Paper MR 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: Sat, 18 Dec 2010 16:44:00 +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/18/t1292690562uoj6xh2j6jpxtrn.htm/, Retrieved Sat, 18 Dec 2010 17:42:53 +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/18/t1292690562uoj6xh2j6jpxtrn.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 «

69 26 9 15 6 25 25 53 20 9 15 6 25 24 43 21 9 14 13 19 21 60 31 14 10 8 18 23 49 21 8 10 7 18 17 62 18 8 12 9 22 19 45 26 11 18 5 29 18 50 22 10 12 8 26 27 75 22 9 14 9 25 23 82 29 15 18 11 23 23 60 15 14 9 8 23 29 59 16 11 11 11 23 21 21 24 14 11 12 24 26 62 17 6 17 8 30 25 54 19 20 8 7 19 25 47 22 9 16 9 24 23 59 31 10 21 12 32 26 37 28 8 24 20 30 20 43 38 11 21 7 29 29 48 26 14 14 8 17 24 79 25 11 7 8 25 23 62 25 16 18 16 26 24 16 29 14 18 10 26 30 38 28 11 13 6 25 22 58 15 11 11 8 23 22 60 18 12 13 9 21 13 67 21 9 13 9 19 24 55 25 7 18 11 35 17 47 23 13 14 12 19 24 59 23 10 12 8 20 21 49 19 9 9 7 21 23 47 18 9 12 8 21 24 57 18 13 8 9 24 24 39 26 16 5 4 23 24 49 18 12 10 8 19 23 26 18 6 11 8 17 26 53 28 14 11 8 24 24 75 17 14 12 6 15 21 65 29 10 12 8 25 23 49 12 4 15 4 27 28 48 25 12 12 7 29 23 45 28 12 16 14 27 22 31 20 14 14 10 18 24 61 17 9 17 9 25 21 49 17 9 13 6 22 23 69 20 10 10 8 26 23 54 31 14 17 11 23 20 80 21 10 12 8 16 23 57 19 9 13 8 27 21 34 23 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 Multiple Linear Regression - Estimated Regression Equation Anxiety[t] = + 58.3998859288407 -0.359270126672522Concern[t] + 0.262562939642511Doubts[t] + 0.882346066972115Pexpectations[t] -1.23270117276060Pcriticism[t] + 0.0505647857106037Standards[t] -0.167665890991060Organization[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) 58.3998859288407 9.185523 6.3578 0 0 Concern -0.359270126672522 0.242166 -1.4836 0.140108 0.070054 Doubts 0.262562939642511 0.444627 0.5905 0.555765 0.277882 Pexpectations 0.882346066972115 0.410304 2.1505 0.033187 0.016594 Pcriticism -1.23270117276060 0.512813 -2.4038 0.017498 0.008749 Standards 0.0505647857106037 0.310606 0.1628 0.870909 0.435454 Organization -0.167665890991060 0.317686 -0.5278 0.59847 0.299235 Multiple Linear Regression - Regression Statistics Multiple R 0.243095200709022 R-squared 0.0590952766077596 Adjusted R-squared 0.0198909131330829 F-TEST (value) 1.50736477703384 F-TEST (DF numerator) 6 F-TEST (DF denominator) 144 p-value 0.17973315446958 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.2487068800742 Sum Squared Residuals 25276.0656951542 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 69 54.3333854281444 14.6666145718556 2 53 56.6566720791707 -3.65667207917067 3 43 46.9857566349114 -3.98575663491143 4 60 46.9540950946205 13.0459049053795 5 49 51.2101152421977 -2.21011524219767 6 62 51.4541427714986 10.5458572285014 7 45 60.1141710608863 -15.1141710608863 8 50 50.635821331768 -0.63582133176802 9 75 51.5253481315628 23.4746518684372 10 82 51.5486872336563 30.4513127663437 11 60 51.0668996370154 8.93310036298456 12 59 49.3278564350063 9.67214356499369 13 21 45.2209183985484 -24.2209183985484 14 62 56.3312414662457 5.6687585337543 15 54 52.0239562950907 1.97604370490928 16 47 53.2394754797964 -6.23947547979641 17 59 50.8837547086767 8.11624529132335 18 37 45.1267338027659 -8.12673380276593 19 43 54.1402405953095 -11.1402405953095 20 48 52.06159931917 -4.06159931916995 21 79 46.028942334786 32.971057665214 22 62 47.0688532823266 14.9311467176734 23 16 51.4968585869687 -35.4968585869687 24 38 52.8782765931134 -14.8782765931134 25 58 53.2175641889696 4.78243581103044 26 60 