Paper - Multiple Regression Model 2

*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: Sun, 12 Dec 2010 19:44:36 +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/12/t1292182953gqah36lcjh2trhr.htm/, Retrieved Sun, 12 Dec 2010 20:42:43 +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/12/t1292182953gqah36lcjh2trhr.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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25.94 23688100 39.18 3940.35 0.0274 144.7 28.66 13741000 35.78 4696.69 0.0322 140.8 33.95 14143500 42.54 4572.83 0.0376 137.1 31.01 16763800 27.92 3860.66 0.0307 137.7 21.00 16634600 25.05 3400.91 0.0319 144.7 26.19 13693300 32.03 3966.11 0.0373 139.2 25.41 10545800 27.95 3766.99 0.0366 143.0 30.47 9409900 27.95 4206.35 0.0341 140.8 12.88 39182200 24.15 3672.82 0.0345 142.5 9.78 37005800 27.57 3369.63 0.0345 135.8 8.25 15818500 22.97 2597.93 0.0345 132.6 7.44 16952000 17.37 2470.52 0.0339 128.6 10.81 24563400 24.45 2772.73 0.0373 115.7 9.12 14163200 23.62 2151.83 0.0353 109.2 11.03 18184800 21.90 1840.26 0.0292 116.9 12.74 20810300 27.12 2116.24 0.0327 109.9 9.98 12843000 27.70 2110.49 0.0362 116.1 11.62 13866700 29.23 2160.54 0.0325 118.9 9.40 15119200 26.50 2027.13 0.0272 116.3 9.27 8301600 22.84 1805.43 0.0272 114.0 7.76 14039600 20.49 1498.80 0.0265 97.0 8.78 12139700 23.28 1690.20 0.0213 85.3 10.65 9649000 25.71 1930.58 0.019 84.9 10.95 8513600 26.52 1950.40 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Apple[t] = + 9.48893045838803 + 1.39428397483603e-06Volume[t] + 6.78801022485275Microsoft[t] + 0.0317791423789989NASDAQ[t] -554.897363696999Inflatie[t] -2.09444585750021Consumentenvertrouwen[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) 9.48893045838803 24.535885 0.3867 0.699614 0.349807 Volume 1.39428397483603e-06 0 4.1451 6.2e-05 3.1e-05 Microsoft 6.78801022485275 1.386798 4.8947 3e-06 1e-06 NASDAQ 0.0317791423789989 0.0102 3.1155 0.002282 0.001141 Inflatie -554.897363696999 313.826586 -1.7682 0.079492 0.039746 Consumentenvertrouwen -2.09444585750021 0.172289 -12.1566 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.849984571934886 R-squared 0.72247377252733 Adjusted R-squared 0.711283198838917 F-TEST (value) 64.5609235633136 F-TEST (DF numerator) 5 F-TEST (DF denominator) 124 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 41.1810422653399 Sum Squared Residuals 210288.902015404 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.94 115.421549619941 -89.4815496199414 2 28.66 108.013900774789 -79.353900774789 3 33.95 155.278888528389 -121.328888528389 4 31.01 39.6315938072626 -8.6215938072626 5 21 -9.9675955752965 30.9675955752965 6 26.19 59.796286063888 -33.6062860638880 7 25.41 13.8143666012734 11.5956333987266 8 30.47 32.1881077256371 -1.71810772563706 9 12.88 27.1670669185108 -14.2870669185108 10 9.78 51.7452113120368 -41.9652113120368 11 8.25 -26.842486012202 35.092486012202 12 7.44 -58.61318106819 66.05318106819 13 10.81 34.7940595133745 -23.9840595133745 14 9.12 9.65120212968203 -0.53120212968203 15 11.03 -19.0607095990887 30.0907095990887 16 12.74 41.5227842938927 -28.7827842938927 17 9.98 19.2407163534762 -9.26071635347617 18 11.62 28.8329184232877 -17.2129184232877 19 9.4 16.1948510602343 -6.79485106023428 20 9.27 -20.3831471827425 29.6531471827425 21 7.76 -2.08500045911836 9.84500045911836 22 8.78 47.6775586197471 -38.8975586197471 23 10.65 70.4527928945821 -59.8027928945821 24 10.95 56.6238897088233 -45.6738897088233 25 12.36 54.2529583579005 -41.8929583579005 26 10.85 39.5220721633665 -28.6720721633665 27 11.84 5.50752579821238 6.33247420178762 28 12.14 -16.0295121896504 28.1695121896504 29 11.65 -24.0361997528486 35.6861997528486 30 8.86 -1.57806188461803 10.4380618846180 31 7.63 -10.9241332982918 18.5541332982918 32 7.38 -11.1566684400972 18.5366684400972 33 7.25 -25.2671765313766 32.5171765313766 34 8.03 34.0506299829973 -26.0206299829974 35 7.75 34.8322917498734 -27.0822917498734 36 7.16 21.432454313487 -14.2724543134870 37 7.18 16.1139774831567 -8.9339774831567 38 7.51 40.2759691315231 -32.7659691315231 39 7.07 48.9409301486713 -41.8709301486713 40 7.11 33.0038018799066 -25.8938018799066 41 8.98 34.4222232231503 -25.4422232231503 42 9.53 29.3214219829883 -19.7914219829883 43 10.54 48.1130305213185 -37.5730305213185 44 11.31 39.3442745757809 -28.0342745757809 45 10.36 57.0634917905285 -46.7034917905285 46 11.44 47.5341067214022 -36.0941067214022 47 10.45 22.4490182285533 -11.9990182285533 48 10.69 33.4837552487065 -22.7937552487065 49 11.28 34.3792826394293 -23.0992826394293 50 11.96 38.8542628992827 -26.8942628992827 51 13.52 40.5773058701212 -27.0573058701212 52 12.89 28.5955660845458 -15.7055660845458 53 14.03 18.1157925985439 -4.08579259854394 54 16.27 18.1043740752621 -1.83437407526207 55 16.17 11.2543458909931 4.91565410900689 56 17.25 15.6119185658775 1.63808143412255 57 19.38 24.3963342241151 -5.01633422411511 58 26.2 53.8780333463027 -27.6780333463027 59 33.53 70.7754972939705 -37.2454972939705 60 32.2 45.9573349515712 -13.7573349515712 61 38.45 67.6970548701849 -29.2470548701849 62 44.86 51.9489299105486 -7.08892991054859 63 41.67 23.0630944390979 18.6069055609021 64 36.06 49.9894447454484 -13.9294447454484 65 39.76 34.3000077974638 5.45999220253624 66 36.81 18.1792022633500 18.6307977366500 67 42.65 28.8350839539555 13.8149160460445 68 46.89 27.3045218917650 19.5854781082350 69 53.61 58.2979121076116 -4.68791210761155 70 57.59 79.6994817670672 -22.1094817670672 71 67.82 60.4199551166812 7.40004488331881 72 71.89 37.8829564772564 34.0070435227436 73 75.51 68.3412721506914 7.16872784930861 74 68.49 66.6594398890978 1.83056011090215 75 62.72 61.383547743893 1.33645225610697 76 70.39 38.7048123928962 31.6851876071038 77 59.77 16.0632157825285 43.7067842174715 78 57.27 21.0372914498579 36.2327085501421 79 67.96 24.9183405791514 43.0416594208486 80 67.85 49.0129331014277 18.8370668985723 81 76.98 64.693605912624 12.2863940873761 82 81.08 71.0928180799886 9.98718192001143 83 91.66 74.931294337327 16.7287056626730 84 84.84 74.4559663520379 10.3840336479621 85 85.73 109.523713720736 -23.7937137207361 86 84.61 56.2287201675384 28.3812798324616 87 92.91 57.8047239435824 35.1052760564176 88 99.8 76.9944944731629 22.8055055268371 89 121.19 86.80139027519 34.3886097248099 90 122.04 101.382796448586 20.6572035514143 91 131.76 87.7592150758376 44.0007849241624 92 138.48 97.1998743609119 41.2801256390881 93 153.47 115.440317300431 38.0296826995687 94 189.95 167.565053424562 22.3849465754383 95 182.22 165.517975279524 16.7020247204762 96 198.08 152.804683080681 45.2753169193188 97 135.36 172.166195450764 -36.8061954507642 98 125.02 138.513865608969 -13.4938656089694 99 143.5 162.587267847151 -19.0872678471510 100 173.95 170.640427154688 3.30957284531218 101 188.75 174.238113214786 14.5118867852137 102 167.44 173.567204202150 -6.12720420215048 103 158.95 155.118353011957 3.83164698804278 104 169.53 140.934886135443 28.5951138645565 105 113.66 153.094860224333 -39.4348602243327 106 107.59 199.995764596504 -92.405764596504 107 92.67 152.341108692389 -59.6711086923891 108 85.35 151.71349712705 -66.36349712705 109 90.13 205.675408912230 -115.545408912230 110 89.31 142.771979287184 -53.461979287184 111 105.12 160.182359654946 -55.0623596549463 112 125.83 144.431935171467 -18.6019351714672 113 135.81 120.410626353669 15.3993736463312 114 142.43 158.620706079367 -16.1907060793667 115 163.39 165.297872356403 -1.90787235640308 116 168.21 150.683661296798 17.5263387032024 117 185.35 167.462148605516 17.8878513944836 118 188.5 187.977489334938 0.522510665062 119 199.91 179.124445747849 20.7855542521511 120 210.73 183.522233278183 27.2077667218173 121 192.06 175.237614012086 16.8223859879140 122 204.62 192.690839676123 11.9291603238774 123 235 186.281269774031 48.7187302259689 124 261.09 189.08330299218 72.0066970078198 125 256.88 158.870666704457 98.0093332955427 126 251.53 150.699938209945 100.830061790055 127 257.25 178.845230055292 78.4047699447084 128 243.1 139.856009838188 103.243990161812 129 283.75 171.534742204531 112.215257795469 130 300.98 188.034982428891 112.945017571109 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.000260337460891707 0.000520674921783415 0.999739662539108 10 0.000295371834282952 0.000590743668565905 0.999704628165717 11 9.96925170550665e-05 0.000199385034110133 0.999900307482945 12 1.13301665874082e-05 2.26603331748163e-05 0.999988669833413 13 1.90658842469306e-06 3.81317684938611e-06 0.999998093411575 14 2.05290680238428e-07 4.10581360476855e-07 0.99999979470932 15 4.12834609557374e-08 8.25669219114749e-08 0.99999995871654 16 6.33910195892225e-09 1.26782039178445e-08 0.999999993660898 17 6.57883000650159e-10 1.31576600130032e-09 0.999999999342117 18 6.99077558624668e-11 1.39815511724934e-10 0.999999999930092 19 1.29073426561961e-11 2.58146853123923e-11 0.999999999987093 20 1.82051247020740e-12 3.64102494041480e-12 0.99999999999818 21 1.86966571416954e-13 3.73933142833908e-13 0.999999999999813 22 3.08152286769874e-14 6.16304573539749e-14 0.99999999999997 23 6.95111659005425e-15 1.39022331801085e-14 0.999999999999993 24 1.42021528576288e-15 2.84043057152576e-15 0.999999999999999 25 1.89461252507148e-16 3.78922505014296e-16 1 26 2.09732528681803e-17 4.19465057363607e-17 1 27 1.96234212289604e-18 3.92468424579208e-18 1 28 2.60405601462145e-19 5.20811202924289e-19 1 29 3.00405772767875e-20 6.0081154553575e-20 1 30 3.22084662707235e-21 6.4416932541447e-21 1 31 4.90261142029008e-22 9.80522284058015e-22 1 32 7.09276014148806e-23 1.41855202829761e-22 1 33 2.93055930515611e-23 5.86111861031221e-23 1 34 3.59411560943121e-24 7.18823121886242e-24 1 35 3.52539528928225e-25 7.0507905785645e-25 1 36 4.53985402566677e-26 9.07970805133354e-26 1 37 1.12435667121651e-26 2.24871334243301e-26 1 38 1.48387739473242e-27 2.96775478946485e-27 1 39 1.48382692923179e-28 2.96765385846358e-28 1 40 1.92503728086135e-29 3.8500745617227e-29 1 41 3.62683406085936e-30 7.25366812171872e-30 1 42 3.57570131425682e-31 7.15140262851364e-31 1 43 3.21430274875752e-32 6.42860549751505e-32 1 44 2.93767887537945e-33 5.8753577507589e-33 1 45 3.05227347793155e-34 6.10454695586311e-34 1 46 4.49752921999721e-35 8.99505843999442e-35 1 47 8.31419652409245e-36 1.66283930481849e-35 1 48 2.29480139386652e-36 4.58960278773303e-36 1 49 3.80658011866872e-37 7.61316023733743e-37 1 50 1.30005671320884e-37 2.60011342641768e-37 1 51 9.6952513444523e-38 1.93905026889046e-37 1 52 2.73284598427192e-38 5.46569196854383e-38 1 53 8.65844524469478e-39 1.73168904893896e-38 1 54 2.7301622255363e-38 5.4603244510726e-38 1 55 1.91817538737984e-37 3.83635077475967e-37 1 56 6.89457383820388e-37 1.37891476764078e-36 1 57 5.55553390633631e-36 1.11110678126726e-35 1 58 2.90102755705065e-31 5.8020551141013e-31 1 59 2.37094512698381e-27 4.74189025396762e-27 1 60 1.26250527307228e-25 2.52501054614456e-25 1 61 1.76743811940447e-24 3.53487623880894e-24 1 62 1.04788383271692e-22 2.09576766543384e-22 1 63 9.41969779294884e-21 1.88393955858977e-20 1 64 9.10017335938205e-21 1.82003467187641e-20 1 65 8.62334657470385e-20 