Personal Standards (Yt) - Maandelijkse effecten

*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: Mon, 29 Nov 2010 10:25:28 +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/Nov/29/t1291026275im1qe7klihg2ou7.htm/, Retrieved Mon, 29 Nov 2010 11:24:57 +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/Nov/29/t1291026275im1qe7klihg2ou7.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 «

24 25 30 19 22 22 25 23 17 21 19 19 15 16 23 27 22 14 22 23 23 21 19 18 20 23 25 19 24 22 25 26 29 32 25 29 28 17 28 29 26 25 14 25 26 20 18 32 25 25 23 21 20 15 30 24 26 24 22 14 24 24 24 24 19 31 22 27 19 25 20 21 27 23 25 20 21 22 23 25 25 17 19 25 19 20 26 23 27 17 17 19 17 22 21 32 21 21 18 18 23 19 20 21 20 17 18 19 22 15 14 18 24 35 29 21 25 20 22 13 26 17 25 20 19 21 22 24 21 26 24 16 23 18 16 26 19 21 21 22 23 29 21 21 23 27 25 21 10 20 26 24 29 19 24 19 24 22 17

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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation PS[t] = + 21.3846153846154 + 1.54395604395604M1[t] -0.456043956043958M2[t] + 1.40109890109889M3[t] + 0.538461538461531M4[t] -0.153846153846160M5[t] + 0.461538461538454M6[t] + 1.38461538461538M7[t] + 1.99999999999999M8[t] + 1.69230769230768M9[t] + 1.15384615384615M10[t] -0.461538461538467M11[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) 21.3846153846154 1.187911 18.0019 0 0 M1 1.54395604395604 1.649688 0.9359 0.350856 0.175428 M2 -0.456043956043958 1.649688 -0.2764 0.782597 0.391298 M3 1.40109890109889 1.649688 0.8493 0.397089 0.198545 M4 0.538461538461531 1.67996 0.3205 0.749029 0.374515 M5 -0.153846153846160 1.67996 -0.0916 0.927159 0.463579 M6 0.461538461538454 1.67996 0.2747 0.783908 0.391954 M7 1.38461538461538 1.67996 0.8242 0.411164 0.205582 M8 1.99999999999999 1.67996 1.1905 0.235767 0.117884 M9 1.69230769230768 1.67996 1.0073 0.315422 0.157711 M10 1.15384615384615 1.67996 0.6868 0.493272 0.246636 M11 -0.461538461538467 1.67996 -0.2747 0.783908 0.391954 Multiple Linear Regression - Regression Statistics Multiple R 0.200542222804997 R-squared 0.040217183127569 Adjusted R-squared -0.031603299767647 F-TEST (value) 0.559968152626386 F-TEST (DF numerator) 11 F-TEST (DF denominator) 147 p-value 0.858509976817269 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.28307523839994 Sum Squared Residuals 2696.67582417582 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 22.9285714285714 1.07142857142861 2 25 20.9285714285714 4.07142857142857 3 30 22.7857142857143 7.21428571428569 4 19 21.9230769230769 -2.92307692307692 5 22 21.2307692307692 0.769230769230767 6 22 21.8461538461538 0.153846153846157 7 25 22.7692307692308 2.23076923076923 8 23 23.3846153846154 -0.384615384615375 9 17 23.0769230769231 -6.07692307692307 10 21 22.5384615384615 -1.53846153846154 11 19 20.9230769230769 -1.92307692307692 12 19 21.3846153846154 -2.38461538461540 13 15 22.9285714285714 -7.92857142857143 14 16 20.9285714285714 -4.92857142857142 15 23 22.7857142857143 0.214285714285715 16 27 21.9230769230769 5.07692307692308 17 22 21.2307692307692 0.769230769230769 18 14 21.8461538461538 -7.84615384615385 19 22 22.7692307692308 -0.76923076923077 20 23 23.3846153846154 -0.384615384615383 21 23 23.0769230769231 -0.0769230769230741 22 21 22.5384615384615 -1.53846153846154 23 19 20.9230769230769 -1.92307692307692 24 18 21.3846153846154 -3.38461538461539 25 20 22.9285714285714 -2.92857142857143 26 23 20.9285714285714 2.07142857142858 27 25 22.7857142857143 2.21428571428572 28 19 21.9230769230769 -2.92307692307692 29 24 21.2307692307692 2.76923076923077 30 22 21.8461538461538 0.153846153846155 31 25 22.7692307692308 2.23076923076923 32 26 23.3846153846154 2.61538461538462 33 29 23.0769230769231 5.92307692307693 34 32 22.5384615384615 9.46153846153846 35 25 20.9230769230769 4.07692307692308 36 29 21.3846153846154 7.61538461538461 37 28 22.9285714285714 5.07142857142857 38 17 20.9285714285714 -3.92857142857142 39 28 22.7857142857143 5.21428571428572 40 29 21.9230769230769 7.07692307692308 41 26 21.2307692307692 4.76923076923077 42 25 21.8461538461538 3.15384615384615 43 14 22.7692307692308 -8.76923076923077 44 25 23.3846153846154 1.61538461538462 45 26 23.0769230769231 2.92307692307692 46 20 22.5384615384615 -2.53846153846154 47 18 20.9230769230769 -2.92307692307692 48 32 21.3846153846154 10.6153846153846 49 25 22.9285714285714 2.07142857142857 50 25 20.9285714285714 4.07142857142857 51 23 22.7857142857143 0.214285714285715 52 21 21.9230769230769 -0.923076923076921 53 20 21.2307692307692 -1.23076923076923 54 15 21.8461538461538 -6.84615384615385 55 30 22.7692307692308 7.23076923076923 56 24 23.3846153846154 0.615384615384615 57 26 23.0769230769231 2.92307692307692 58 24 22.5384615384615 1.46153846153846 59 22 20.9230769230769 1.07692307692308 60 14 21.3846153846154 -7.38461538461539 61 24 22.9285714285714 1.07142857142857 62 24 20.9285714285714 3.07142857142857 63 24 22.7857142857143 1.21428571428572 64 24 21.9230769230769 2.07692307692308 65 19 21.2307692307692 -2.23076923076923 66 31 21.8461538461538 9.15384615384615 67 22 22.7692307692308 -0.76923076923077 68 27 23.3846153846154 3.61538461538461 69 19 23.0769230769231 -4.07692307692307 70 25 22.5384615384615 2.46153846153846 71 20 20.9230769230769 -0.923076923076922 72 21 21.3846153846154 -0.384615384615392 73 27 22.9285714285714 4.07142857142857 74 23 20.9285714285714 2.07142857142858 75 25 22.7857142857143 2.21428571428572 76 20 21.9230769230769 -1.92307692307692 77 21 21.2307692307692 -0.230769230769232 78 22 21.8461538461538 0.153846153846155 79 23 22.7692307692308 0.230769230769230 80 25 23.3846153846154 1.61538461538462 81 25 23.0769230769231 1.92307692307693 82 17 22.5384615384615 -5.53846153846154 83 19 20.9230769230769 -1.92307692307692 84 25 21.3846153846154 3.61538461538461 85 19 22.9285714285714 -3.92857142857143 86 20 20.9285714285714 -0.928571428571425 87 26 22.7857142857143 3.21428571428572 88 23 21.9230769230769 1.07692307692308 89 27 21.2307692307692 5.76923076923077 90 17 21.8461538461538 -4.84615384615385 91 17 22.7692307692308 -5.76923076923077 92 19 23.3846153846154 -4.38461538461538 93 17 23.0769230769231 -6.07692307692307 94 22 22.5384615384615 -0.538461538461538 95 21 20.9230769230769 0.076923076923077 96 32 21.3846153846154 10.6153846153846 97 21 22.9285714285714 -1.92857142857143 98 21 20.9285714285714 0.0714285714285749 99 18 22.7857142857143 -4.78571428571428 100 18 21.9230769230769 -3.92307692307692 101 23 21.2307692307692 1.76923076923077 102 19 21.8461538461538 -2.84615384615385 103 20 22.7692307692308 -2.76923076923077 104 21 23.3846153846154 -2.38461538461538 105 20 