*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 03 Dec 2010 10:07:03 +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/03/t12913709328g35fe7584jimu8.htm/, Retrieved Fri, 03 Dec 2010 11:09:04 +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/03/t12913709328g35fe7584jimu8.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1 13 13 14 14 13 3 3 25 25 55 55 147 147 0 12 0 8 0 13 5 0 158 0 7 0 71 0 1 10 10 12 12 16 6 6 0 0 0 1 9 9 7 7 12 6 6 143 143 10 10 0 1 10 10 10 10 11 5 5 67 67 74 74 43 43 1 12 12 7 7 12 3 3 0 0 0 1 13 13 16 16 18 8 8 148 148 138 138 8 8 1 12 12 11 11 11 4 4 28 28 0 0 1 12 12 14 14 14 4 4 114 114 113 113 34 34 0 6 0 6 0 9 4 0 0 0 0 0 5 0 16 0 14 6 0 123 0 115 0 103 0 0 12 0 11 0 12 6 0 145 0 9 0 0 1 11 11 16 16 11 5 5 113 113 114 114 73 73 0 14 0 12 0 12 4 0 152 0 59 0 159 0 0 14 0 7 0 13 6 0 0 0 0 0 12 0 13 0 11 4 0 36 0 114 0 113 0 1 12 12 11 11 12 6 6 0 0 0 1 11 11 15 15 16 6 6 8 8 102 102 44 44 0 11 0 7 0 9 4 0 108 0 0 0 0 7 0 9 0 11 4 0 112 0 86 0 0 0 9 0 7 0 13 2 0 51 0 17 0 41 0 1 11 11 14 14 15 7 7 43 43 45 45 74 74 0 11 0 15 0 10 5 0 120 0 123 0 0 0 12 0 7 0 11 4 0 13 0 24 0 0 0 12 0 15 0 13 6 0 55 0 5 0 0 0 11 0 17 0 16 6 0 103 0 123 0 32 0 0 11 0 15 0 15 7 0 127 0 136 0 126 0 1 8 8 14 14 14 5 5 14 14 4 4 154 154 0 9 0 14 0 14 6 0 135 0 76 0 129 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Liked[t] = + 10.9076439084437 -0.0539084263550218gender[t] + 0.0942832024265008FindingFriends[t] -0.239150405757677FF_G[t] + 0.306793741609170KnowingPeople[t] + 0.0479727705095403KP_G[t] + 0.434063277964381Celebrity[t] + 0.175313638246559C_G[t] -0.162231908269386FBF[t] + 0.140103616364116FBF_G[t] -0.0138083217008717SBF[t] + 0.0259449406246356SBF_G[t] + 0.0539522434431308TBF[t] -0.171883276901893`TBF_G `[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) 10.9076439084437 5.714597 1.9087 0.058314 0.029157 gender -0.0539084263550218 0.068714 -0.7845 0.434031 0.217016 FindingFriends 0.0942832024265008 0.065061 1.4491 0.149501 0.074751 FF_G -0.239150405757677 0.068283 -3.5024 0.000617 0.000308 KnowingPeople 0.306793741609170 0.065369 4.6933 6e-06 3e-06 KP_G 0.0479727705095403 0.070032 0.685 0.494456 0.247228 Celebrity 0.434063277964381 0.063486 6.8371 0 0 C_G 0.175313638246559 0.075068 2.3354 0.020924 0.010462 FBF -0.162231908269386 0.074656 -2.1731 0.031434 0.015717 FBF_G 0.140103616364116 0.084742 1.6533 0.100479 0.050239 SBF -0.0138083217008717 0.071324 -0.1936 0.846765 0.423383 SBF_G 0.0259449406246356 0.084646 0.3065 0.759666 0.379833 TBF 0.0539522434431308 0.073948 0.7296 0.466835 0.233418 `TBF_G ` -0.171883276901893 0.072502 -2.3708 0.019095 0.009548 Multiple Linear Regression - Regression Statistics Multiple R 0.72593515474096 R-squared 0.526981848888782 Adjusted R-squared 0.483677370265924 F-TEST (value) 12.1692228066826 F-TEST (DF numerator) 13 F-TEST (DF denominator) 142 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 30.5403940626352 Sum Squared Residuals 132445.625069148 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 -1.45623141818493 14.4562314181849 2 13 -5.23498181374992 18.2349818137499 3 16 16.2830887309628 -0.283088730962812 4 143 71.7172583432004 71.2827416567996 5 67 58.8422173714636 8.1577826285364 6 0 11.5425192736505 -11.5425192736505 7 8 41.4155338144981 -33.4155338144981 8 1 -5.8299659167488 6.8299659167488 9 0 2.06464382494511 -2.06464382494511 10 16 