Happiness

*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: Tue, 23 Nov 2010 20:06:26 +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/23/t1290542891wlut1f03yqmb0jh.htm/, Retrieved Tue, 23 Nov 2010 21:08:22 +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/23/t1290542891wlut1f03yqmb0jh.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:

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14 23 26 9 15 6 11 13 4 18 21 20 9 15 6 12 16 4 11 21 21 9 14 13 15 19 6 12 21 31 14 10 8 10 15 8 16 24 21 8 10 7 12 14 8 18 22 18 8 12 9 11 13 4 14 21 26 11 18 5 5 19 4 14 22 22 10 12 8 16 15 5 15 21 22 9 14 9 11 14 5 15 20 29 15 18 11 15 15 8 17 22 15 14 9 8 12 16 4 19 21 16 11 11 11 9 16 4 10 21 24 14 11 12 11 16 4 18 23 17 6 17 8 15 17 4 14 22 19 20 8 7 12 15 4 14 23 22 9 16 9 16 15 8 17 22 31 10 21 12 14 20 4 14 24 28 8 24 20 11 18 4 16 23 38 11 21 7 10 16 4 18 21 26 14 14 8 7 16 4 14 23 25 11 7 8 11 19 8 12 23 25 16 18 16 10 16 3 17 21 29 14 18 10 11 17 4 9 20 28 11 13 6 16 17 4 16 32 15 11 11 8 14 16 4 14 22 18 12 13 9 12 15 10 11 21 21 9 13 9 12 14 5 16 21 25 7 18 11 11 15 4 13 21 23 13 14 12 6 12 4 17 22 23 10 12 8 14 14 4 15 21 19 9 9 7 9 16 4 14 21 18 9 12 8 15 14 4 16 21 18 13 8 9 12 7 10 9 22 26 16 5 4 12 10 4 15 21 18 12 10 8 9 14 8 17 21 18 6 11 8 13 16 4 13 21 28 14 11 8 15 16 4 15 21 17 14 12 6 11 16 4 16 23 29 10 12 8 10 14 7 16 21 12 4 15 4 13 20 4 12 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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 16.0606034156061 + 0.0191423359391710Age[t] -0.0115621244172054Concern_over_mistakes[t] -0.250443166783529Doubts_about_actions[t] + 0.0879285077749663Parental_expectations[t] -0.0909414328317329Parental_criticism[t] + 0.0351776347057642Popularity[t] + 0.0420782212752515Perceived_learning_competence[t] -0.143892749023934Amotivation[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) 16.0606034156061 2.475415 6.488 0 0 Age 0.0191423359391710 0.062121 0.3081 0.758447 0.379224 Concern_over_mistakes -0.0115621244172054 0.038417 -0.301 0.763903 0.381952 Doubts_about_actions -0.250443166783529 0.077967 -3.2122 0.001647 0.000824 Parental_expectations 0.0879285077749663 0.069261 1.2695 0.206435 0.103218 Parental_criticism -0.0909414328317329 0.086108 -1.0561 0.292796 0.146398 Popularity 0.0351776347057642 0.063569 0.5534 0.580919 0.29046 Perceived_learning_competence 0.0420782212752515 0.089299 0.4712 0.638253 0.319126 Amotivation -0.143892749023934 0.07338 -1.9609 0.051947 0.025974 Multiple Linear Regression - Regression Statistics Multiple R 0.419576907453149 R-squared 0.176044781267948 Adjusted R-squared 0.127217805343086 F-TEST (value) 3.60548196838682 F-TEST (DF numerator) 8 F-TEST (DF denominator) 135 p-value 0.000793486487427386 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.22706924744676 Sum Squared Residuals 669.578053444616 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 15.0779522881879 -1.07795228818793 2 18 15.2704526613444 2.72954733865561 3 11 14.4783540692253 -3.47835406922526 4 12 12.5815235675167 -0.581523567516666 5 16 14.3764493011754 1.62355069882458 6 18 14.8651402925499 3.13485970745012 7 14 14.9349117583161 -0.934911758316131 8 14 14.5250987612011 -0.525098761201125 9 15 14.6233487799596 0.376651220040392 10 15 12.9415542508617 2.05844574913827 11 17 13.3857358731387 3.61426412686132 12 19 13.9038607260703 5.09613927392967 13 10 13.0394480669619 -3.03944806696189 14 18 16.2363384821039 1.76366151789606 15 14 11.7977630785502 2.20223692144981 16 14 14.6237786151202 -0.623778615120154 17 17 15.1325590660827 