23.0769230769231 -3.07692307692308 106 17 22.5384615384615 -5.53846153846154 107 18 20.9230769230769 -2.92307692307692 108 19 21.3846153846154 -2.38461538461539 109 22 22.9285714285714 -0.928571428571431 110 15 20.9285714285714 -5.92857142857143 111 14 22.7857142857143 -8.7857142857143 112 18 21.9230769230769 -3.92307692307692 113 24 21.2307692307692 2.76923076923077 114 35 21.8461538461538 13.1538461538462 115 29 22.7692307692308 6.23076923076923 116 21 23.3846153846154 -2.38461538461538 117 25 23.0769230769231 1.92307692307693 118 20 22.5384615384615 -2.53846153846154 119 22 20.9230769230769 1.07692307692308 120 13 21.3846153846154 -8.38461538461539 121 26 22.9285714285714 3.07142857142857 122 17 20.9285714285714 -3.92857142857142 123 25 22.7857142857143 2.21428571428572 124 20 21.9230769230769 -1.92307692307692 125 19 21.2307692307692 -2.23076923076923 126 21 21.8461538461538 -0.846153846153846 127 22 22.7692307692308 -0.76923076923077 128 24 23.3846153846154 0.615384615384615 129 21 23.0769230769231 -2.07692307692308 130 26 22.5384615384615 3.46153846153846 131 24 20.9230769230769 3.07692307692308 132 16 21.3846153846154 -5.38461538461539 133 23 22.9285714285714 0.0714285714285696 134 18 20.9285714285714 -2.92857142857142 135 16 22.7857142857143 -6.78571428571429 136 26 21.9230769230769 4.07692307692308 137 19 21.2307692307692 -2.23076923076923 138 21 21.8461538461538 -0.846153846153846 139 21 22.7692307692308 -1.76923076923077 140 22 23.3846153846154 -1.38461538461538 141 23 23.0769230769231 -0.0769230769230741 142 29 22.5384615384615 6.46153846153846 143 21 20.9230769230769 0.076923076923077 144 21 21.3846153846154 -0.384615384615392 145 23 22.9285714285714 0.0714285714285696 146 27 20.9285714285714 6.07142857142857 147 25 22.7857142857143 2.21428571428572 148 21 21.9230769230769 -0.923076923076921 149 10 21.2307692307692 -11.2307692307692 150 20 21.8461538461538 -1.84615384615385 151 26 22.7692307692308 3.23076923076923 152 24 23.3846153846154 0.615384615384615 153 29 23.0769230769231 5.92307692307693 154 19 22.5384615384615 -3.53846153846154 155 24 20.9230769230769 3.07692307692308 156 19 21.3846153846154 -2.38461538461539 157 24 22.9285714285714 1.07142857142857 158 22 20.9285714285714 1.07142857142858 159 17 22.7857142857143 -5.78571428571428 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.864751796938915 0.27049640612217 0.135248203061085 16 0.876431217950371 0.247137564099257 0.123568782049629 17 0.79506818688698 0.409863626226042 0.204931813113021 18 0.829136782556346 0.341726434887308 0.170863217443654 19 0.764523509147891 0.470952981704218 0.235476490852109 20 0.675163692568172 0.649672614863657 0.324836307431828 21 0.656673592590036 0.686652814819927 0.343326407409964 22 0.564902847924592 0.870194304150815 0.435097152075408 23 0.473316258646911 0.946632517293821 0.526683741353089 24 0.391394633611533 0.782789267223067 0.608605366388467 25 0.314853585609012 0.629707171218023 0.685146414390988 26 0.259617639576978 0.519235279153957 0.740382360423022 27 0.20381603189129 0.40763206378258 0.79618396810871 28 0.18384653790972 0.36769307581944 0.81615346209028 29 0.144556319229767 0.289112638459534 0.855443680770233 30 0.128327837249788 0.256655674499576 0.871672162750212 31 0.0964945352096202 0.192989070419240 0.90350546479038 32 0.078611709888819 