Master regressie 1 aug 2005 zonder trend

*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, 14 Dec 2008 11:44:59 -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/Dec/14/t1229280433xt3wyaxci5nuj56.htm/, Retrieved Sun, 14 Dec 2008 19:47:23 +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/2008/Dec/14/t1229280433xt3wyaxci5nuj56.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8310 0 7649 0 7279 0 6857 0 6496 0 6280 0 8962 0 11205 0 10363 0 9175 0 8234 0 8121 0 7438 0 6876 0 6489 0 6319 0 5952 0 6055 0 9107 0 11493 0 10213 0 9238 0 8218 0 7995 0 7581 0 7051 0 6668 0 6433 0 6135 0 6365 0 10095 0 12029 0 12184 0 11331 0 9961 0 9739 0 9080 0 8507 0 8097 0 7772 0 7440 0 7902 0 13539 0 14992 0 15436 0 14156 0 12846 0 12302 0 11691 0 10648 0 10064 0 10016 0 9691 0 10260 0 16882 0 18573 0 18227 0 16346 0 14694 0 14453 0 13949 0 13277 0 12726 0 12279 0 11819 0 12207 0 18637 0 20519 0 19974 0 17802 0 15997 0 15430 0 14452 0 13614 0 13080 0 12290 0 11890 0 12292 0 18700 0 20388 1 19170 1 17530 1 15564 1 15163 1 13406 1 12763 1 12083 1 12054 1 11770 1 12266 1 17549 1 18655 1 17279 1 14788 1 13138 1 12494 1 11767 1 10928 1 10104 1 9760 1 9536 1 9978 1 14846 1 15565 1 13587 1 11804 1 10611 1 10915 1 9988 1 9376 1 9319 1 8852 1 8392 1 9050 1 13250 1 14037 1 12486 1 11182 1 10287 1

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 NWWZm[t] = + 11384.9239543726 + 1382.56147021547Dummy[t] -1033.49239543727M1[t] -1730.79239543726M2[t] -2208.79239543727M3[t] -2536.49239543726M4[t] -2887.59239543726M5[t] -2534.19239543726M6[t] + 2357.00760456273M7[t] + 3807.65145754118M8[t] + 2953.95145754119M9[t] + 1397.25145754119M10[t] + 17.0514575411919M11[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) 11384.9239543726 983.171146 11.5798 0 0 Dummy 1382.56147021547 564.506641 2.4492 0.015959 0.007979 M1 -1033.49239543727 1330.288776 -0.7769 0.438952 0.219476 M2 -1730.79239543726 1330.288776 -1.3011 0.196057 0.098029 M3 -2208.79239543727 1330.288776 -1.6604 0.099792 0.049896 M4 -2536.49239543726 1330.288776 -1.9067 0.059264 0.029632 M5 -2887.59239543726 1330.288776 -2.1707 0.032189 0.016095 M6 -2534.19239543726 1330.288776 -1.905 0.059491 0.029745 M7 2357.00760456273 1330.288776 1.7718 0.079301 0.039651 M8 3807.65145754118 1330.687963 2.8614 0.005082 0.002541 M9 2953.95145754119 1330.687963 2.2199 0.028557 0.014279 M10 1397.25145754119 1330.687963 1.05 0.296096 0.148048 M11 17.0514575411919 1330.687963 0.0128 0.9898 0.4949 Multiple Linear Regression - Regression Statistics Multiple R 0.658353306764718 R-squared 0.433429076528038 Adjusted R-squared 0.369288971984043 F-TEST (value) 6.7575361719396 F-TEST (DF numerator) 12 F-TEST (DF denominator) 106 p-value 6.64524435478597e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2894.98908069412 Sum Squared Residuals 888381948.397846 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8310 10351.4315589354 -2041.43155893541 2 7649 9654.13155893537 -2005.13155893537 3 7279 9176.13155893535 -1897.13155893535 4 6857 8848.43155893537 -1991.43155893537 5 6496 8497.33155893535 -2001.33155893535 6 6280 8850.73155893537 -2570.73155893537 7 8962 13741.9315589354 -4779.93155893536 8 11205 15192.5754119138 -3987.57541191383 9 10363 14338.8754119138 -3975.87541191381 10 9175 12782.1754119138 -3607.1754119138 11 8234 11401.9754119138 -3167.97541191381 12 8121 11384.9239543726 -3263.92395437262 13 7438 10351.4315589354 -2913.43155893535 14 6876 9654.13155893536 -2778.13155893536 15 6489 9176.13155893536 -2687.13155893536 16 6319 8848.43155893536 -2529.43155893536 17 5952 8497.33155893536 -2545.33155893536 18 6055 8850.73155893536 -2795.73155893536 19 9107 13741.9315589354 -4634.93155893536 20 11493 15192.5754119138 -3699.57541191381 