WS 10 Multiple Regression

*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: Fri, 10 Dec 2010 20:24:04 +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/10/t1292012655lidqckjdtqoya1z.htm/, Retrieved Fri, 10 Dec 2010 21:24:25 +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/10/t1292012655lidqckjdtqoya1z.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 «

1 41 25 15 9 3 1 38 25 15 9 4 1 37 19 14 9 4 1 42 18 10 8 4 1 40 23 18 15 3 1 43 25 14 9 4 1 40 23 11 11 4 1 45 30 17 6 5 1 45 32 21 10 4 1 44 25 7 11 4 1 42 26 18 16 4 1 32 25 13 11 5 1 32 25 13 11 5 1 41 35 18 7 4 1 38 20 12 10 4 1 38 21 9 9 4 1 24 23 11 15 3 1 46 17 11 6 5 1 42 27 16 12 4 1 46 25 12 10 4 1 43 18 14 14 5 1 38 22 13 9 4 1 39 23 17 14 4 1 40 25 13 14 3 1 37 19 13 9 2 1 41 20 12 8 4 1 46 26 12 10 4 1 26 16 12 9 3 1 37 22 9 9 3 1 39 25 17 9 4 1 44 29 18 11 5 1 38 22 12 10 2 1 38 32 12 8 0 1 38 23 9 14 4 1 33 18 13 10 3 1 43 26 11 14 4 1 41 14 13 15 2 1 49 20 6 8 4 1 45 25 11 10 5 1 31 21 18 13 3 1 30 21 18 13 3 1 38 23 15 10 4 1 39 24 11 11 4 1 40 21 14 10 4 1 36 17 12 16 2 1 49 29 8 6 5 1 41 25 11 11 4 1 18 16 10 12 2 1 42 25 17 14 3 1 41 25 16 9 5 1 43 21 13 11 4 1 46 23 15 8 3 1 41 25 16 8 5 1 39 25 7 11 4 1 42 24 16 16 4 1 35 21 13 12 5 1 36 22 15 14 3 1 48 14 12 8 4 1 41 20 12 10 4 1 47 21 24 14 3 1 41 22 15 10 3 1 31 19 8 5 5 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation StudyForCareer[t] = + 32.5974764805152 -0.213406913845935Gender[t] + 0.174696937710941PersonalStandards[t] + 0.0533645733833355ParentalExpectation[t] -0.222271249914753Doubts[t] + 1.40057205675814LeaderPreference[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) 32.5974764805152 3.276628 9.9485 0 0 Gender -0.213406913845935 0.875608 -0.2437 0.807801 0.403901 PersonalStandards 0.174696937710941 0.104199 1.6766 0.095858 0.047929 ParentalExpectation 0.0533645733833355 0.131271 0.4065 0.68498 0.34249 Doubts -0.222271249914753 0.156784 -1.4177 0.158504 0.079252 LeaderPreference 1.40057205675814 0.48395 2.894 0.004413 0.002206 Multiple Linear Regression - Regression Statistics Multiple R 0.368414053928045 R-squared 0.135728915131696 Adjusted R-squared 0.104862090672114 F-TEST (value) 4.39724258999894 F-TEST (DF numerator) 5 F-TEST (DF denominator) 140 p-value 0.000948505454698712 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.99882997972892 Sum Squared Residuals 3498.36216327312 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 39.7532365312343 1.24676346876571 2 38 41.1538085879926 -3.15380858799256 3 37 40.0522623883436 -3.05226238834359 4 42 39.886378407014 2.11362159298596 5 40 38.230308876474 1.76969112352598 6 43 41.1004440146092 1.89955598539078 7 40 40.1464139192078 -0.146413919207826 8 45 44.2014082298163 0.798591770183674 9 45 42.4746033423544 2.5253966576456 10 44 40.2823495010964 3.71765049890363 11 42 39.9327004964502 2.06729950354977 12 32 42.0031089981545 -10.0031089981545 13 32 42.0031089981545 -10.0031089981545 14 41 43.5054141850815 -2.50541418508148 15 38 39.8979589293731 -1.89795892937309 16 38 40.1348333968488 -2.13483339684878 17 24 37.8567568627907 -13.8567568627907 18 46 41.6101605992741 4.38983940072592 19 42 40.8897532870535 1.11024671294649 20 46 40.7714436179278 5.2285563820722 21 43 40.166781257817 2.83321874218299 22 38 40.5229886280931 -2.52298862809306 23 39 39.7997876097636 -0.79978760976358 24 40 38.535151134894 1.46484886510602 25 37 37.197753701444 -0.197753701443968 26 41 40.3425014292026 0.657498570797402 27 46 40.9461405556387 5.05385944436126 28 26 38.0208703716859 -12.0208703716859 29 37 38.9089582778016 -1.90895827780158 30 39 41.2605377347592 -2.26053773475923 31 44 42.9687196159150 1.03128038408504 32 38 37.4462086912787 0.553791308721298 33 38 36.8365764547013 1.16342354529866 34 38 39.3728710226969 -1.37287102269690 35 33 38.2013575705764 -5.20135757057641 36 43 40.0036909825964 2.99630901740361 37 41 34.9906415134007 6.00935848659926 38 49 40.0223139889026 8.97768601109741 39 45 42.1186511013026 2.8813488986974 40 31 38.3254575008817 -7.32545750088165 41 30 38.3254575008817 -8.32545750088165 42 38 40.5821434626559 -2.58214346265592 43 39 40.3211108569188 -1.32111085691877 44 40 40.1793850138507 -0.179385013850704 45 36 35.2390965032355 0.760903496764521 46 49 43.5464301316554 5.45356986834463 47 41 40.4958077946297 0.504192205370292 48 18 35.8467554184169 -17.8467554184169 49 42 38.7486094284273 3.25139057157267 50 41 42.607745218134 -1.60774521813403 51 43 39.9037491905526 3.09625080944738 52 46 39.6261139057273 6.37388609427271 53 41 42.8300164680488 -1.83001646804878 54 39 40.2823495010964 -1.28234950109637 55 42 39.4765774742617 2.52342252573832 56 35 41.082049997396 -6.082049997396 57 36 38.1177894685278 -2.11778946852783 58 48 39.294319802937 8.70568019706305 59 41 39.8979589293731 1.10204107062691 60 47 38.4233736912669 8.57662630873309 61 41 39.0068744681868 1.99312553181315 62 31 42.0217320044607 -11.0217320044607 63 36 41.1711793715311 -5.17117937153113 64 46 41.2605377347592 4.73946226524077 65 44 40.6772920870635 3.32270791293645 66 43 36.9327013069616 6.06729869303845 67 40 40.457020298676 -0.457020298676028 68 40 39.0486585192111 0.951341480788867 69 46 37.0308464864398 8.96915351356022 70 39 40.5851661578578 -1.58516615785781 71 44 41.5595897319997 2.44041026800034 72 38 36.3504527528092 1.64954724719076 73 39 39.5933718001774 -0.593371800177387 74 41 42.9037745201726 -1.90377452017257 75 39 38.5885157082773 0.411484291722681 76 40 39.492177785366 0.507822214634013 77 44 40.2065658473141 3.79343415268594 78 42 39.4156521672646 2.58434783273544 79 46 42.5863546458502 3.41364535414980 80 44 39.9299329304726 4.0700670695274 81 37 40.4278400036854 -3.42784000368544 82 39 37.1083953382159 1.89160466178413 83 40 38.1799669982926 1.82003300170742 84 42 38.7486094284273 3.25139057157267 85 37 38.8409904868573 -1.84099048685732 86 33 37.7694188267806 -4.7694188267806 87 35 39.8330138336307 -4.83301383363071 88 42 35.5721166886622 6.42788331133784 89 36 35.9650137078552 0.0349862921448101 90 44 39.5862779365428 4.41372206345717 91 45 39.8677040210204 5.13229597897956 92 47 41.1585503759413 5.8414496240587 93 40 41.2458831375109 -1.24588313751089 94 49 38.8049191771939 10.1950808228061 95 48 44.3438246575161 3.65617534248390 96 29 40.8247568116237 -11.8247568116237 97 45 41.7603928898119 3.2396071101881 98 29 36.5754401890142 -7.57544018901423 99 41 40.5083340308447 0.49166596915528 100 34 37.1287374372689 -3.12873743726892 101 38 36.1271330296637 1.87286697033629 102 37 37.9848252021538 -0.984825202153773 103 48 44.2285376835923 3.77146231640773 104 39 41.052818322718 -2.052818322718 105 34 40.5906499101257 -6.59064991012567 106 35 36.611433978859 -1.61143397885899 107 41 40.2093081737356 0.790691826264434 108 43 39.4543621433995 3.54563785660045 109 41 38.5836711609538 2.41632883904624 110 39 36.1067395509233 2.89326044907675 111 36 41.1721250853871 -5.17212508538709 112 32 41.6506671879746 -9.65066718797462 113 46 40.0841850097557 5.91581499024433 114 42 41.0324248439775 0.967575156022457 115 42 36.3619818954809 5.63801810451912 116 45 39.4601785447104 5.53982145528964 117 39 40.8635181674461 -1.86351816744613 118 45 41.1265763748418 3.87342362515821 119 48 42.4951744305004 5.50482556949957 120 28 38.3487961550053 -10.3487961550053 121 35 37.6538253439451 -2.65382534394511 122 38 39.0591644282081 -1.05916442820812 123 42 38.4527577357943 3.54724226420565 124 36 38.2128867132480 -2.21288671324805 125 37 40.8519376450871 -3.85193764508708 126 38 39.2029105984942 -1.20291059849421 127 43 40.9196503068071 2.08034969319290 128 