0.157223419777638 0.921388290111181 33 0.157323120455648 0.314646240911295 0.842676879544352 34 0.360411812413372 0.720823624826744 0.639588187586628 35 0.375996959283984 0.751993918567968 0.624003040716016 36 0.552187412365217 0.895625175269566 0.447812587634783 37 0.63385406055876 0.732291878882481 0.366145939441241 38 0.617622811123999 0.764754377752002 0.382377188876001 39 0.590852600399279 0.818294799201442 0.409147399600721 40 0.660715028671013 0.678569942657973 0.339284971328987 41 0.643117677020397 0.713764645959205 0.356882322979603 42 0.644533530820981 0.710932938358038 0.355466469179019 43 0.783084811697851 0.433830376604297 0.216915188302149 44 0.742370971430964 0.515258057138073 0.257629028569036 45 0.713149342333033 0.573701315333934 0.286850657666967 46 0.696692459302374 0.606615081395252 0.303307540697626 47 0.665974625849455 0.66805074830109 0.334025374150545 48 0.825203411183597 0.349593177632805 0.174796588816403 49 0.801238503056153 0.397522993887695 0.198761496943847 50 0.797323016458846 0.405353967082309 0.202676983541155 51 0.77365767583678 0.452684648326441 0.226342324163221 52 0.739216949871929 0.521566100256143 0.260783050128071 53 0.710063118905153 0.579873762189694 0.289936881094847 54 0.75288985023446 0.49422029953108 0.24711014976554 55 0.82330666181787 0.353386676364261 0.176693338182131 56 0.78856478060516 0.422870438789681 0.211435219394841 57 0.763952998316855 0.47209400336629 0.236047001683145 58 0.726261312125159 0.547477375749682 0.273738687874841 59 0.686364412936928 0.627271174126144 0.313635587063072 60 0.795058755298117 0.409882489403766 0.204941244701883 61 0.760402879879232 0.479194240241536 0.239597120120768 62 0.738293462864896 0.523413074270208 0.261706537135104 63 0.708167134307789 0.583665731384422 0.291832865692211 64 0.673358136658039 0.653283726683921 0.326641863341961 65 0.646408286417026 0.707183427165948 0.353591713582974 66 0.800804603675093 0.398390792649814 0.199195396324907 67 0.76589427475951 0.468211450480979 0.234105725240489 68 0.752141025039457 0.495717949921086 0.247858974960543 69 0.750049383258682 0.499901233482637 0.249950616741318 70 0.722463032397216 0.555073935205569 0.277536967602784 71 0.68163359643706 0.63673280712588 0.31836640356294 72 0.638701703178038 0.722596593643923 0.361298296821962 73 0.632767423035874 0.734465153928252 0.367232576964126 74 0.597012326738653 0.805975346522695 0.402987673261347 75 0.574351944562522 0.851296110874956 0.425648055437478 76 0.538512527074804 0.922974945850391 0.461487472925196 77 0.491717776803143 0.983435553606287 0.508282223196857 78 0.443341627394073 0.886683254788147 0.556658372605927 79 0.395759178033614 0.791518356067227 0.604240821966386 80 0.359590816328295 0.71918163265659 0.640409183671705 81 0.3238020132973 0.6476040265946 0.6761979867027 82 0.354341856412602 0.708683712825205 0.645658143587398 83 0.319106416007349 0.638212832014699 0.680893583992651 84 0.308345311323664 0.616690622647327 0.691654688676336 85 0.300767648357083 0.601535296714167 0.699232351642917 86 0.262616735426485 0.525233470852969 0.737383264573515 87 0.266944981924703 0.533889963849406 0.733055018075297 88 0.233193966323901 0.466387932647802 0.766806033676099 89 0.276038627407287 0.552077254814573 0.723961372592713 90 0.293053485342617 0.586106970685234 0.706946514657383 