54.3421711572765 5.65782884272353 27 67 50.5312175860085 16.4687824139915 28 55 52.4980369976799 2.50196300232014 29 47 48.0471716399238 -1.04717163992383 30 59 50.9791578367782 8.02084216322176 31 49 50.4545713793986 -1.45457137939855 32 47 52.0605126432358 -5.06051264323576 33 57 48.5003733182886 8.49962668171144 34 39 49.879804001012 -10.8798040010119 35 49 51.1500456477889 -2.15004564778891 36 26 49.8528868325116 -23.8528868325116 37 53 49.0499743648828 3.95002563511721 38 75 56.3976087723516 18.6023912276484 39 65 48.741029223314 16.258970776686 40 49 60.1138867473164 -11.1138867473164 41 48 52.1381959248921 -4.13819592489212 42 45 46.0273979230087 -1.0273979230087 43 31 51.8023825193945 -20.8023825193945 44 61 56.3040687478239 4.69593125217614 45 49 55.9857618591032 -6.98576185910325 46 69 50.2603330151331 18.7396669848669 47 54 50.1882356466698 3.81176435333024 48 80 51.1601071652987 28.8398928347013 49 57 53.3899749707722 3.61002502922785 50 34 49.6931818994059 -15.6931818994059 51 69 53.6514512085572 15.3485487914428 52 44 49.7296990521499 -5.72969905214989 53 70 49.7227166059001 20.2772833940999 54 51 53.029226641001 -2.02922664100097 55 66 53.2852470051652 12.7147529948348 56 18 47.8241912392161 -29.8241912392161 57 74 51.8444530844945 22.1555469155055 58 59 56.77440574358 2.22559425642002 59 48 53.4865523397392 -5.48655233973921 60 55 50.9043067414718 4.09569325852823 61 44 46.9601349290329 -2.96013492903292 62 56 49.1843849576482 6.81561504235184 63 65 52.4549762409838 12.5450237590162 64 77 49.9870393180124 27.0129606819876 65 46 47.1870874119503 -1.18708741195033 66 70 51.1711100632884 18.8288899367116 67 39 56.7761102910232 -17.7761102910232 68 55 56.9426904827073 -1.94269048270728 69 44 49.8288553505976 -5.82885535059765 70 45 53.5899831019140 -8.58998310191404 71 45 54.7447079724858 -9.74470797248582 72 49 55.6875872054548 -6.68758720545485 73 65 50.0250623921715 14.9749376078284 74 45 47.7042360489941 -2.70423604899415 75 48 52.8254350985071 -4.82543509850708 76 41 47.8504964310968 -6.85049643109675 77 40 51.3124880493052 -11.3124880493052 78 64 54.7083252075465 9.2916747924535 79 56 50.8011147773533 5.19888522264669 80 52 52.5197347620798 -0.519734762079786 81 41 49.5922666441831 -8.59226664418313 82 42 55.2642270326336 -13.2642270326336 83 54 55.3113774646975 -1.31137746469753 84 40 47.8509513548666 -7.85095135486657 85 40 51.019345454055 -11.0193454540550 86 51 52.3396784185856 -1.33967841858559 87 48 57.9266650315225 -9.92666503152247 88 80 60.017195589912 19.982804410088 89 38 54.8922899979350 -16.8922899979350 90 57 54.0319565565153 2.96804344348475 91 28 46.2956567755890 -18.2956567755890 92 51 55.4804675910415 -4.48046759104147 93 46 53.9868305790284 -7.98683057902839 94 58 55.4181252892165 2.58187471078350 95 67 52.3456505154579 14.6543494845421 96 72 50.6227939792013 21.3772060207987 97 26 50.360933596059 -24.360933596059 98 54 55.7898215207578 -1.78982152075781 99 53 50.8833069798584 2.11669302014162 100 64 48.358852228782 15.6411477712180 101 47 49.3607677858263 -2.36076778582625 102 43 56.7196639723006 -13.7196639723006 103 66 48.8709766814946 17.1290233185054 104 54 51.6096187563907 2.39038124360933 105 62 59.5259800115174 2.47401998848256 106 52 52.3149173409705 -0.314917340970525 107 64 55.001338010657 8.99866198934296 108 55 51.8329268201483 3.16707317985171 109 57 52.2412163182036 4.75878368179641 110 74 56.14570073987 17.8542992601300 111 32 50.2802577635798 -18.2802577635798 112 38 54.9809277821275 -16.9809277821275 113 66 53.5847188622457 12.4152811377543 