 -1.88799082232724 86 26 22.6224155032293 3.37758449677065 87 23 21.1531296036157 1.84687039638430 88 27 24.5218843199494 2.47811568005063 89 17 20.2526697734074 -3.25266977340741 90 17 22.3760412457908 -5.3760412457908 91 19 19.9831294721654 -0.983129472165419 92 17 19.6249744934655 -2.62497449346553 93 22 22.5478747596857 -0.54787475968566 94 21 24.1042795613991 -3.10427956139915 95 32 29.0655147279217 2.93448527207831 96 21 23.7956913539753 -2.79569135397530 97 21 24.7785458291776 -3.77854582917763 98 18 20.7365353868179 -2.73653538681792 99 18 21.5248298414760 -3.52482984147597 100 23 23.1018747569259 -0.101874756925861 101 19 20.1836243613537 -1.18362436135368 102 20 21.7975879969896 -1.79758799698959 103 21 22.6081367464553 -1.60813674645531 104 20 24.1323885337656 -4.13238853376561 105 17 19.1791311281402 -2.17913112814022 106 18 20.2424034384410 -2.24240343844095 107 19 20.8572106203013 -1.85721062030133 108 22 22.4324698906192 -0.43246989061923 109 15 17.5105239414586 -2.51052394145862 110 14 19.2004413271801 -5.2004413271801 111 18 26.572667808271 -8.572667808271 112 24 21.8885366074199 2.11146339258007 113 35 23.9587116001398 11.0412883998602 114 29 18.8471180464216 10.1528819535784 115 21 22.3354701265229 -1.33547012652286 116 25 21.1464414844467 3.85355851555328 117 20 18.035639851859 1.964360148141 118 22 22.0807735259196 -0.0807735259195528 119 13 15.8935840669944 -2.89358406699437 120 26 22.8015768436531 3.19842315634688 121 17 17.4390415770972 -0.439041577097251 122 25 20.4914441148855 4.50855588511451 123 20 20.3391927870663 -0.339192787066306 124 19 17.5771033710676 1.42289662893241 125 21 21.0829634567250 -0.0829634567249715 126 22 21.1912795998767 0.808720400123346 127 24 22.6804071010946 1.31959289890544 128 21 23.2399778177666 -2.23997781776660 129 26 25.2475192731113 0.752480726888736 130 24 20.7473200921654 3.25267990783464 131 16 20.5101872124217 -4.51018721242169 132 23 22.4169385200482 0.583061479951772 133 18 20.8190080445639 -2.8190080445639 134 16 22.0788724431762 -6.0788724431762 135 26 21.8397598531341 4.16024014686593 136 19 19.5541578161299 -0.554157816129855 137 21 17.4196809202405 3.58031907975947 138 21 22.2211700720996 -1.22117007209955 139 22 18.6437797801546 3.35622021984542 140 23 19.5840897553837 3.41591024461629 141 29 24.946615986352 4.05338401364798 142 21 20.3365274401464 0.663472559853618 143 21 20.0461944449213 0.953805555078684 144 23 22.5123933224215 0.487606677578455 145 27 23.1222502568711 3.87774974312893 146 25 25.6519318180701 -0.651931818070076 147 21 21.0615436711115 -0.0615436711115204 148 10 17.4578058960550 -7.45780589605502 149 20 22.7513670629223 -2.75136706292233 150 26 22.3735599452575 3.62644005474245 151 24 24.2251070127566 -0.225107012756570 152 29 32.1262672519407 -3.12626725194067 153 19 19.5422299608837 -0.542229960883653 154 24 22.8924708289908 1.10752917100924 155 19 21.1609605753186 -2.16096057531862 156 24 23.1830384814002 0.81696151859982 157 22 22.2559762228679 -0.255976222867863 158 17 24.2411685019621 -7.24116850196213 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 14 0.973563485975858 0.0528730280482846 0.0264365140241423 15 0.945574431549052 0.108851136901897 0.0544255684509485 16 0.903533838411931 0.192932323176138 0.0964661615880688 17 0.847422592109532 0.305154815780937 0.152577407890468 18 0.773350155076146 0.453299689847708 0.226649844923854 19 0.793838553659489 0.412322892681022 0.206161446340511 20 0.760872614315812 0.478254771368376 0.239127385684188 21 0.742599677834574 0.514800644330852 0.257400322165426 22 0.707672116283652 0.584655767432696 0.292327883716348 23 0.664309633563626 0.671380732872747 0.335690366436374 24 0.655544505977978 0.688910988044044 0.344455494022022 25 0.644259769053714 0.711480461892571 0.355740230946286 26 0.568202658847986 0.863594682304028 0.431797341152014 27 0.494576364001745 0.98915272800349 0.505423635998255 28 0.441639286460324 0.883278572920647 0.558360713539676 29 0.410066048658141 0.820132097316283 