*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: Thu, 19 Nov 2009 01:09:26 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj.htm/, Retrieved Thu, 19 Nov 2009 09:10:19 +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/2009/Nov/19/t125861820789xeyj6m9n6jhqj.htm/}, year = {2009}, } @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 = {2009}, 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 «

100 0 108.1560276 0 114.0150276 0 102.1880309 0 110.3672031 0 96.8602511 0 94.1944583 0 99.51621961 0 94.06333487 0 97.5541476 0 78.15062422 0 81.2434643 0 92.36262465 0 96.06324371 0 114.0523777 0 110.6616666 0 104.9171949 0 90.00187193 0 95.7008067 0 86.02741157 0 84.85287668 0 100.04328 0 80.91713823 0 74.06539709 0 77.30281369 0 97.23043249 0 90.75515676 0 100.5614455 0 92.01293267 0 99.24012138 0 105.8672755 0 90.9920463 0 93.30624423 0 91.17419413 0 77.33295039 0 91.1277721 0 85.01249943 0 83.90390242 0 104.8626302 0 110.9039108 0 95.43714373 0 111.6238727 0 108.8925403 0 96.17511682 0 101.9740205 0 99.11953031 0 86.78158147 0 118.4195003 0 118.7441447 0 106.5296192 0 134.7772694 0 104.6778714 0 105.2954304 0 139.4139849 0 103.6060491 0 99.78182974 0 103.4610301 0 120.0594945 0 96.71377168 0 107.1308929 0 105.3608372 0 111.6942359 0 132.0519998 0 126.8037879 0 154.4824253 0 141.5570984 0 109.9506882 0 127.904198 0 133.0888617 0 120.079 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 85.3644295390171 -1.76326950435899X[t] + 5.27565729087253M1[t] + 4.40171315059118M2[t] + 16.4648034314210M3[t] + 9.49537782336185M4[t] + 9.24836954419162M5[t] + 15.0024448772436M6[t] + 2.10976788585115M7[t] -0.762959099985754M8[t] + 1.02460681417736M9[t] + 9.56288284167381M10[t] -9.89589302082977M11[t] + 0.366035712503561t + 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) 85.3644295390171 5.818583 14.671 0 0 X -1.76326950435899 5.081565 -0.347 0.729372 0.364686 M1 5.27565729087253 6.744029 0.7823 0.436022 0.218011 M2 4.40171315059118 6.734716 0.6536 0.514974 0.257487 M3 16.4648034314210 6.726279 2.4478 0.016228 0.008114 M4 9.49537782336185 6.718721 1.4133 0.160879 0.080439 M5 9.24836954419162 6.712045 1.3779 0.171513 0.085756 M6 15.0024448772436 6.706254 2.2371 0.027644 0.013822 M7 2.10976788585115 6.70135 0.3148 0.753591 0.376795 M8 -0.762959099985754 6.697335 -0.1139 0.909544 0.454772 M9 1.02460681417736 6.694211 0.1531 0.87868 0.43934 M10 9.56288284167381 6.691978 1.429 0.156317 0.078159 M11 -9.89589302082977 6.690638 -1.4791 0.142466 0.071233 t 0.366035712503561 0.077314 4.7344 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.685296660999714 R-squared 0.469631513577357 Adjusted R-squared 0.396282680348693 F-TEST (value) 6.40271280271481 F-TEST (DF numerator) 13 F-TEST (DF denominator) 94 p-value 1.59991639936408e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.1920393586397 Sum Squared Residuals 18932.9142287749 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 91.0061225423931 8.99387745760688 2 108.1560276 90.4982141146154 17.6578134853846 3 114.0150276 102.927340107949 11.0876874920513 4 102.1880309 96.3239502123931 5.86408068760685 5 110.3672031 96.4429776457265 13.9242254542735 6 96.8602511 102.563088691282 -5.70283759128207 7 94.1944583 90.0364474123932 4.15801088760679 8 99.51621961 87.5297561390598 11.9864634709402 9 94.06333487 89.6833577657265 4.37997710427351 10 97.5541476 98.5876695057265 -1.03352190572648 11 78.15062422 79.4949293557265 -1.34430513572649 12 81.2434643 89.7568580890599 -8.51339378905987 13 92.36262465 95.398551092436 -3.03592644243589 14 