114 37 52.3629910596434 -15.3629910596434 115 26 50.9245186575052 -24.9245186575052 116 64 53.1830421917704 10.8169578082296 117 28 49.5499682906021 -21.5499682906021 118 66 58.079530367138 7.92046963286203 119 65 56.0924374264196 8.90756257358043 120 48 49.2899549814081 -1.28995498140812 121 44 56.3944881870575 -12.3944881870575 122 64 54.3206508413799 9.67934915862008 123 39 51.1415687140846 -12.1415687140846 124 50 54.4623814138904 -4.46238141389043 125 66 53.4972451590971 12.5027548409029 126 48 49.1316650836027 -1.13166508360275 127 70 52.8869393596788 17.1130606403212 128 66 56.4464944950274 9.55350550497262 129 61 51.0434240482184 9.95657595178162 130 31 50.5058076389855 -19.5058076389855 131 61 52.323338268607 8.67666173139299 132 54 46.6521336565823 7.3478663434177 133 34 47.3403340549265 -13.3403340549265 134 62 46.6857937254558 15.3142062745442 135 47 56.2193487974417 -9.21934879744167 136 52 49.4584458651551 2.54155413484487 137 37 56.7643877364826 -19.7643877364826 138 46 48.7562220956256 -2.75622209562558 139 38 49.4904902675992 -11.4904902675992 140 63 53.2175641889696 9.78243581103044 141 34 52.35294682904 -18.3529468290400 142 46 48.8915391456998 -2.89153914569980 143 40 47.0697358359429 -7.06973583594288 144 30 47.8509513548666 -17.8509513548666 145 35 49.74089719584 -14.74089719584 146 51 49.2899549814081 1.71004501859188 147 56 57.0969395421922 -1.09693954219220 148 68 54.8162279809236 13.1837720190764 149 39 52.8344638819052 -13.8344638819052 150 44 51.1500456477889 -7.15004564778891 151 58 52.8344638819052 5.16553611809477 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.911146501905794 0.177706996188412 0.0888534980942058 11 0.83720655469473 0.325586890610540 0.162793445305270 12 0.749575071920995 0.500849856158011 0.250424928079005 13 0.942444672159739 0.115110655680522 0.0575553278402612 14 0.906814221884336 0.186371556231327 0.0931857781156637 15 0.862292453595272 0.275415092809455 0.137707546404728 16 0.870623175136293 0.258753649727414 0.129376824863707 17 0.822365539632849 0.355268920734302 0.177634460367151 18 0.77767950024534 0.444640999509321 0.222320499754660 19 0.821627798213232 0.356744403573537 0.178372201786768 20 0.813289437750062 0.373421124499876 0.186710562249938 21 0.90156970767888 0.196860584642241 0.0984302923211203 22 0.898898444800412 0.202203110399177 0.101101555199588 23 0.97565396992041 0.0486920601591811 0.0243460300795905 24 0.982611083419082 0.0347778331618359 0.0173889165809179 25 0.974332978065764 0.0513340438684727 0.0256670219342364 26 0.96383579272279 0.072328414554419 0.0361642072772095 27 0.965269779588375 0.0694604408232501 0.0347302204116251 28 0.951567706093554 0.096864587812892 0.048432293906446 29 0.934409138082439 0.131181723835123 0.0655908619175613 30 0.914566783823743 0.170866432352515 0.0854332161762574 31 0.897997795282129 0.204004409435743 0.102002204717871 32 0.87668236125301 0.246635277493981 0.123317638746990 33 0.849768372821794 0.300463254356413 0.150231627178206 34 0.857321271506089 0.285357456987822 0.142678728493911 35 0.827057370457543 0.345885259084914 0.172942629542457 36 0.890613457919886 0.218773084160228 0.109386542080114 37 0.862338312675116 0.275323374649768 0.137661687324884 38 0.881493232411485 0.237013535177029 0.118506767588515 39 0.886373377760856 0.227253244478289 0.113626622239144 40 0.870379367894077 0.259241264211847 0.129620632105923 41 0.846213671235132 0.307572657529736 0.153786328764868 42 0.815884730893107 0.368230538213786 0.184115269106893 43 0.85808278706089 0.28383442587822 