0.589933951341859 30 0.343647598634996 0.687295197269991 0.656352401365004 31 0.315321751715943 0.630643503431886 0.684678248284057 32 0.310633333425846 0.621266666851692 0.689366666574154 33 0.40056325298966 0.80112650597932 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69 0.45154536487852 0.90309072975704 0.54845463512148 70 0.436022161394265 0.87204432278853 0.563977838605735 71 0.510501642219656 0.978996715560688 0.489498357780344 72 0.462360329078122 0.924720658156244 0.537639670921878 73 0.4175062465648 0.8350124931296 0.5824937534352 74 0.384046985004067 0.768093970008133 0.615953014995933 75 0.349629313926797 0.699258627853595 0.650370686073203 76 0.324814181075246 0.649628362150493 0.675185818924754 77 0.283690820396231 0.567381640792462 0.716309179603769 78 0.249370704845817 0.498741409691633 0.750629295154183 79 0.214519905851015 0.429039811702031 0.785480094148985 80 0.189274440119874 0.378548880239748 0.810725559880126 81 0.300038704743211 0.600077409486423 0.699961295256789 82 0.271083570647963 0.542167141295925 0.728916429352037 83 0.234284830549141 0.468569661098283 0.765715169450859 84 0.232605380948609 0.465210761897218 0.767394619051391 85 0.208125856627594 0.416251713255189 0.791874143372406 86 0.207625900657262 0.415251801314523 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0.038930092934507 0.077860185869014 0.961069907065493 105 0.0319443154390978 0.0638886308781955 0.968055684560902 106 0.0276040168058954 0.0552080336117908 0.972395983194105 107 0.0235469039257523 0.0470938078515045 0.976453096074248 108 0.0173587129539882 0.0347174259079764 0.982641287046012 109 0.0159083280548811 0.0318166561097621 0.984091671945119 110 0.0219082784332125 0.0438165568664251 0.978091721566787 111 0.115708228944400 0.231416457888801 0.8842917710556 112 0.106259112699560 0.212518225399121 0.89374088730044 113 0.457094732753297 0.914189465506593 0.542905267246703 114 0.785650296352116 0.428699407295767 0.214349703647884 115 0.744341869519473 0.511316260961054 0.255658130480527 116 0.821956543487791 0.356086913024417 0.178043456512209 117 0.79992250278695 0.4001549944261 0.20007749721305 118 0.755167335094304 0.489665329811391 0.244832664905696 119 0.76423389306938 0.471532213861242 0.235766106930621 120 0.73572130083281 0.528557398334381 0.264278699167190 121 0.68194337931536 0.636113241369281 0.318056620684641 122 0.702685906931684 0.594628186136633 0.297314093068316 123 0.647613683969824 0.704772632060352 0.352386316030176 124 0.588376998460726 0.823246003078548 0.411623001539274 125 0.54754566981672 0.90490866036656 0.45245433018328 126 0.500740088478052 0.998519823043896 0.499259911521948 127 0.444779663144027 0.889559326288054 0.555220336855973 128 0.384399277539522 0.768798555079045 0.615600722460478 129 0.328686665659967 0.657373331319934 0.671313334340033 130 0.309651836190470 0.619303672380941 0.69034816380953 131 0.313115128060602 0.626230256121203 0.686884871939399 132 0.382163147058339 0.764326294116678 0.617836852941661 133 0.35228337397177 0.70456674794354 0.64771662602823 134 0.302238893426928 0.604477786853856 0.697761106573072 135 0.243829218590009 0.487658437180017 0.756170781409991 136 0.186225145535720 0.372450291071439 0.81377485446428 137 0.179780522002677 0.359561044005353 0.820219477997324 138 0.126146196590667 0.252292393181334 0.873853803409333 139 0.0969933788038216 0.193986757607643 0.903006621196178 140 0.072914452509362 0.145828905018724 0.927085547490638 141 0.0844151126656436 0.168830225331287 0.915584887334356 142 0.143436921527209 0.286873843054419 0.85656307847279 143 0.0822635621176883 0.164527124235377 0.917736437882312 144 0.0444199958049524 0.0888399916099047 0.955580004195048 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 4 0.0305343511450382 OK 10% type I error level 11 0.083969465648855 OK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 9 ; 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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