seabelt law Q3.2

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 06:05:29 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8.htm/, Retrieved Thu, 27 Nov 2008 13:16:26 +0000

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/2008/Nov/27/t12277917862xht04dub09jdy8.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Severijns Britt

Dataseries X:

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492865 0 480961 0 461935 0 456608 0 441977 0 439148 0 488180 0 520564 0 501492 0 485025 0 464196 0 460170 0 467037 0 460070 0 447988 0 442867 0 436087 0 431328 0 484015 0 509673 0 512927 0 502831 0 470984 0 471067 0 476049 0 474605 0 470439 0 461251 0 454724 0 455626 0 516847 0 525192 0 522975 0 518585 0 509239 0 512238 0 519164 0 517009 0 509933 0 509127 0 500857 0 506971 0 569323 0 579714 0 577992 0 565464 0 547344 0 554788 0 562325 0 560854 0 555332 0 543599 0 536662 0 542722 1 593530 1 610763 1 612613 1 611324 1 594167 1 595454 1 590865 1 589379 1 584428 1 573100 1 567456 1 569028 1 620735 1 628884 1 628232 1 612117 1 595404 1 597141 1 593408 1 590072 1 579799 1 574205 1 572775 1 572942 1 619567 1 625809 1 619916 1 587625 1 565742 1 557274 1 560576 1 548854 0 531673 0 525919 0 511038 0 498662 0 555362 0 564591 0 541657 0 527070 0 509846 0 514258 0 516922 0 507561 0 492622 0 490243 0 469357 0 477580 0 528379 0 533590 0