18.4286965290779 -2.42869652907785 11 11 8.65033664689065 2.34966335310935 12 16 28.1858409077172 -12.1858409077172 13 0 5.60697039539112 -5.60697039539112 14 0 12.5274527415966 -12.5274527415966 15 0 9.2628008121568 -9.2628008121568 16 6 11.1412060005519 -5.14120600055185 17 102 57.0898626004807 44.9101373995193 18 0 42.5793682918182 -42.5793682918182 19 0 50.8553611323748 -50.8553611323748 20 0 7.96939416550511 -7.96939416550511 21 74 23.7969093001764 50.2030906998236 22 0 -23.186325560602 23.186325560602 23 12 20.5060158122785 -8.50601581227855 24 0 -1.67952503105283 1.67952503105283 25 0 -0.570515477658293 0.570515477658293 26 8 30.8004373737074 -22.8004373737074 27 0 -16.1000853616950 16.1000853616950 28 0 9.37698757142566 -9.37698757142566 29 10 9.05056832349069 0.949431676509306 30 10 -6.24224265621931 16.2422426562193 31 12 4.09332892461398 7.90667107538602 32 8 11.4700314761331 -3.47003147613315 33 11 18.7616405864576 -7.76164058645758 34 16 15.6465587574373 0.353441242562654 35 10 22.1939388338959 -12.1939388338959 36 8 6.97576971744538 1.02423028255462 37 14 9.32796492682256 4.67203507317744 38 16 16.6951366360267 -0.695136636026657 39 13 13.7182089494011 -0.718208949401064 40 5 10.2716212593308 -5.27162125933084 41 12 -9.39135237262212 21.3913523726221 42 4 16.6473074666009 -12.6473074666009 43 127 49.7870651928142 77.2129348071858 44 76 36.5584309954533 39.4415690045467 45 25 35.5521462206598 -10.5521462206598 46 0 10.6070553094009 -10.6070553094009 47 111 100.141791750524 10.8582082494760 48 0 37.5832940338728 -37.5832940338728 49 0 2.07822619829041 -2.07822619829041 50 51 97.5274131348986 -46.5274131348986 51 53 106.231846568909 -53.2318465689087 52 131 18.6614706720989 112.338529327901 53 11 15.8749104633964 -4.87491046339637 54 9 15.9768764533434 -6.9768764533434 55 9 13.9627838796735 -4.96278387967353 56 11 30.6795719977376 -19.6795719977376 57 0 4.88594333092586 -4.88594333092586 58 12 26.0340806920860 -14.0340806920860 59 14 8.50412674023324 5.49587325976676 60 12 16.8937572617192 -4.89375726171916 61 15 18.8519562062100 -3.85195620621003 62 12 -2.27409247089255 14.2740924708926 63 13 12.7716863774422 0.228313622557798 64 12 17.9714297500092 -5.97142975000919 65 12 10.8348448246611 1.16515517533886 66 0 57.0337272609345 -57.0337272609345 67 2 80.2129704944614 -78.2129704944614 68 36 41.290091438925 -5.29009143892499 69 36 42.0219758280602 -6.02197582806017 70 141 84.7768653709318 56.2231346290682 71 0 13.7360696884561 -13.7360696884561 72 37 14.3519187932181 22.6480812067819 73 0 14.4706962574182 -14.4706962574182 74 10 30.9643674627582 -20.9643674627582 75 0 54.3239372287093 -54.3239372287093 76 0 23.6593405449579 -23.6593405449579 77 0 14.1468443770239 -14.1468443770239 78 58 18.876547755578 39.123452244422 79 4 40.7381380512141 -36.7381380512141 80 91 85.420838622764 5.5791613772361 81 132 94.8123249439454 37.1876750560546 82 0 52.9449868010472 -52.9449868010472 83 0 12.2671642865290 -12.2671642865290 84 7 13.7207336818016 -6.72073368180163 85 11 19.0880713803169 -8.08807138031689 86 11 17.5118336357838 -6.51183363578381 87 11 11.0701850748028 -0.0701850748028354 88 0 -3.36799736615727 3.36799736615727 89 12 33.5193050927784 -21.5193050927784 90 10 20.205681104634 -10.2056811046340 91 0 -13.3229477310733 13.3229477310733 92 8 21.4032501415980 -13.4032501415980 93 13 13.8920224653129 -0.89202246531288 94 21 