0.14191721293911 44 0.829185502363844 0.341628995272312 0.170814497636156 45 0.802530096144802 0.394939807710395 0.197469903855198 46 0.821080295928896 0.357839408142207 0.178919704071104 47 0.785734162788818 0.428531674422365 0.214265837211182 48 0.89193740325402 0.21612519349196 0.10806259674598 49 0.866903456836555 0.266193086326889 0.133096543163445 50 0.87040261401892 0.259194771962158 0.129597385981079 51 0.866885253805213 0.266229492389574 0.133114746194787 52 0.865915104516875 0.268169790966249 0.134084895483125 53 0.88784432885794 0.224311342284119 0.112155671142060 54 0.864620049749279 0.270759900501442 0.135379950250721 55 0.857385849113662 0.285228301772675 0.142614150886338 56 0.954459048183343 0.0910819036333147 0.0455409518166574 57 0.970826267966408 0.058347464067183 0.0291737320335915 58 0.962153211719614 0.0756935765607712 0.0378467882803856 59 0.952685926385016 0.094628147229967 0.0473140736149835 60 0.940241852067872 0.119516295864255 0.0597581479321277 61 0.935875156376224 0.128249687247551 0.0641248436237756 62 0.924197158759804 0.151605682480392 0.0758028412401958 63 0.920723480041835 0.158553039916331 0.0792765199581653 64 0.96509051822945 0.0698189635411015 0.0349094817705508 65 0.955467135783528 0.0890657284329438 0.0445328642164719 66 0.96575528021861 0.0684894395627802 0.0342447197813901 67 0.973305077248333 0.053389845503334 0.026694922751667 68 0.965252274232808 0.0694954515343848 0.0347477257671924 69 0.95872413263182 0.0825517347363592 0.0412758673681796 70 0.952317242432603 0.0953655151347941 0.0476827575673971 71 0.947587208512252 0.104825582975496 0.0524127914877478 72 0.938051869695295 0.123896260609411 0.0619481303047053 73 0.943032078809006 0.113935842381988 0.0569679211909942 74 0.930994396854809 0.138011206290383 0.0690056031451913 75 0.91557550147013 0.168848997059739 0.0844244985298694 76 0.900077742189484 0.199844515621032 0.0999222578105162 77 0.895159977999986 0.209680044000027 0.104840022000014 78 0.897277457835477 0.205445084329045 0.102722542164523 79 0.878384279892682 0.243231440214636 0.121615720107318 80 0.852547252156552 0.294905495686895 0.147452747843448 81 0.83327088158632 0.33345823682736 0.16672911841368 82 0.831217865416703 0.337564269166593 0.168782134583297 83 0.799503118164238 0.400993763671523 0.200496881835762 84 0.775849623460614 0.448300753078773 0.224150376539386 85 0.764068805342419 0.471862389315162 0.235931194657581 86 0.734633940367607 0.530732119264785 0.265366059632393 87 0.727582201836136 0.544835596327727 0.272417798163864 88 0.758201827239592 0.483596345520815 0.241798172760408 89 0.795660887061815 0.40867822587637 0.204339112938185 90 0.760043668350047 0.479912663299905 0.239956331649953 91 0.781591487478468 0.436817025043064 0.218408512521532 92 0.748724564231269 0.502550871537463 0.251275435768731 93 0.729449766880204 0.541100466239593 0.270550233119796 94 0.688874845222971 0.622250309554058 0.311125154777029 95 0.684936221871595 0.63012755625681 0.315063778128405 96 0.750923012541848 0.498153974916303 0.249076987458151 97 0.837291488301237 0.325417023397527 0.162708511698763 98 0.804124509224816 0.391750981550368 0.195875490775184 99 0.766572076872158 0.466855846255684 0.233427923127842 100 0.81373069259526 0.372538614809479 0.186269307404739 101 0.779456746124135 0.441086507751729 0.220543253875865 102 0.793941922241918 0.412116155516164 0.206058077758082 103 0.824243111368934 0.351513777262131 0.175756888631066 104 0.794258482465436 0.411483035069128 0.205741517534564 105 0.760700937164353 0.478598125671294 0.239299062835647 