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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation W[t] = + 477621.443604266 + 72190.5134428678D[t] + 3835.02730592134M1[t] + 5797.27206152402M2[t] -5302.76245430301M3[t] -12182.1303034634M4[t] -22256.6092637348M5[t] -30456.7008287694M6[t] + 22570.709099848M7[t] + 35699.3412506877M8[t] + 33488.1868808144M9[t] + 19497.3329205429M10[t] -163.021039728533M11[t] + 520.478960271469t + 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) 477621.443604266 9883.832175 48.3235 0 0 D 72190.5134428678 5794.281589 12.4589 0 0 M1 3835.02730592134 12148.876483 0.3157 0.752984 0.376492 M2 5797.27206152402 12169.375634 0.4764 0.634957 0.317478 M3 -5302.76245430301 12169.56699 -0.4357 0.664069 0.332034 M4 -12182.1303034634 12170.400547 -1.001 0.319528 0.159764 M5 -22256.6092637348 12171.876172 -1.8285 0.070783 0.035391 M6 -30456.7008287694 12144.165812 -2.5079 0.013937 0.006968 M7 22570.709099848 12145.154176 1.8584 0.066379 0.03319 M8 35699.3412506877 12146.785911 2.939 0.004183 0.002092 M9 33488.1868808144 12496.588611 2.6798 0.00876 0.00438 M10 19497.3329205429 12495.024953 1.5604 0.122173 0.061087 M11 -163.021039728533 12494.086665 -0.013 0.989618 0.494809 t 520.478960271469 88.406251 5.8874 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.897427950099305 R-squared 0.805376925619441 Adjusted R-squared 0.777264703764472 F-TEST (value) 28.6486400745685 F-TEST (DF numerator) 13 F-TEST (DF denominator) 90 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 24987.5477726451 Sum Squared Residuals 56193978932.1201 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 492865 481976.949870457 10888.0501295426 2 480961 484459.673586332 -3498.6735863321 3 461935 473880.118030777 -11945.1180307766 4 456608 467521.229141888 -10913.2291418877 5 441977 457967.229141888 -15990.2291418877 6 439148 450287.616537125 -11139.6165371246 7 488180 503835.505426014 -15655.5054260135 8 520564 517484.616537125 3079.38346287537 9 501492 515793.941127523 -14301.9411275228 10 485025 502323.566127523 -17298.5661275229 11 464196 483183.691127523 -18987.6911275229 12 460170 483867.191127523 -23697.1911275229 13 467037 488222.697393716 -21185.6973937157 14 460070 490705.42110959 -30635.4211095898 15 447988 480125.865554034 -32137.8655540343 16 442867 473766.976665145 -30899.9766651454 17 436087 464212.976665145 -28125.9766651454 18 431328 456533.364060382 -25205.3640603823 19 484015 510081.252949271 -26066.2529492712 20 509673 523730.364060382 -14057.3640603823 21 512927 522039.68865078 -9112.68865078049 22 502831 508569.31365078 -5738.31365078048 23 470984 489429.43865078 -18445.4386507805 24 471067 490112.93865078 -19045.9386507805 25 476049 494468.444916973 -18419.4449169733 26 474605 496951.168632847 -22346.1686328475 27 470439 486371.613077292 -15932.6130772919 28 461251 480012.724188403 -18761.724188403 29 454724 470458.724188403 -15734.724188403 30 455626 462779.11158364 -7153.1115836399 31 516847 516327.000472529 519.9995274712 32 525192 529976.11158364 -4784.11158363991 33 522975 528285.436174038 -5310.43617403812 34 518585 514815.061174038 3769.93882596189 35 509239 495675.186174038 13563.8138259619 36 512238 496358.686174038 15879.3138259619 37 519164 500714.192440231 18449.8075597691 38 517009 503196.916156105 13812.0838438949 39 509933 492617.360600549 17315.6393994505 40 509127 486258.471711661 22868.5282883394 41 500857 476704.471711661 24152.5282883394 42 506971 469024.859106898 37946.1408931024 43 569323 522572.747995786 46750.2520042135 44 579714 536221.859106898 43492.1408931024 45 577992 534531.183697296 43460.8163027042 46 565464 521060.808697296 44403.1913027042 47 547344 501920.933697296 45423.0663027043 48 554788 502604.433697296 52183.5663027042 49 562325 506959.939963489 55365.0600365114 50 560854 509442.663679363 51411.3363206373 51 555332 498863.108123807 56468.8918761928 52 543599 492504.219234918 51094.7807650817 53 536662 482950.219234918 53711.7807650817 54 542722 547461.120073023 -4739.12007302299 55 593530 601009.008961912 -7479.00896191187 56 610763 614658.120073023 -3895.12007302298 57 612613 612967.444663421 -354.444663421198 58 611324 599497.069663421 11826.9303365788 59 594167 580357.194663421 13809.8053365788 60 595454 581040.694663421 14413.3053365788 61 590865 585396.200929614 5468.79907038598 62 589379 587878.924645488 1500.07535451183 63 584428 577299.369089933 7128.6309100674 64 573100 570940.480201044 2159.51979895629 65 567456 561386.480201044 6069.51979895629 66 569028 553706.867596281 15321.1324037194 67 620735 607254.75648517 13480.2435148305 68 628884 620903.867596281 7980.13240371939 69 628232 619213.192186679 9018.80781332117 70 612117 605742.817186679 6374.18281332118 71 595404 586602.942186679 8801.05781332117 72 597141 587286.442186679 9854.55781332117 73 593408 591641.948452872 1766.05154712835 74 590072 594124.672168746 -4052.6721687458 75 579799 583545.11661319 -3746.11661319024 76 574205 577186.227724301 -2981.22772430134 77 572775 567632.227724301 5142.77227569865 78 572942 559952.615119538 12989.3848804617 79 619567 613500.504008427 6066.49599157287 80 625809 627149.615119538 -1340.61511953825 81 619916 625458.939709937 -5542.93970993646 82 587625 611988.564709937 -24363.5647099365 83 565742 592848.689709937 -27106.6897099365 84 557274 593532.189709937 -36258.1897099365 85 560576 597887.695976129 -37311.6959761293 86 548854 528179.906249136 20674.0937508644 87 531673 517600.35069358 14072.6493064199 88 525919 511241.461804691 14677.5381953088 89 511038 501687.461804691 9350.53819530884 90 498662 494007.849199928 4654.15080007194 91 555362 547555.738088817 7806.26191118304 92 564591 561204.849199928 3386.15080007192 93 541657 559514.173790326 -17857.1737903263 94 527070 546043.798790326 -18973.7987903263 95 509846 526903.923790326 -17057.9237903263 96 514258 527587.423790326 -13329.4237903263 97 516922 531942.930056519 -15020.9300565191 98 507561 534425.653772393 -26864.6537723932 99 492622 523846.098216838 -31224.0982168377 100 490243 517487.209327949 -27244.2093279488 