96.06324371 94.8906426646581 1.17260104534188 15 114.0523777 107.319768657991 6.73260904200854 16 110.6616666 100.716378762436 9.9452878375641 17 104.9171949 100.835406195769 4.08178870423077 18 90.00187193 106.955517241325 -16.9536453113248 19 95.7008067 94.428875962436 1.27193073756411 20 86.02741157 91.9221846891026 -5.89477311910256 21 84.85287668 94.0757863157692 -9.22290963576923 22 100.04328 102.980098055769 -2.93681805576923 23 80.91713823 83.8873579057692 -2.97021967576922 24 74.06539709 94.1492866391026 -20.0838895491026 25 77.30281369 99.7909796424786 -22.4881659524786 26 97.23043249 99.2830712147009 -2.05263872470086 27 90.75515676 111.712197208034 -20.9570404480342 28 100.5614455 105.108807312479 -4.54736181247863 29 92.01293267 105.227834745812 -13.2149020758120 30 99.24012138 111.347945791368 -12.1078244113675 31 105.8672755 98.8213045124786 7.04597098752138 32 90.9920463 96.3146132391453 -5.3225669391453 33 93.30624423 98.468214865812 -5.16197063581196 34 91.17419413 107.372526605812 -16.1983324758120 35 77.33295039 88.279786455812 -10.9468360658120 36 91.1277721 98.5417151891453 -7.4139430891453 37 85.01249943 104.183408192521 -19.1709087625214 38 83.90390242 103.675499764744 -19.7715973447436 39 104.8626302 116.104625758077 -11.2419955580769 40 110.9039108 109.501235862521 1.40267493747864 41 95.43714373 109.620263295855 -14.1831195658547 42 111.6238727 115.740374341410 -4.11650164141025 43 108.8925403 103.213733062521 5.67880723747863 44 96.17511682 100.707041789188 -4.53192496918803 45 101.9740205 102.860643415855 -0.886622915854707 46 99.11953031 111.764955155855 -12.6454248458547 47 86.78158147 92.6722150058547 -5.89063353585469 48 118.4195003 102.934143739188 15.4853565608120 49 118.7441447 108.575836742564 10.1683079574359 50 106.5296192 108.067928314786 -1.53830911478633 51 134.7772694 120.497054308120 14.2802150918803 52 104.6778714 113.893664412564 -9.2157930125641 53 105.2954304 114.012691845897 -8.71726144589743 54 139.4139849 120.132802891453 19.281182008547 55 103.6060491 107.606161612564 -4.00011251256409 56 99.78182974 105.099470339231 -5.31764059923076 57 103.4610301 107.253071965897 -3.79204186589743 58 120.0594945 116.157383705897 3.90211079410256 59 96.71377168 97.0646435558974 -0.350871875897444 60 107.1308929 107.326572289231 -0.195679389230766 61 105.3608372 112.968265292607 -7.60742809260684 62 111.6942359 112.460356864829 -0.766120964829072 63 132.0519998 124.889482858162 7.16251694183761 64 126.8037879 118.286092962607 8.51769493739316 65 154.4824253 118.405120395940 36.0773049040598 66 141.5570984 124.525231441496 17.0318669585043 67 109.9506882 111.998590162607 -2.04790196260683 68 127.904198 109.491898889274 18.4122991107265 69 133.0888617 111.645500515940 21.4433611840598 70 120.0796299 120.549812255940 -0.470182355940181 71 117.5557142 101.457072105940 16.0986420940598 72 143.0362309 111.719000839274 31.3172300607265 73 159.982927 115.597424338291 44.3855026617094 74 128.5991124 115.089515910513 13.5095964894872 75 149.7373327 127.518641903846 22.2186907961538 76 126.8169313 120.915252008291 5.9016792917094 77 140.9639674 121.034279441624 19.9296879583761 78 137.6691981 127.154390487179 10.5148076128205 79 117.9402337 114.627749208291 3.31248449170940 80 122.3095247 112.121057934957 10.1884667650427 81 127.7804207 114.274659561624 13.5057611383761 82 136.1677176 123.178971301624 12.9887462983761 83 116.2405856 104.086231151624 12.1543544483761 84 123.1576893 114.348159884957 8.80952941504274 85 116.3400234 119.989852888333 -3.64982948833333 86 