21 10213 14338.8754119138 -4125.87541191382 22 9238 12782.1754119138 -3544.17541191382 23 8218 11401.9754119138 -3183.97541191381 24 7995 11384.9239543726 -3389.92395437262 25 7581 10351.4315589354 -2770.43155893536 26 7051 9654.13155893536 -2603.13155893536 27 6668 9176.13155893536 -2508.13155893536 28 6433 8848.43155893536 -2415.43155893536 29 6135 8497.33155893536 -2362.33155893536 30 6365 8850.73155893536 -2485.73155893536 31 10095 13741.9315589354 -3646.93155893536 32 12029 15192.5754119138 -3163.57541191381 33 12184 14338.8754119138 -2154.87541191382 34 11331 12782.1754119138 -1451.17541191382 35 9961 11401.9754119138 -1440.97541191381 36 9739 11384.9239543726 -1645.92395437262 37 9080 10351.4315589354 -1271.43155893536 38 8507 9654.13155893536 -1147.13155893536 39 8097 9176.13155893536 -1079.13155893536 40 7772 8848.43155893536 -1076.43155893536 41 7440 8497.33155893536 -1057.33155893536 42 7902 8850.73155893536 -948.73155893536 43 13539 13741.9315589354 -202.931558935362 44 14992 15192.5754119138 -200.575411913815 45 15436 14338.8754119138 1097.12458808618 46 14156 12782.1754119138 1373.82458808618 47 12846 11401.9754119138 1444.02458808619 48 12302 11384.9239543726 917.076045627375 49 11691 10351.4315589354 1339.56844106464 50 10648 9654.13155893536 993.86844106464 51 10064 9176.13155893536 887.868441064638 52 10016 8848.43155893536 1167.56844106464 53 9691 8497.33155893536 1193.66844106464 54 10260 8850.73155893536 1409.26844106464 55 16882 13741.9315589354 3140.06844106464 56 18573 15192.5754119138 3380.42458808619 57 18227 14338.8754119138 3888.12458808618 58 16346 12782.1754119138 3563.82458808618 59 14694 11401.9754119138 3292.02458808619 60 14453 11384.9239543726 3068.07604562738 61 13949 10351.4315589354 3597.56844106464 62 13277 9654.13155893536 3622.86844106464 63 12726 9176.13155893536 3549.86844106464 64 12279 8848.43155893536 3430.56844106464 65 11819 8497.33155893536 3321.66844106464 66 12207 8850.73155893536 3356.26844106464 67 18637 13741.9315589354 4895.06844106464 68 20519 15192.5754119138 5326.42458808619 69 19974 14338.8754119138 5635.12458808619 70 17802 12782.1754119138 5019.82458808618 71 15997 11401.9754119138 4595.02458808618 72 15430 11384.9239543726 4045.07604562737 73 14452 10351.4315589354 4100.56844106464 74 13614 9654.13155893536 3959.86844106464 75 13080 9176.13155893536 3903.86844106464 76 12290 8848.43155893536 3441.56844106464 77 11890 8497.33155893536 3392.66844106464 78 12292 8850.73155893536 3441.26844106464 79 18700 13741.9315589354 4958.06844106464 80 20388 16575.1368821293 3812.86311787072 81 19170 15721.4368821293 3448.56311787072 82 17530 14164.7368821293 3365.26311787072 83 15564 12784.5368821293 2779.46311787072 84 15163 12767.4854245881 2395.51457541191 85 13406 11733.9930291508 1672.00697084918 86 12763 11036.6930291508 1726.30697084918 87 12083 10558.6930291508 1524.30697084918 88 12054 10230.9930291508 1823.00697084917 89 11770 9879.89302915083 1890.10697084917 90 12266 10233.2930291508 2032.70697084918 91 17549 15124.4930291508 2424.50697084918 92 18655 16575.1368821293 2079.86311787072 93 17279 15721.4368821293 1557.56311787072 94 14788 14164.7368821293 623.26311787072 95 13138 12784.5368821293 353.463117870722 96 12494 12767.4854245881 -273.485424588088 97 11767 11733.9930291508 33.0069708491811 98 10928 11036.6930291508 -108.693029150823 99 10104 10558.6930291508 -454.693029150824 100 9760 10230.9930291508 -470.993029150825 101 9536 9879.89302915083 -343.893029150825 102 9978 10233.2930291508 -255.293029150823 103 14846 15124.4930291508 -278.493029150824 104 15565 16575.1368821293 -1010.13688212928 105 13587 15721.4368821293 -2134.43688212928 106 11804 14164.7368821293 -2360.73688212928 107 10611 