1.72466931494077e-19 1 66 5.40342528175047e-19 1.08068505635009e-18 1 67 7.35824602605841e-18 1.47164920521168e-17 1 68 1.74494188390613e-16 3.48988376781227e-16 1 69 1.04147917353816e-15 2.08295834707632e-15 0.999999999999999 70 1.83195578928673e-15 3.66391157857345e-15 0.999999999999998 71 1.72914445992471e-13 3.45828891984943e-13 0.999999999999827 72 1.60391292798948e-11 3.20782585597895e-11 0.99999999998396 73 4.49839090787498e-11 8.99678181574996e-11 0.999999999955016 74 6.31578855418951e-11 1.26315771083790e-10 0.999999999936842 75 6.15436871011405e-11 1.23087374202281e-10 0.999999999938456 76 8.99825161643034e-11 1.79965032328607e-10 0.999999999910018 77 8.81011538748269e-11 1.76202307749654e-10 0.999999999911899 78 5.93741714511972e-11 1.18748342902394e-10 0.999999999940626 79 3.65101516517629e-10 7.30203033035258e-10 0.999999999634898 80 5.90933388720419e-10 1.18186677744084e-09 0.999999999409067 81 5.35931592884111e-09 1.07186318576822e-08 0.999999994640684 82 1.62056191597256e-07 3.24112383194512e-07 0.999999837943808 83 2.22099651926736e-06 4.44199303853473e-06 0.99999777900348 84 3.43763357994366e-06 6.87526715988732e-06 0.99999656236642 85 2.22366122486276e-06 4.44732244972551e-06 0.999997776338775 86 4.83213644982896e-06 9.66427289965792e-06 0.99999516786355 87 1.40417153606695e-05 2.80834307213390e-05 0.99998595828464 88 6.39372264870552e-05 0.000127874452974110 0.999936062773513 89 0.000317702336252145 0.00063540467250429 0.999682297663748 90 0.000528334956876484 0.00105666991375297 0.999471665043123 91 0.000734162459626029 0.00146832491925206 0.999265837540374 92 0.00147187549599081 0.00294375099198163 0.99852812450401 93 0.00527298784291521 0.0105459756858304 0.994727012157085 94 0.0307205567397077 0.0614411134794153 0.969279443260292 95 0.038396403712929 0.076792807425858 0.961603596287071 96 0.0736975322119308 0.147395064423862 0.926302467788069 97 0.0744924306242567 0.148984861248513 0.925507569375743 98 0.0761982978556542 0.152396595711308 0.923801702144346 99 0.0813821609051711 0.162764321810342 0.918617839094829 100 0.121252137762470 0.242504275524939 0.87874786223753 101 0.292419921805565 0.58483984361113 0.707580078194435 102 0.290467325021326 0.580934650042652 0.709532674978674 103 0.419218255968304 0.838436511936609 0.580781744031696 104 0.75948142588584 0.481037148228321 0.240518574114160 105 0.996439196156486 0.00712160768702712 0.00356080384351356 106 0.99805869792664 0.00388260414672112 0.00194130207336056 107 0.996478101865371 0.00704379626925709 0.00352189813462855 108 0.998187870475993 0.00362425904801428 0.00181212952400714 109 0.996713305693163 0.00657338861367405 0.00328669430683702 110 0.99357440134082 0.0128511973183614 0.00642559865918071 111 0.988081952867515 0.0238360942649701 0.0119180471324851 112 0.98971260988395 0.0205747802320978 0.0102873901160489 113 0.992420370362919 0.0151592592741622 0.00757962963708109 114 0.985918209857206 0.0281635802855887 0.0140817901427943 115 0.984771912593652 0.0304561748126949 0.0152280874063475 116 0.98142858250681 0.0371428349863806 0.0185714174931903 117 0.99946140970139 0.00107718059722096 0.00053859029861048 118 0.99993301997469 0.000133960050618823 6.69800253094114e-05 119 0.99965830471134 0.00068339057732134 0.00034169528866067 120 0.999939701250538 0.000120597498924632 6.02987494623161e-05 121 0.999214033297928 0.00157193340414430 0.000785966702072151 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 94 0.831858407079646 NOK 5% type I error level 102 0.902654867256637 NOK 10% type I error level 104 0.920353982300885 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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