paper - multiple regression - link 3

*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, 13 Dec 2009 04:12:30 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/13/t1260702856w5slm9f6t0zzcn6.htm/, Retrieved Sun, 13 Dec 2009 12:14:29 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/13/t1260702856w5slm9f6t0zzcn6.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

100.00 100.00 103.53 102.62 108.36 107.62 115.20 103.46 123.51 103.61 132.87 106.10 130.55 107.13 136.68 108.82 140.63 112.93 143.47 109.35 124.10 108.75 111.49 110.83 119.93 110.95 131.79 114.96 136.61 120.45 141.79 122.89 142.23 120.43 146.74 121.76 154.85 122.78 148.44 125.32 154.18 128.68 149.10 127.91 152.22 125.52 149.34 127.56 160.94 127.90 176.16 130.75 195.12 133.57 186.07 135.83 200.78 135.26 208.15 135.99 209.56 139.12 203.33 137.64 198.84 138.59 200.63 138.32 206.47 135.99 196.68 136.96 203.81 137.13 190.18 138.67 187.50 143.04 187.62 143.98 168.92 144.09 164.78 144.97 175.98 147.77 174.70 149.73 166.95 153.11 161.76 151.58 149.65 149.04 137.42 154.70 142.60 154.91 146.94 159.08 152.52 168.01 147.47 164.17 146.15 163.77 152.04 163.49 144.42 166.13 138.15 166.15 125.94 170.05 112.61 167.37 111.48 164.80 95.25 169.53 105.38 168.17 109.59 172.45 99.07 177.81 92.07 175.38 89.10 175.64 86.36 178.80 95.39 180.49 95.27 182.71 98.56 185.7 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 32.692139819348 + 1.28981479437032X[t] + 0.144892381334993M1[t] + 1.73658903653866M2[t] -0.550465623628254M3[t] + 0.621836851411566M4[t] + 2.96612775017956M5[t] + 3.54060389137733M6[t] + 6.75112864261287M7[t] + 5.90591649878049M8[t] + 0.100824026717422M9[t] + 3.89912416000683M10[t] + 7.93909788953396M11[t] -2.06872918563815t + 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) 32.692139819348 35.621144 0.9178 0.360424 0.180212 X 1.28981479437032 0.374494 3.4442 0.00077 0.000385 M1 0.144892381334993 11.663962 0.0124 0.990108 0.495054 M2 1.73658903653866 11.903884 0.1459 0.884237 0.442119 M3 -0.550465623628254 11.98989 -0.0459 0.963451 0.481726 M4 0.621836851411566 11.919197 0.0522 0.958472 0.479236 M5 2.96612775017956 11.896975 0.2493 0.803505 0.401753 M6 3.54060389137733 11.895701 0.2976 0.766452 0.383226 M7 6.75112864261287 11.899343 0.5674 0.571445 0.285723 M8 5.90591649878049 11.898229 0.4964 0.620466 0.310233 M9 0.100824026717422 12.016082 0.0084 0.993318 0.496659 M10 3.89912416000683 11.899852 0.3277 0.743691 0.371845 M11 7.93909788953396 11.935595 0.6652 0.507117 0.253558 t -2.06872918563815 0.52147 -3.9671 0.000119 6e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.469536623350642 R-squared 0.220464640667523 Adjusted R-squared 0.143106169894071 F-TEST (value) 2.84990949876923 F-TEST (DF numerator) 13 F-TEST (DF denominator) 131 p-value 0.00122249412706421 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 29.1235780132500 Sum Squared Residuals 111111.946314495 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 159.749782452077 -59.7497824520768 2 103.53 162.652064682893 -59.1220646828931 3 108.36 164.745354808939 -56.3853548089388 4 115.2 158.48329855376 -43.28329855376 5 123.51 158.952332486045 -35.4423324860454 6 132.87 160.669718279587 -27.7997182795871 7 130.55 163.140023083386 -32.5900230833859 8 136.68 162.405868756401 -25.7258687564012 9 140.63 159.833185903562 -19.2031859035620 10 143.47 156.945219887367 -13.4752198873675 11 124.1 158.142575554634 -34.0425755546343 12 111.49 150.817563251752 -39.3275632517525 13 119.93 149.048504222774 -29.1185042227737 14 131.79 153.743629017764 -21.9536290177642 15 136.61 156.468928393052 -19.8589283930522 16 141.79 158.719649780717 -16.9296497807174 17 142.23 155.822267099696 -13.5922670996963 18 146.74 156.043467731768 -9.3034677317684 19 154.85 158.500874387624 -3.65087438762354 20 148.44 158.863062635854 -10.4230626358536 21 154.18 155.323018687237 -1.14301868723665 22 149.1 156.059432243223 -6.95943224322275 23 152.22 154.948019428567 -2.72801942856666 24 149.34 147.57141453391 1.76858546608999 25 160.94 146.086114759693 14.8538852403072 26 176.16 149.285054393214 26.8749456067863 27 195.12 148.566548267533 46.5534517324671 28 186.07 150.585102992211 35.4848970077885 29 200.78 150.125470272550 50.6545297274498 30 208.15 149.5727820280 58.5772179719998 31 209.56 154.751697899977 54.8083021000233 32 203.33 149.928830674838 53.4011693251619 33 198.84 143.280333071789 55.5596669282113 34 200.63 144.66165402496 55.9683459750401 35 206.47 143.627630097966 62.8423699020339 36 196.68 134.870923373333 61.8090766266668 37 203.81 133.166355084073 70.643644915927 38 190.18 134.675637336969 55.5043626630313 39 187.5 135.956344142562 51.543655857438 40 187.62 136.272343338672 51.3476566613283 41 168.92 136.689784679182 32.2302153208177 42 164.78 136.330568653788 28.4494313462122 43 175.98 141.083845643622 34.8961543563779 44 174.7 140.697941311117 34.0020586888826 45 166.95 137.183693658388 29.7663063416121 46 161.76 136.939847970652 24.8201520293475 47 149.65 135.634962936841 14.0150370631591 48 137.42 132.927487597805 4.49251240219524 49 142.6 131.274511900319 11.3254880996806 50 146.94 136.176007062409 10.7639929375909 51 152.52 143.338269330331 9.18173066966907 52 147.47 137.488953809351 9.9810461906494 53 146.15 137.248589604732 8.90141039526766 54 152.04 135.393188417868 16.6468115821317 55 144.42 139.940095040603 4.47990495939671 56 138.15 137.051950007020 1.09804999297984 57 125.94 134.208406047363 -8.26840604736318 58 112.61 132.481273346102 -19.871273346102 59 111.48 131.137693868459 -19.6576938684593 60 95.25 127.230690770659 -31.9806907706587 61 105.38 123.552705846012 -18.1727058460119 62 109.59 128.596080635482 -19.0060806354824 63 99.07 131.153704087502 -32.0837040875022 64 92.07 127.123027426584 -35.0530274265841 65 89.1 127.73394098625 -38.6339409862501 66 86.36 130.31550269202 -43.95550269202 67 95.39 133.637085260103 -38.2470852601032 68 95.27 133.586532774135 -38.3165327741348 69 98.56 129.607951795432 -31.0479517954319 70 101.79 128.035596869495 -26.2455968694951 71 102.02 128.639637731352 -26.6196377313516 72 98.21 122.913995773489 -24.7039957734890 73 104.42 120.809584897974 -16.3895848979739 74 105.62 124.537348597187 -18.9173485971867 75 109.46 124.489546164578 -15.0295461645785 76 110.94 121.955054665130 -11.0150546651299 77 113.09 121.804977496117 -8.71497749611747 78 109.58 124.154372538901 -14.5743725389007 79 111.41 126.934232893348 -15.5242328933483 80 109.83 125.155328582924 -15.3253285829237 81 110.58 121.357321675433 -10.7773216754327 82 109.04 121.822874124601 -12.782874124601 83 107.8 119.950470581266 -12.1504705812665 84 109.79 115.978976743747 -6.18897674374743 85 110.76 114.326001046262 -3.56600104626201 86 112.64 118.453607331730 -5.81360733172959 87 114.17 115.748786422719 -1.57878642271852 88 115.99 117.573868928242 -1.58386892824155 89 119.01 117.901023233146 1.10897676685382 90 117.92 118.444677563811 -0.524677563810919 91 115.92 122.153204570205 -6.23320457020526 92 120.75 122.257429859561 -1.50742985956122 93 124.94 138.761107815459 -13.8211078154590 94 129.17 141.290363935620 -12.1203639356198 95 128.14 138.205534485577 -10.0655344855772 96 134.18 133.266679552280 0.913320447719609 97 131.74 133.832185301112 -2.09218530111194 98 134.32 136.025069395024 -1.70506939502400 99 137.8 140.65929466598 -2.85929466598001 100 141.79 137.028460591317 4.76153940868333 101 142.75 136.710707499036 6.03929250096385 102 144.3 137.293056273532 7.00694372646804 103 145.49 144.445388780895 1.04461121910497 104 138.21 143.066327056725 -4.85632705672518 105 139.02 139.281218297178 -0.261218297177833 106 141.91 137.32191893293 4.58808106706998 107 144.95 133.669570973364 11.2804290266356 108 146.11 130.162410461819 15.9475895381813 109 150.96 125.968499619424 24.9915003805762 110 148.2 129.954226277511 18.2457737224894 111 152.12 138.935127405495 13.1848725945054 112 154.74 132.092654492849 