35 34.6993539993109 0.300646000689057 129 36 39.8357300199209 -3.83573001992094 130 33 36.4221338235871 -3.42213382358712 131 39 38.2536475305977 0.746352469402314 132 32 41.239095782788 -9.23909578278799 133 45 39.3300332240013 5.66996677599866 134 35 39.8833043321248 -4.88330433212475 135 38 37.8043380032633 0.195661996736691 136 36 37.4335535555576 -1.43355355555760 137 42 38.1867029733281 3.81329702667193 138 41 39.5454909790619 1.45450902093808 139 47 38.9300423411738 8.06995765882624 140 35 38.7019808300795 -3.70198083007948 141 43 37.7683442134185 5.23165578658146 142 40 40.189917062979 -0.189917062978970 143 46 40.8043633328833 5.19563666711673 144 44 41.7603928898119 2.23960711018810 145 35 38.8117326720480 -3.81173267204804 146 29 39.7317684391319 -10.7317684391319 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.228263643747141 0.456527287494281 0.77173635625286 10 0.111585781609344 0.223171563218688 0.888414218390656 11 0.0503817217129347 0.100763443425869 0.949618278287065 12 0.284438673930541 0.568877347861083 0.715561326069459 13 0.320479425346107 0.640958850692214 0.679520574653893 14 0.339739764479001 0.679479528958003 0.660260235520999 15 0.248447815833058 0.496895631666117 0.751552184166942 16 0.180976592142866 0.361953184285731 0.819023407857134 17 0.741392185289308 0.517215629421383 0.258607814710692 18 0.723091115077321 0.553817769845357 0.276908884922679 19 0.678871532498777 0.642256935002447 0.321128467501223 20 0.724911151228256 0.550177697543487 0.275088848771744 21 0.721798485774899 0.556403028450203 0.278201514225101 22 0.673124618524043 0.653750762951913 0.326875381475957 23 0.603822234909232 0.792355530181537 0.396177765090768 24 0.564236496323409 0.871527007353182 0.435763503676591 25 0.505185560831123 0.989628878337755 0.494814439168877 26 0.435205354408425 0.87041070881685 0.564794645591575 27 0.470806371565563 0.941612743131125 0.529193628434437 28 0.732774024592937 0.534451950814127 0.267225975407063 29 0.678987246232436 0.642025507535129 0.321012753767564 30 0.630832646815615 0.73833470636877 0.369167353184385 31 0.570712020875561 0.858575958248878 0.429287979124439 32 0.518250100866923 0.963499798266153 0.481749899133077 33 0.459284526936258 0.918569053872517 0.540715473063742 34 0.401884603107711 0.803769206215422 0.598115396892289 35 0.381023460990496 0.762046921980992 0.618976539009504 36 0.355204548828101 0.710409097656202 0.644795451171899 37 0.428086245729008 0.856172491458016 0.571913754270992 38 0.565383527955224 0.869232944089553 0.434616472044776 39 0.525180708419469 0.949638583161062 0.474819291580531 40 0.551625471816883 0.896749056366234 0.448374528183117 41 0.596675533311239 0.806648933377522 0.403324466688761 42 0.551526979271896 0.896946041456208 0.448473020728104 43 0.501819570083944 0.996360859832113 0.498180429916056 44 0.449482482908326 0.898964965816651 0.550517517091675 45 0.407320684061989 0.814641368123977 0.592679315938011 46 0.389131866712786 0.778263733425571 0.610868133287214 47 0.338600521919588 0.677201043839176 0.661399478080412 48 0.830397708851963 0.339204582296074 0.169602291148037 49 0.82134471162286 0.357310576754279 0.178655288377140 50 0.78926655329413 0.421466893411742 0.210733446705871 51 0.770855171287047 0.458289657425905 0.229144828712953 52 0.804637124226481 0.390725751547037 0.195362875773519 53 0.772987539579148 0.454024920841704 0.227012460420852 54 0.740299303280779 0.519401393438442 0.259700696719221 55 0.713526556219301 0.572946887561398 0.286473443780699 56 0.73391523513731 0.53216952972538 0.26608476486269 57 0.702930373362184 0.594139253275631 0.297069626637816 58 0.799805396145053 0.400389207709895 0.200194603854947 59 0.765267474748036 0.469465050503927 0.234732525251964 60 0.837405205273008 0.325189589453984 0.162594794726992 61 