17.5645537563069 3.43544624369305 95 118 70.4810407238269 47.5189592761731 96 39 80.2578239020356 -41.2578239020356 97 63 55.6325238956821 7.36747610431785 98 78 70.425544166167 7.57445583383294 99 26 19.1594916633541 6.84050833664593 100 50 54.591463459504 -4.59146345950401 101 104 116.392455188913 -12.3924551889129 102 0 59.2400392627704 -59.2400392627704 103 0 5.71723967964059 -5.71723967964059 104 122 107.995188098880 14.0048119011202 105 149 85.881136508902 63.1188634910979 106 17 29.8093921189283 -12.8093921189283 107 91 79.4594650494405 11.5405349505595 108 111 58.6118839661732 52.3881160338268 109 99 69.0053931319316 29.9946068680684 110 40 28.2283005101187 11.7716994898813 111 132 59.5481439179453 72.4518560820547 112 123 60.9126144201834 62.0873855798166 113 54 47.9695267545053 6.03047324549466 114 90 39.4819066277776 50.5180933722224 115 86 38.7106852476309 47.2893147523691 116 152 97.7351443936914 54.2648556063086 117 152 125.393665394004 26.6063346059965 118 123 49.7981491662396 73.2018508337604 119 100 93.9331090448942 6.0668909551058 120 116 96.8675073172337 19.1324926827663 121 59 71.1309121679744 -12.1309121679744 122 12 20.5647370157687 -8.56473701576867 123 12 13.1403891508116 -1.14038915081164 124 8 20.5231556414531 -12.5231556414531 125 8 25.2447438763270 -17.2447438763270 126 14 21.5019145705868 -7.50191457058685 127 12 25.3388208463015 -13.3388208463015 128 0 18.3962483587954 -18.3962483587954 129 6 13.5774206049397 -7.57742060493974 130 0 33.5505461039532 -33.5505461039532 131 0 80.5421866652787 -80.5421866652787 132 93 70.751363289338 22.248636710662 133 0 9.16523906799077 -9.16523906799077 134 0 50.9031810564828 -50.9031810564828 135 0 -19.8188601785399 19.8188601785399 136 17 29.3499923500707 -12.3499923500707 137 0 -16.0547236333581 16.0547236333581 138 0 -5.56983624424273 5.56983624424273 139 1 14.7210575963478 -13.7210575963478 140 1 39.7952241566575 -38.7952241566575 141 0 28.1516177636635 -28.1516177636635 142 0 -4.66113860607162 4.66113860607162 143 11 6.57425555557049 4.42574444442951 144 12 -12.9206603306079 24.9206603306079 145 11 -26.5691046302022 37.5691046302022 146 0 1.89367081705365 -1.89367081705365 147 0 -3.25986343406242 3.25986343406242 148 12 17.4291204682336 -5.42912046823356 149 14 14.1461602963893 -0.146160296389311 150 11 13.1594271374496 -2.15942713744955 151 14 13.8292889666508 0.170711033349214 152 8 20.1881295214837 -12.1881295214837 153 12 18.0977848718506 -6.09778487185056 154 6 16.3933329657405 -10.3933329657405 155 5 50.2384699413155 -45.2384699413155 156 3 11.4830987067367 -8.4830987067367 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0368207209240702 0.0736414418481404 0.96317927907593 18 0.0486957145658643 0.0973914291317287 0.951304285434136 19 0.0179633921593415 0.0359267843186831 0.982036607840658 20 0.0071819397871367 0.0143638795742734 0.992818060212863 21 0.197794284405120 0.395588568810239 0.80220571559488 22 0.134382444346067 0.268764888692134 0.865617555653933 23 0.0819845653764255 0.163969130752851 0.918015434623574 24 0.0479999906800724 0.095999981360145 0.952000009319928 25 0.0271411740750152 0.0542823481500304 0.972858825924985 26 0.0154676340236894 0.0309352680473788 0.98453236597631 27 0.022869088376855 0.04573817675371 0.977130911623145 28 0.0128125149680664 0.0256250299361328 0.987187485031934 29 0.0074742423248124 0.0149484846496248 0.992525757675188 30 