22.6473455071509 113 150.8 131.220281038989 19.5797189610106 114 152.6 135.156148278848 17.4438517211519 115 158.74 137.716740118253 21.0232598817473 116 161.83 136.814909868 25.0150901320001 117 162.4 135.506245513644 26.8937544863564 118 156.11 134.991538719091 21.1184612809095 119 154.93 128.501598211910 26.4284017880898 120 157.18 126.297150642679 30.8828493573214 121 159.85 120.684443526476 39.1655564735237 122 154.4 123.909179455885 30.4908205441154 123 151.57 124.364404793081 27.2055952069192 124 133.34 119.792005918527 13.5479940814729 125 131.2 114.095725133722 17.1042748662776 126 124.17 117.631749787326 6.5382502126738 127 133.19 116.271304651845 16.9186953481548 128 130.94 116.130465130271 14.8095348697291 129 119.58 112.564624885766 7.01537511423354 130 118.55 105.523455231700 13.0265447683004 131 119.96 102.606301704925 17.3536982950749 132 108.42 101.704567078007 6.71543292199257 133 95.93 102.515137637769 -6.58513763776925 134 88.83 104.192095813933 -15.3620958139332 135 84.98 104.853691518229 -19.8736915182287 136 81.61 111.515579502640 -29.9055795026404 137 72.84 112.074900470532 -39.2349004705318 138 74.72 113.224767754550 -38.5047677545505 139 83.4 120.325507670139 -36.9255076701387 140 87.42 119.591353343154 -32.171353343154 141 86.33 121.042892648750 -34.7128926487502 142 94.28 122.346824714259 -28.0668247142592 143 98.81 125.466004425138 -26.6560044251378 144 100.96 121.288140220519 -20.3281402205195 145 99.14 124.446173706035 -25.3061737060353 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00466609395647789 0.00933218791295578 0.995333906043522 18 0.00235012749651757 0.00470025499303514 0.997649872503482 19 0.000397503160361459 0.000795006320722918 0.999602496839639 20 0.000219373660454627 0.000438747320909255 0.999780626339545 21 6.85159217903825e-05 0.000137031843580765 0.99993148407821 22 4.54154331249988e-05 9.08308662499977e-05 0.999954584566875 23 2.27113173819099e-05 4.54226347638197e-05 0.999977288682618 24 4.54853812350786e-05 9.09707624701572e-05 0.999954514618765 25 6.29302029737019e-05 0.000125860405947404 0.999937069797026 26 0.000110823645130869 0.000221647290261738 0.99988917635487 27 0.000280215093412296 0.000560430186824592 0.999719784906588 28 0.000168025484843652 0.000336050969687305 0.999831974515156 29 0.000261543204288888 0.000523086408577776 0.999738456795711 30 0.000246920739119622 0.000493841478239244 0.99975307926088 31 0.000262430796347758 0.000524861592695516 0.999737569203652 32 0.000129309685724667 0.000258619371449334 0.999870690314275 33 8.72186896849832e-05 0.000174437379369966 0.999912781310315 34 4.69367364643406e-05 9.38734729286811e-05 0.999953063263536 35 3.44459612235950e-05 6.889192244719e-05 0.999965554038776 36 2.34915126026657e-05 4.69830252053315e-05 0.999976508487397 37 2.00178196198469e-05 4.00356392396938e-05 0.99997998218038 38 9.22043344337917e-05 0.000184408668867583 0.999907795665566 39 0.000545518394658858 0.00109103678931772 0.999454481605341 40 0.00149441438681749 0.00298882877363497 0.998505585613183 41 0.01714860786237 0.03429721572474 0.98285139213763 42 0.0959213738318002 0.191842747663600 0.9040786261682 43 0.191258915943445 0.38251783188689 0.808741084056555 44 0.306650102276662 0.613300204553324 0.693349897723338 45 0.500459076941334 0.999081846117332 0.499540923058666 46 0.682348767807298 0.635302464385403 0.317651232192702 47 0.815305024771566 0.369389950456869 0.184694975228434 48 0.91515894607564 0.169682107848721 0.0848410539243603 49 0.955060252940817 0.089879494118367 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0.999990325522576 1.93489548476382e-05 9.67447742381908e-06 67 0.999994049823632 1.19003527353767e-05 5.95017636768837e-06 68 0.99999576444737 8.47110526090719e-06 4.23555263045359e-06 69 0.999995197334924 9.60533015255845e-06 4.80266507627922e-06 70 0.999993876198967 1.22476020659492e-05 6.1238010329746e-06 71 0.99999259446481 1.48110703808655e-05 