12784.5368821293 -2173.53688212928 108 10915 12767.4854245881 -1852.48542458809 109 9988 11733.9930291508 -1745.99302915082 110 9376 11036.6930291508 -1660.69302915082 111 9319 10558.6930291508 -1239.69302915082 112 8852 10230.9930291508 -1378.99302915082 113 8392 9879.89302915082 -1487.89302915082 114 9050 10233.2930291508 -1183.29302915082 115 13250 15124.4930291508 -1874.49302915082 116 14037 16575.1368821293 -2538.13688212928 117 12486 15721.4368821293 -3235.43688212928 118 11182 14164.7368821293 -2982.73688212928 119 10287 12784.5368821293 -2497.53688212928 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0140584876916688 0.0281169753833375 0.985941512308331 17 0.00323409087549703 0.00646818175099407 0.996765909124503 18 0.000602851745354596 0.00120570349070919 0.999397148254645 19 0.000113442137774938 0.000226884275549876 0.999886557862225 20 2.16495834282337e-05 4.32991668564675e-05 0.999978350416572 21 3.92633268354398e-06 7.85266536708796e-06 0.999996073667316 22 6.61342024646314e-07 1.32268404929263e-06 0.999999338657975 23 1.06306978890933e-07 2.12613957781866e-07 0.999999893693021 24 1.78135193401242e-08 3.56270386802485e-08 0.99999998218648 25 3.53883010680272e-09 7.07766021360545e-09 0.99999999646117 26 6.297281479538e-10 1.2594562959076e-09 0.999999999370272 27 1.13055795585362e-10 2.26111591170725e-10 0.999999999886944 28 1.89926809380461e-11 3.79853618760922e-11 0.999999999981007 29 3.03999596620486e-12 6.07999193240972e-12 0.99999999999696 30 5.71024858953053e-13 1.14204971790611e-12 0.999999999999429 31 4.54877122254607e-12 9.09754244509215e-12 0.99999999999545 32 4.30152245200739e-12 8.60304490401477e-12 0.999999999995699 33 5.92221593159693e-10 1.18444318631939e-09 0.999999999407778 34 1.83396088477133e-08 3.66792176954265e-08 0.999999981660391 35 6.65446710192931e-08 1.33089342038586e-07 0.99999993345533 36 1.83361287048308e-07 3.66722574096615e-07 0.999999816638713 37 2.70281667079154e-07 5.40563334158309e-07 0.999999729718333 38 3.91358265750733e-07 7.82716531501465e-07 0.999999608641734 39 5.36794631249903e-07 1.07358926249981e-06 0.999999463205369 40 7.07610472114044e-07 1.41522094422809e-06 0.999999292389528 41 9.7597346208963e-07 1.95194692417926e-06 0.999999024026538 42 2.41514049577081e-06 4.83028099154162e-06 0.999997584859504 43 0.000361567061176056 0.000723134122352111 0.999638432938824 44 0.00311433669933933 0.00622867339867866 0.99688566330066 45 0.027083058860248 0.054166117720496 0.972916941139752 46 0.0817663474995018 0.163532694999004 0.918233652500498 47 0.157117937859072 0.314235875718144 0.842882062140928 48 0.242016582090128 0.484033164180257 0.757983417909872 49 0.330393414859281 0.660786829718562 0.669606585140719 50 0.402191218631695 0.80438243726339 0.597808781368305 51 0.466901164498354 0.933802328996708 0.533098835501646 52 0.532651110554994 0.934697778890012 0.467348889445006 53 0.595204664145763 0.809590671708474 0.404795335854237 54 0.671151106708901 0.657697786582197 0.328848893291099 55 0.847294508527811 0.305410982944377 0.152705491472189 56 0.92498478618515 0.150030427629699 0.0750152138148494 57 0.960411135985106 0.0791777280297879 0.0395888640148940 58 0.972538446626108 0.0549231067477839 0.0274615533738919 59 0.977829844161277 0.0443403116774468 0.0221701558387234 60 0.982247045119248 0.0355059097615037 0.0177529548807519 61 0.985733078100697 0.0285338437986062 0.0142669218993031 62 0.98806234230214 0.0238753153957186 0.0119376576978593 63 0.989398678976214 0.0212026420475710 0.0106013210237855 64 0.989933894509593 0.0201322109808130 0.0100661054904065 65 0.990067757789201 0.0198644844215974 0.0099322422107987 66 0.99032687969932 0.0193462406013599 0.00967312030067996 67 0.993239548149827 0.013520903700347 