0.809836669130789 0.380326661738422 0.190163330869211 62 0.91323431461073 0.17353137077854 0.08676568538927 63 0.91852173416005 0.162956531679902 0.0814782658399508 64 0.912649441500226 0.174701116999547 0.0873505584997737 65 0.898897617889191 0.202204764221618 0.101102382110809 66 0.907124624955422 0.185750750089156 0.092875375044578 67 0.888513900626627 0.222972198746745 0.111486099373373 68 0.864393581131037 0.271212837737926 0.135606418868963 69 0.910333438720209 0.179333122559582 0.089666561279791 70 0.892509037642712 0.214981924714577 0.107490962357288 71 0.872682974172032 0.254634051655936 0.127317025827968 72 0.84825682082848 0.303486358343039 0.151743179171520 73 0.822190871122518 0.355618257754965 0.177809128877482 74 0.796590555826813 0.406818888346373 0.203409444173187 75 0.760066136056082 0.479867727887836 0.239933863943918 76 0.721518213656286 0.556963572687428 0.278481786343714 77 0.69753429651982 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0.517467919486094 0.965064161027812 0.482532080513906 96 0.794667455267338 0.410665089465325 0.205332544732662 97 0.768380418256935 0.463239163486129 0.231619581743065 98 0.810183321250158 0.379633357499685 0.189816678749842 99 0.771483236899313 0.457033526201375 0.228516763100687 100 0.753344968387844 0.493310063224312 0.246655031612156 101 0.710392294903492 0.579215410193017 0.289607705096509 102 0.665980393361268 0.668039213277464 0.334019606638732 103 0.649825056926951 0.700349886146098 0.350174943073049 104 0.607480666139012 0.785038667721977 0.392519333860988 105 0.59823718744903 0.80352562510194 0.40176281255097 106 0.557329143513771 0.885341712972458 0.442670856486229 107 0.502908106270219 0.994183787459562 0.497091893729781 108 0.462854845104759 0.925709690209518 0.537145154895241 109 0.413651881143673 0.827303762287345 0.586348118856327 110 0.364743887789218 0.729487775578435 0.635256112210782 111 0.348294749888064 0.696589499776128 0.651705250111936 112 0.531675793200155 0.93664841359969 0.468324206799845 113 0.546841427832406 0.906317144335187 0.453158572167594 114 0.485806287913937 0.971612575827875 0.514193712086063 115 0.487826755308053 0.975653510616106 0.512173244691947 116 0.459031473951189 0.918062947902379 0.540968526048811 117 0.403835825911353 0.807671651822707 0.596164174088647 118 0.366706757959778 0.733413515919556 0.633293242040222 119 0.377522356351754 0.755044712703508 0.622477643648246 120 0.572709785760507 0.854580428478985 0.427290214239493 121 0.516702971945154 0.966594056109691 0.483297028054846 122 0.445988871176304 0.891977742352608 0.554011128823696 123 0.403583814967395 0.80716762993479 0.596416185032605 124 0.343731582413569 0.687463164827138 0.656268417586431 125 0.303189669888066 0.606379339776132 0.696810330111934 126 0.246878162686412 0.493756325372823 0.753121837313588 127 0.230172024234614 0.460344048469228 0.769827975765386 128 0.173206099929773 0.346412199859546 0.826793900070227 129 0.147273333152675 0.294546666305350 0.852726666847325 130 0.140646190886761 0.281292381773522 0.859353809113239 131 0.106227309112403 0.212454618224806 0.893772690887597 132 0.225094983518646 0.450189967037292 0.774905016481354 133 0.160657819732109 0.321315639464218 0.83934218026789 134 0.151029992495192 0.302059984990383 0.848970007504808 135 0.0972632458038745 0.194526491607749 0.902736754196126 136 0.0633597369443892 0.126719473888778 0.936640263055611 137 0.0307994335742104 0.0615988671484208 0.96920056642579 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 1 0.00775193798449612 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 = 2 ; 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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Software written by Ed van Stee & Patrick Wessa

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