0.00428257014556094 0.00856514029112188 0.99571742985444 31 0.00337479701741827 0.00674959403483654 0.996625202982582 32 0.00215618608549602 0.00431237217099203 0.997843813914504 33 0.00109689252984151 0.00219378505968303 0.998903107470158 34 0.000541755056305739 0.00108351011261148 0.999458244943694 35 0.000274319872425151 0.000548639744850302 0.999725680127575 36 0.000131136540709278 0.000262273081418556 0.99986886345929 37 7.40280417108423e-05 0.000148056083421685 0.99992597195829 38 3.35438891355874e-05 6.70877782711749e-05 0.999966456110864 39 1.63966523136878e-05 3.27933046273757e-05 0.999983603347686 40 7.27345541745506e-06 1.45469108349101e-05 0.999992726544582 41 7.57175240829552e-06 1.51435048165910e-05 0.999992428247592 42 3.54765497799009e-06 7.09530995598019e-06 0.999996452345022 43 0.00238555569427737 0.00477111138855474 0.997614444305723 44 0.00190827121196919 0.00381654242393837 0.99809172878803 45 0.00146112507354777 0.00292225014709554 0.998538874926452 46 0.000883663170947149 0.00176732634189430 0.999116336829053 47 0.00200024467316507 0.00400048934633015 0.997999755326835 48 0.0173332378540361 0.0346664757080722 0.982666762145964 49 0.0120045515541600 0.0240091031083200 0.98799544844584 50 0.0551703565135198 0.110340713027040 0.94482964348648 51 0.0705769768846935 0.141153953769387 0.929423023115307 52 0.438858422586507 0.877716845173014 0.561141577413493 53 0.390470618469224 0.780941236938448 0.609529381530776 54 0.411635721298793 0.823271442597585 0.588364278701208 55 0.371353285753005 0.74270657150601 0.628646714246995 56 0.354591387431292 0.709182774862585 0.645408612568708 57 0.312286659630951 0.624573319261903 0.687713340369049 58 0.271587780880132 0.543175561760263 0.728412219119868 59 0.232385339626618 0.464770679253235 0.767614660373382 60 0.194434455792125 0.388868911584250 0.805565544207875 61 0.162116592688948 0.324233185377895 0.837883407311052 62 0.137450230561803 0.274900461123606 0.862549769438197 63 0.110732886103022 0.221465772206043 0.889267113896978 64 0.0881994201151212 0.176398840230242 0.911800579884879 65 0.0698918069574376 0.139783613914875 0.930108193042562 66 0.103626125132293 0.207252250264586 0.896373874867707 67 0.2641544410716 0.5283088821432 0.7358455589284 68 0.227864783828235 0.45572956765647 0.772135216171765 69 0.194223702413528 0.388447404827057 0.805776297586472 70 0.356148854694813 0.712297709389627 0.643851145305187 71 0.321937515839481 0.643875031678961 0.67806248416052 72 0.303049209878394 0.606098419756789 0.696950790121606 73 0.307873729820884 0.615747459641768 0.692126270179116 74 0.27738570505699 0.55477141011398 0.72261429494301 75 0.391282255569949 0.782564511139898 0.608717744430051 76 0.407141805155265 0.814283610310531 0.592858194844735 77 0.371313876603369 0.742627753206739 0.62868612339663 78 0.415548682228345 0.83109736445669 0.584451317771655 79 0.405955955625502 0.811911911251004 0.594044044374498 80 0.396958165526277 0.793916331052554 0.603041834473723 81 0.466846188233985 0.93369237646797 0.533153811766015 82 0.527858552198168 0.944282895603664 0.472141447801832 83 0.49813211000485 0.9962642200097 0.50186788999515 84 0.449723284817512 0.899446569635025 0.550276715182488 85 0.403366236194806 0.806732472389612 0.596633763805194 86 0.364024017685623 0.728048035371246 0.635975982314377 87 0.319256865158642 0.638513730317284 0.680743134841358 88 0.275649420374853 