101 469357 507933.209327949 -38576.2093279488 102 477580 500253.596723186 -22673.5967231857 103 528379 553801.485612075 -25422.4856120746 104 533590 567450.596723186 -33860.5967231857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0207481872303212 0.0414963744606423 0.979251812769679 18 0.00681232256540594 0.0136246451308119 0.993187677434594 19 0.00307675434294558 0.00615350868589116 0.996923245657054 20 0.000749460807374477 0.00149892161474895 0.999250539192626 21 0.00274265710320251 0.00548531420640502 0.997257342896797 22 0.00655513436555751 0.0131102687311150 0.993444865634442 23 0.00433384769946563 0.00866769539893126 0.995666152300534 24 0.00362339690307741 0.00724679380615481 0.996376603096923 25 0.00184955489537949 0.00369910979075897 0.99815044510462 26 0.00141546715744973 0.00283093431489946 0.99858453284255 27 0.00226267295297256 0.00452534590594512 0.997737327047027 28 0.00250718799681412 0.00501437599362823 0.997492812003186 29 0.00357928851740956 0.00715857703481911 0.99642071148259 30 0.00578879415688482 0.0115775883137696 0.994211205843115 31 0.0157758855703139 0.0315517711406278 0.984224114429686 32 0.0155119893690862 0.0310239787381723 0.984488010630914 33 0.0192698581809809 0.0385397163619619 0.98073014181902 34 0.0286550005766302 0.0573100011532604 0.97134499942337 35 0.0868547093648454 0.173709418729691 0.913145290635155 36 0.212819372899478 0.425638745798957 0.787180627100522 37 0.274100138120562 0.548200276241123 0.725899861879438 38 0.411396216520093 0.822792433040187 0.588603783479907 39 0.58236908660789 0.83526182678422 0.41763091339211 40 0.74405103767834 0.51189792464332 0.25594896232166 41 0.863997501083263 0.272004997833473 0.136002498916737 42 0.924506563577798 0.150986872844404 0.0754934364222022 43 0.960098615948234 0.0798027681035318 0.0399013840517659 44 0.963551225080577 0.072897549838846 0.036448774919423 45 0.96778199033048 0.0644360193390393 0.0322180096695196 46 0.966843108706323 0.0663137825873535 0.0331568912936767 47 0.966675400803034 0.066649198393931 0.0333245991969655 48 0.969930778245975 0.0601384435080494 0.0300692217540247 49 0.965489594518015 0.0690208109639696 0.0345104054819848 50 0.960332908467667 0.0793341830646666 0.0396670915323333 51 0.959675833162884 0.0806483336742311 0.0403241668371156 52 0.950701274416597 0.098597451166805 0.0492987255834025 53 0.945910270546864 0.108179458906272 0.0540897294531358 54 0.967882609792153 0.0642347804156949 0.0321173902078474 55 0.989026826580686 0.0219463468386288 0.0109731734193144 56 0.995776641145158 0.00844671770968426 0.00422335885484213 57 0.997900699387145 0.00419860122570928 0.00209930061285464 58 0.997142328098608 0.00571534380278394 0.00285767190139197 59 0.996105840060914 0.00778831987817133 0.00389415993908566 60 0.994456896725903 0.0110862065481934 0.00554310327409671 61 0.99408959512355 0.0118208097529010 0.00591040487645052 62 0.996308749195983 0.00738250160803446 0.00369125080401723 63 0.996112831837128 0.00777433632574413 0.00388716816287207 64 0.998199755560795 0.0036004888784097 0.00180024443920485 65 0.998824156225739 0.00235168754852229 0.00117584377426115 66 0.999254855925422 0.00149028814915703 0.000745144074578517 67 0.999710392734417 0.000579214531165485 0.000289607265582743 68 0.999956025373731 8.79492525376047e-05 4.39746262688023e-05 69 0.999955775918749 8.8448162502105e-05 4.42240812510525e-05 70 0.999920688614152 0.000158622771695443 7.93113858477213e-05 71 0.999834123269144 0.000331753461712690 0.000165876730856345 72 0.999635050202043 0.000729899595913218 0.000364949797956609 73 0.999498366592345 0.00100326681530987 0.000501633407654933 74 0.9995612546913 0.000877490617400805 0.000438745308700403 75 0.999371714278018 0.00125657144396388 0.00062828572198194 76 0.999261321113862 0.00147735777227539 0.000738678886137694 77 0.998591790357242 0.00281641928551673 0.00140820964275837 78 0.997921249175764 0.00415750164847271 0.00207875082423635 79 0.995745301834366 0.0085093963312688 0.0042546981656344 80 0.99215308900742 0.0156938219851607 0.00784691099258036 81 0.998947962961277 0.00210407407744529 0.00105203703872264 82 0.999124604804148 0.00175079039170469 0.000875395195852343 83 0.99908929428001 0.00182141143997973 0.000910705719989863 84 0.997649972224083 0.0047000555518349 0.00235002777591745 85 0.993588156007932 0.0128236879841359 0.00641184399206794 86 0.985678172256464 0.0286436554870716 0.0143218277435358 87 0.96264970429756 0.0747005914048792 0.0373502957024396 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 36 0.507042253521127 NOK 5% type I error level 49 0.690140845070423 NOK 10% type I error level 62 0.873239436619718 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/10ozt31227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/10ozt31227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/18os01227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/18os01227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/229e21227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/229e21227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/3iima1227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/3iima1227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/4kcny1227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/4kcny1227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/5fme81227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/5fme81227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/6g4b31227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/6g4b31227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/7e4w61227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/7e4w61227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/8vfcs1227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/8vfcs1227791115.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/94gtn1227791115.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277917862xht04dub09jdy8/94gtn1227791115.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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