108.6119282 119.481944460556 -10.8700162605556 87 125.8982264 131.911070453889 -6.01284405388889 88 112.8003105 125.307680558333 -12.5073700583333 89 107.5182447 125.426707991667 -17.9084632916667 90 135.0955413 131.546819037222 3.54872226277779 91 115.5096488 119.020177758333 -3.51052895833333 92 115.8640759 116.513486485 -0.649410584999993 93 104.5883906 118.667088111667 -14.0786975116667 94 163.7213386 127.571399851667 36.1499387483333 95 113.4482275 108.478659701667 4.96956779833334 96 98.0428844 118.740588435 -20.697704035 97 116.7868521 124.382281438376 -7.59542933837607 98 126.5330444 123.874373010598 2.65867138940171 99 113.0336597 136.303499003932 -23.2698393039316 100 124.3392163 129.700109108376 -5.36089280837607 101 109.8298759 129.819136541709 -19.9892606417094 102 124.4434777 135.939247587265 -11.4957698872650 103 111.5039454 123.412606308376 -11.9086609083761 104 102.0350019 120.905915035043 -18.8709131350427 105 116.8726598 123.059516661709 -6.1868568617094 106 112.2073122 131.963828401709 -19.7565162017094 107 101.1513902 112.871088251709 -11.7196980517094 108 124.4255108 123.133016985043 1.29249381495726 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0969837326446764 0.193967465289353 0.903016267355324 18 0.0366702572823415 0.073340514564683 0.963329742717658 19 0.0145325802642958 0.0290651605285917 0.985467419735704 20 0.0096085695622418 0.0192171391244836 0.990391430437758 21 0.00371235418948817 0.00742470837897633 0.996287645810512 22 0.00178293121667593 0.00356586243335186 0.998217068783324 23 0.00081747096488756 0.00163494192977512 0.999182529035112 24 0.000309403244306179 0.000618806488612357 0.999690596755694 25 0.000479715526223488 0.000959431052446977 0.999520284473776 26 0.000181006569468291 0.000362013138936581 0.999818993430532 27 0.000422123914812614 0.000844247829625228 0.999577876085187 28 0.000177869237214638 0.000355738474429276 0.999822130762785 29 8.91331760153943e-05 0.000178266352030789 0.999910866823985 30 0.000204106635675157 0.000408213271350313 0.999795893364325 31 0.000609331691667317 0.00121866338333463 0.999390668308333 32 0.000306031471105927 0.000612062942211854 0.999693968528894 33 0.000222716096548830 0.000445432193097660 0.999777283903451 34 0.000120154690938949 0.000240309381877897 0.99987984530906 35 6.21472805330555e-05 0.000124294561066111 0.999937852719467 36 0.000169171507775609 0.000338343015551217 0.999830828492224 37 0.000125310707444065 0.00025062141488813 0.999874689292556 38 0.000144849464127118 0.000289698928254236 0.999855150535873 39 9.95345156116222e-05 0.000199069031223244 0.999900465484388 40 9.77874073622333e-05 0.000195574814724467 0.999902212592638 41 6.7770858998776e-05 0.000135541717997552 0.999932229141001 42 0.00030250727518946 0.00060501455037892 0.99969749272481 43 0.000313805779567022 0.000627611559134044 0.999686194220433 44 0.000225028032105345 0.000450056064210691 0.999774971967895 45 0.000254966590590436 0.000509933181180872 0.99974503340941 46 0.000312185377088081 0.000624370754176162 0.999687814622912 47 0.000369981162879416 0.000739962325758832 0.99963001883712 48 0.00650842603567148 0.0130168520713430 0.993491573964329 49 0.0171566001491189 0.0343132002982377 0.982843399850881 50 0.0135316569558187 0.0270633139116373 0.986468343044181 51 0.0214402928132259 0.0428805856264519 0.978559707186774 52 0.0195662811266752 0.0391325622533505 0.980433718873325 53 0.0203505884835867 0.0407011769671735 0.979649411516413 54 0.0537373865031828 0.107474773006366 0.946262613496817 55 0.0440377287911653 0.0880754575823307 