7.40553519043273e-06 72 0.999991090451117 1.78190977662922e-05 8.90954888314612e-06 73 0.999986686617917 2.66267641657596e-05 1.33133820828798e-05 74 0.999982660035133 3.46799297341047e-05 1.73399648670523e-05 75 0.99997516312954 4.96737409182542e-05 2.48368704591271e-05 76 0.999961169741207 7.7660517586249e-05 3.88302587931245e-05 77 0.999937100172971 0.000125799654057645 6.28998270288226e-05 78 0.999903356370831 0.000193287258337537 9.66436291687684e-05 79 0.999860616007487 0.000278767985025181 0.000139383992512590 80 0.999805048192847 0.000389903614305939 0.000194951807152970 81 0.999690540780698 0.000618918438603204 0.000309459219301602 82 0.99953841942285 0.000923161154299814 0.000461580577149907 83 0.999375863161697 0.00124827367660652 0.00062413683830326 84 0.99914825444997 0.00170349110005937 0.000851745550029687 85 0.998817548702945 0.00236490259411082 0.00118245129705541 86 0.998488414074782 0.00302317185043616 0.00151158592521808 87 0.997854179841976 0.00429164031604817 0.00214582015802409 88 0.996984429405176 0.00603114118964812 0.00301557059482406 89 0.99572519656439 0.00854960687121791 0.00427480343560896 90 0.99397432300185 0.0120513539962988 0.00602567699814939 91 0.992790944738787 0.0144181105224258 0.0072090552612129 92 0.991716366903705 0.0165672661925904 0.00828363309629519 93 0.995790857797706 0.00841828440458832 0.00420914220229416 94 0.997242402515649 0.00551519496870281 0.00275759748435140 95 0.998302284300695 0.00339543139861066 0.00169771569930533 96 0.998791478775342 0.00241704244931664 0.00120852122465832 97 0.999280260530135 0.00143947893973079 0.000719739469865396 98 0.999475245862342 0.00104950827531573 0.000524754137657867 99 0.999588711086559 0.000822577826882706 0.000411288913441353 100 0.999538810395198 0.000922379209603499 0.000461189604801749 101 0.999410082839271 0.00117983432145734 0.000589917160728671 102 0.999187183587936 0.00162563282412803 0.000812816412064014 103 0.999192488996213 0.00161502200757341 0.000807511003786706 104 0.99956311311339 0.000873773773219914 0.000436886886609957 105 0.999746636729578 0.000506726540843991 0.000253363270421995 106 0.999877974552133 0.000244050895733151 0.000122025447866575 107 0.99995847418004 8.30516399209289e-05 4.15258199604644e-05 108 0.99999010241836 1.97951632820818e-05 9.8975816410409e-06 109 0.999996443618963 7.11276207496605e-06 3.55638103748303e-06 110 0.99999806328208 3.87343584077805e-06 1.93671792038903e-06 111 0.999999069925442 1.86014911527108e-06 9.3007455763554e-07 112 0.999998039430377 3.92113924660873e-06 1.96056962330437e-06 113 0.999995939902913 8.12019417388826e-06 4.06009708694413e-06 114 0.999990502388005 1.89952239904769e-05 9.49761199523846e-06 115 0.999982480349997 3.50393000055203e-05 1.75196500027602e-05 116 0.999966581676123 6.68366477550678e-05 3.34183238775339e-05 117 0.999917577154765 0.000164845690468883 8.24228452344413e-05 118 0.99993189657231 0.000136206855378252 6.81034276891262e-05 119 0.99997461993141 5.07601371811786e-05 2.53800685905893e-05 120 0.999996781978523 6.43604295389104e-06 3.21802147694552e-06 121 0.999994828481464 1.03430370724634e-05 5.17151853623171e-06 122 0.9999813795936 3.72408127995358e-05 1.86204063997679e-05 123 0.9999454913634 0.000109017273198957 5.45086365994783e-05 124 0.999863844774914 0.000272310450172826 0.000136155225086413 125 0.99944000488547 0.00111999022906019 0.000559995114530093 126 0.998243409326439 0.00351318134712291 0.00175659067356145 127 0.993141138615833 0.0137177227683350 0.00685886138416749 128 0.970080422804408 0.0598391543911847 0.0299195771955923 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 92 0.821428571428571 NOK 5% type I error level 102 0.910714285714286 NOK 10% type I error level 105 0.9375 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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Software written by Ed van Stee & Patrick Wessa

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