91 0.331333987553568 0.662667975107135 0.668666012446433 92 0.330410037056704 0.660820074113408 0.669589962943296 93 0.37724561213505 0.7544912242701 0.62275438786495 94 0.331548952030314 0.663097904060628 0.668451047969686 95 0.288623519788016 0.577247039576033 0.711376480211984 96 0.606890963048471 0.786218073903058 0.393109036951529 97 0.570558547558209 0.858882904883583 0.429441452441791 98 0.522719150259515 0.95456169948097 0.477280849740485 99 0.526028907545675 0.94794218490865 0.473971092454325 100 0.510633686222591 0.978732627554817 0.489366313777409 101 0.497213898991802 0.994427797983605 0.502786101008198 102 0.491691449370183 0.983382898740366 0.508308550629817 103 0.47608134417759 0.95216268835518 0.52391865582241 104 0.436676298111581 0.873352596223161 0.56332370188842 105 0.423321737319179 0.846643474638358 0.576678262680821 106 0.467268568395091 0.934537136790181 0.53273143160491 107 0.456857518342823 0.913715036685645 0.543142481657177 108 0.427010952276396 0.854021904552793 0.572989047723604 109 0.381495633962788 0.762991267925576 0.618504366037212 110 0.4225617119994 0.8451234239988 0.5774382880006 111 0.534319128048304 0.931361743903392 0.465680871951696 112 0.523793756923973 0.952412486152055 0.476206243076027 113 0.583081234031432 0.833837531937137 0.416918765968568 114 0.928100484601733 0.143799030796535 0.0718995153982675 115 0.945128037619163 0.109743924761674 0.0548719623808371 116 0.932015456432582 0.135969087134835 0.0679845435674175 117 0.91138555192106 0.177228896157881 0.0886144480789407 118 0.90931157058905 0.181376858821900 0.0906884294109499 119 0.883108321007247 0.233783357985507 0.116891678992753 120 0.915169902631809 0.169660194736382 0.0848300973681909 121 0.89851638087058 0.202967238258840 0.101483619129420 122 0.906400670409298 0.187198659181405 0.0935993295907026 123 0.916340617232858 0.167318765534285 0.0836593827671425 124 0.902050247376216 0.195899505247569 0.0979497526237843 125 0.898065544576503 0.203868910846995 0.101934455423497 126 0.863573329402 0.272853341196 0.136426670598 127 0.824367328486195 0.351265343027609 0.175632671513805 128 0.775277126760681 0.449445746478638 0.224722873239319 129 0.771169727108326 0.457660545783347 0.228830272891674 130 0.724846079890096 0.550307840219808 0.275153920109904 131 0.667218848225922 0.665562303548155 0.332781151774078 132 0.64887029611554 0.70225940776892 0.35112970388446 133 0.57122230409691 0.85755539180618 0.42877769590309 134 0.609975907344255 0.78004818531149 0.390024092655745 135 0.619857593514923 0.760284812970154 0.380142406485077 136 0.595573340527665 0.80885331894467 0.404426659472335 137 0.707254164627692 0.585491670744616 0.292745835372308 138 0.617296062616984 0.765407874766032 0.382703937383016 139 0.585272276821922 0.829455446356156 0.414727723178078 140 0.486055092611856 0.972110185223712 0.513944907388144 141 0.477021115569851 0.954042231139702 0.522978884430149 142 0.730684502424978 0.538630995150045 0.269315497575022 143 0.632504715224611 0.734990569550779 0.367495284775389 144 0.480971305322613 0.961942610645226 0.519028694677387 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

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 = 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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