0.955962271208835 56 0.0425649504129392 0.0851299008258784 0.95743504958706 57 0.0439370555346221 0.0878741110692441 0.956062944465378 58 0.0538423283270369 0.107684656654074 0.946157671672963 59 0.0636132935277611 0.127226587055522 0.936386706472239 60 0.0813297741483767 0.162659548296753 0.918670225851623 61 0.125383207150756 0.250766414301512 0.874616792849244 62 0.122843711548185 0.245687423096371 0.877156288451814 63 0.105193076084196 0.210386152168392 0.894806923915804 64 0.0873686908377483 0.174737381675497 0.912631309162252 65 0.292376266892768 0.584752533785536 0.707623733107232 66 0.286122913422245 0.572245826844491 0.713877086577755 67 0.263971966921069 0.527943933842139 0.73602803307893 68 0.252813856608212 0.505627713216424 0.747186143391788 69 0.262254540245287 0.524509080490574 0.737745459754713 70 0.351196010458396 0.702392020916792 0.648803989541604 71 0.363814262518266 0.727628525036532 0.636185737481734 72 0.39909873389439 0.79819746778878 0.60090126610561 73 0.542771592027513 0.914456815944974 0.457228407972487 74 0.530757534696219 0.938484930607562 0.469242465303781 75 0.561898650492966 0.876202699014069 0.438101349507034 76 0.526056486144465 0.94788702771107 0.473943513855535 77 0.598066775152311 0.803866449695377 0.401933224847689 78 0.52642679543818 0.94714640912364 0.47357320456182 79 0.469952687413514 0.939905374827027 0.530047312586486 80 0.403010398588755 0.80602079717751 0.596989601411245 81 0.354342703155057 0.708685406310114 0.645657296844943 82 0.290172139345715 0.58034427869143 0.709827860654285 83 0.220480383266680 0.440960766533360 0.77951961673332 84 0.169074760326590 0.338149520653181 0.83092523967341 85 0.130004908880454 0.260009817760908 0.869995091119546 86 0.152128567261591 0.304257134523181 0.84787143273841 87 0.117323762914511 0.234647525829021 0.88267623708549 88 0.109374261566929 0.218748523133859 0.89062573843307 89 0.0874817884447473 0.174963576889495 0.912518211555253 90 0.0473159219787511 0.0946318439575023 0.952684078021249 91 0.0226813798636342 0.0453627597272684 0.977318620136366 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.36 NOK 5% type I error level 36 0.48 NOK 10% type I error level 41 0.546666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/10darg1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/10darg1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/1ihhg1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/1ihhg1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/2kt9v1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/2kt9v1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/3adhp1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/3adhp1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/46c2e1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/46c2e1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/5kprj1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/5kprj1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/6a6dk1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/6a6dk1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/7hiit1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/7hiit1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/8x8hh1258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/8x8hh1258618159.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/9wm921258618159.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125861820789xeyj6m9n6jhqj/9wm921258618159.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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