0.551298840749706 0.724350579625147 89 0.255104749495487 0.510209498990974 0.744895250504513 90 0.228089409038668 0.456178818077336 0.771910590961332 91 0.192387365010974 0.384774730021949 0.807612634989026 92 0.161603877122162 0.323207754244324 0.838396122877838 93 0.132366671824109 0.264733343648217 0.867633328175891 94 0.107403632401691 0.214807264803383 0.892596367598309 95 0.131809029063984 0.263618058127969 0.868190970936016 96 0.141734415514454 0.283468831028908 0.858265584485546 97 0.114684587758512 0.229369175517024 0.885315412241488 98 0.0930797372139033 0.186159474427807 0.906920262786097 99 0.075727601974108 0.151455203948216 0.924272398025892 100 0.05969615936175 0.1193923187235 0.94030384063825 101 0.0522553694066388 0.104510738813278 0.947744630593361 102 0.0960228104281153 0.192045620856231 0.903977189571885 103 0.0779938582258538 0.155987716451708 0.922006141774146 104 0.0732052232918423 0.146410446583685 0.926794776708158 105 0.157451986923239 0.314903973846479 0.84254801307676 106 0.13437647444688 0.26875294889376 0.86562352555312 107 0.113499720552726 0.226999441105451 0.886500279447274 108 0.161383279760884 0.322766559521768 0.838616720239116 109 0.147043476496657 0.294086952993313 0.852956523503343 110 0.122170955906622 0.244341911813245 0.877829044093378 111 0.247411176136255 0.49482235227251 0.752588823863745 112 0.372626595652876 0.745253191305751 0.627373404347124 113 0.318654227860676 0.637308455721352 0.681345772139324 114 0.389586338907093 0.779172677814187 0.610413661092907 115 0.513525373263644 0.972949253472712 0.486474626736356 116 0.695708436047174 0.608583127905651 0.304291563952826 117 0.736051746547485 0.527896506905029 0.263948253452515 118 0.982221078396045 0.0355578432079092 0.0177789216039546 119 0.98349364331553 0.03301271336894 0.01650635668447 120 0.999686280478192 0.000627439043616126 0.000313719521808063 121 0.9999918808912 1.62382175990573e-05 8.11910879952863e-06 122 0.999981988643163 3.60227136735071e-05 1.80113568367535e-05 123 0.99995550800732 8.89839853612628e-05 4.44919926806314e-05 124 0.999897936358055 0.000204127283889688 0.000102063641944844 125 0.999837885101874 0.000324229796252259 0.000162114898126129 126 0.999656023883102 0.000687952233796648 0.000343976116898324 127 0.99932122091268 0.00135755817463896 0.000678779087319482 128 0.998818929787244 0.00236214042551298 0.00118107021275649 129 0.997471148963396 0.00505770207320722 0.00252885103660361 130 0.998475890769088 0.00304821846182453 0.00152410923091227 131 0.999979052981592 4.18940368161043e-05 2.09470184080521e-05 132 0.99999997684531 4.63093783613257e-08 2.31546891806629e-08 133 0.999999808737812 3.82524376720981e-07 1.91262188360490e-07 134 0.999999052110464 1.8957790712539e-06 9.4788953562695e-07 135 0.99999614033055 7.71933889966302e-06 3.85966944983151e-06 136 0.9999987342436 2.53151279910016e-06 1.26575639955008e-06 137 0.999987120642577 2.57587148469673e-05 1.28793574234836e-05 138 0.999888605253461 0.000222789493077047 0.000111394746538524 139 0.998707379651552 0.00258524069689637 0.00129262034844818 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 38 0.308943089430894 NOK 5% type I error level 48 0.390243902439024 NOK 10% type I error level 52 0.422764227642276 NOK

Charts produced by software:

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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