0.0067604518501735 68 0.994903606108928 0.0101927877821434 0.00509639389107168 69 0.996174000790913 0.00765199841817462 0.00382599920908731 70 0.996374320830526 0.00725135833894708 0.00362567916947354 71 0.99608069664093 0.00783860671813936 0.00391930335906968 72 0.995296314113972 0.00940737177205507 0.00470368588602753 73 0.994265436353676 0.0114691272926478 0.00573456364632389 74 0.992810534716106 0.0143789305677872 0.00718946528389362 75 0.990862509498304 0.0182749810033921 0.00913749050169606 76 0.98788272693872 0.0242345461225614 0.0121172730612807 77 0.98403341670581 0.0319331665883793 0.0159665832941897 78 0.979910984836941 0.0401780303261175 0.0200890151630588 79 0.976807088894523 0.0463858222109542 0.0231929111054771 80 0.981318665371727 0.0373626692565455 0.0186813346282727 81 0.987822804012094 0.0243543919758122 0.0121771959879061 82 0.993698787655826 0.0126024246883485 0.00630121234417424 83 0.996071839428955 0.00785632114209021 0.00392816057104511 84 0.996481349377886 0.00703730124422742 0.00351865062211371 85 0.995756008620016 0.0084879827599688 0.0042439913799844 86 0.995012637755023 0.00997472448995322 0.00498736224497661 87 0.993736417531999 0.0125271649360026 0.00626358246800128 88 0.993022095401533 0.0139558091969337 0.00697790459846683 89 0.992465279328376 0.0150694413432481 0.00753472067162406 90 0.991801088853398 0.0163978222932047 0.00819891114660233 91 0.993909801590534 0.0121803968189318 0.00609019840946589 92 0.996931183293353 0.00613763341329509 0.00306881670664754 93 0.999368084114712 0.00126383177057689 0.000631915885288446 94 0.999819546866508 0.000360906266984689 0.000180453133492345 95 0.999936310340528 0.000127379318944117 6.36896594720587e-05 96 0.99989142945593 0.00021714108814161 0.000108570544070805 97 0.999851793727667 0.000296412544665926 0.000148206272332963 98 0.999753368203589 0.000493263592822708 0.000246631796411354 99 0.999228437737019 0.00154312452596280 0.000771562262981398 100 0.99776632925822 0.00446734148355936 0.00223367074177968 101 0.994428334885469 0.0111433302290623 0.00557166511453115 102 0.983898617040084 0.0322027659198314 0.0161013829599157 103 0.971721629980197 0.0565567400396054 0.0282783700198027 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 45 0.511363636363636 NOK 5% type I error level 73 0.829545454545455 NOK 10% type I error level 77 0.875 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/1038ls1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/1038ls1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/11b4p1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/11b4p1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/2iytb1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/2iytb1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/34cag1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/34cag1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/49rxw1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/49rxw1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/5exwt1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/5exwt1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/6uvye1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/6uvye1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/721ty1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/721ty1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/8gi8x1229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/8gi8x1229280289.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/9k7d01229280289.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229280433xt3wyaxci5nuj56/9k7d01229280289.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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