106 0.716435953357829 0.567128093284343 0.283564046642171 107 0.685223770775814 0.629552458448372 0.314776229224186 108 0.648575210229906 0.702849579540188 0.351424789770094 109 0.600613382319744 0.798773235360512 0.399386617680256 110 0.64589561002356 0.70820877995288 0.35410438997644 111 0.680343188903277 0.639313622193445 0.319656811096723 112 0.67577452151177 0.64845095697646 0.32422547848823 113 0.653452750028858 0.693094499942284 0.346547249971142 114 0.651292398808089 0.697415202383823 0.348707601191912 115 0.792270887917716 0.415458224164569 0.207729112082284 116 0.77683250105898 0.44633499788204 0.22316749894102 117 0.858275797411942 0.283448405176115 0.141724202588058 118 0.8267790124036 0.346441975192799 0.173220987596399 119 0.800723959415297 0.398552081169406 0.199276040584703 120 0.753331130466252 0.493337739067496 0.246668869533748 121 0.730987531687666 0.538024936624667 0.269012468312334 122 0.705003315471948 0.589993369056104 0.294996684528052 123 0.701791408285096 0.596417183429808 0.298208591714904 124 0.640277923621333 0.719444152757334 0.359722076378667 125 0.630149828883618 0.739700342232764 0.369850171116382 126 0.559812157809727 0.880375684380545 0.440187842190273 127 0.61765765108344 0.764684697833119 0.382342348916560 128 0.58978058456426 0.820438830871481 0.410219415435740 129 0.674169407854343 0.651661184291314 0.325830592145657 130 0.717810714144086 0.564378571711828 0.282189285855914 131 0.727926085536777 0.544147828926445 0.272073914463223 132 0.652356055438803 0.695287889122394 0.347643944561197 133 0.716300088017466 0.567399823965068 0.283699911982534 134 0.65935993826491 0.68128012347018 0.34064006173509 135 0.605867879586721 0.788264240826558 0.394132120413279 136 0.609379409142875 0.78124118171425 0.390620590857125 137 0.620824655768853 0.758350688462293 0.379175344231147 138 0.51354880710378 0.97290238579244 0.48645119289622 139 0.394802115840547 0.789604231681093 0.605197884159453 140 0.360530027510884 0.721060055021768 0.639469972489116 141 0.41848607076154 0.83697214152308 0.58151392923846 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 2 0.0151515151515152 OK 10% type I error level 17 0.128787878787879 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/10vwud1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/10vwud1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/1h5em1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/1h5em1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/2h5em1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/2h5em1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/3h5em1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/3h5em1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/49wvp1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/49wvp1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/59wvp1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/59wvp1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/69wvp1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/69wvp1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/725ua1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/725ua1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/8vwud1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/8vwud1292690628.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/9vwud1292690628.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292690562uoj6xh2j6jpxtrn/9vwud1292690628.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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