1.86744093391730 18 14 15.0529811587830 -1.05298115878296 19 16 14.9660071045525 1.03399289544749 20 18 13.5031645339563 4.49683546604375 21 14 13.3802154827307 0.619784517269298 22 12 12.9257332182720 -0.925733218271973 23 17 13.8210980862393 3.17890191376065 24 9 14.6648587410489 -5.66485874104889 25 16 14.5747010178424 1.42529898215757 26 14 13.2072737163034 0.792726283696607 27 11 14.5821600313076 -3.58216003130761 28 16 15.4453508760106 0.554649123989363 29 13 13.2210378228577 -0.221037822857698 30 17 14.5449958951211 2.45500410487893 31 15 14.5579694021628 0.442030597837232 32 14 14.8692849827572 -0.869284982757222 33 16 12.1614199045038 3.83858009549615 34 9 12.5172485435579 -3.51724854355791 35 15 13.1554616625264 1.84453833747362 36 17 15.5464871484718 1.45351285152818 37 13 13.4976758394431 -0.497675839443059 38 15 13.7539600426477 1.24603995735231 39 16 13.9223766986622 2.07762330133785 40 16 17.0005388760699 -1.00053887606991 41 12 13.8818619789282 -1.88186197892816 42 11 13.4296137885327 -2.42961378853269 43 15 15.236448799787 -0.236448799787013 44 17 14.8927094544706 2.10729054552944 45 13 14.3111454372712 -1.31114543727118 46 16 11.7608150818851 4.23918491811492 47 14 14.1940192507578 -0.194019250757793 48 11 13.4641094884327 -2.46410948843266 49 12 13.1752845451896 -1.17528454518956 50 12 13.9607429899927 -1.96074298999265 51 15 14.0216717679516 0.978328232048416 52 16 15.1279918472786 0.872008152721424 53 15 14.8167127993799 0.183287200620092 54 12 14.9747072136804 -2.97470721368041 55 12 14.4392776828147 -2.43927768281471 56 8 12.8347214829339 -4.83472148293395 57 13 15.3946704295832 -2.39467042958323 58 11 14.9065615455410 -3.90656154554104 59 14 14.6883104129871 -0.688310412987106 60 15 13.1756825446938 1.82431745530620 61 10 14.0598900072451 -4.05989000724508 62 11 12.7631232913734 -1.76312329137341 63 12 13.8717481205393 -1.87174812053932 64 15 13.1660194281387 1.83398057186127 65 15 14.5159213494338 0.484078650566199 66 14 13.7741067656847 0.225893234315285 67 16 13.2161996996528 2.78380030034716 68 15 14.9578968909679 0.0421031090321294 69 15 15.2697013986737 -0.269701398673676 70 13 14.848550480332 -1.84855048033201 71 17 14.8661384548628 2.13386154513725 72 13 13.4158267459841 -0.415826745984057 73 15 13.7863826161484 1.21361738385159 74 13 14.6005177994864 -1.60051779948635 75 15 14.0976568569614 0.902343143038571 76 16 14.5589597349070 1.44104026509302 77 15 14.5831975450757 0.416802454924332 78 16 14.2395514470958 1.76044855290416 79 15 14.3568511216736 0.643148878326449 80 14 14.7749855796484 -0.77498557964838 81 15 12.3345092456483 2.66549075435175 82 7 11.6855997390837 -4.6855997390837 83 17 15.0084355883792 1.99156441162081 84 13 14.5452660368803 -1.54526603688032 85 15 13.6603984349183 1.33960156508174 86 14 13.8786437840147 0.121356215985340 87 13 13.7439434803704 -0.7439434803704 88 16 15.2920855029073 0.707914497092714 89 12 14.176131274987 -2.17613127498700 90 14 15.5628880163819 -1.56288801638194 91 17 14.7194050239523 2.28059497604773 92 15 15.5194923230004 -0.519492323000411 93 17 13.260543871222 3.73945612877799 94 12 13.9681810482396 -1.96818104823960 95 16 15.3973361909104 0.602663809089637 96 11 14.1190627659778 -3.11906276597783 97 15 13.2691464766065 1.73085352339346 98 9 14.0890899483988 -5.08908994839878 99 16 14.7571298656708 1.2428701343292 100 10 13.1132570563521 -3.11325705635209 101 10 13.2577107599210 -3.25771075992098 102 15 14.9073124673474 0.092687532652553 103 11 13.9705819710362 -2.97058197103620 104 13 15.2290907699082 -2.22909076990817 105 14 13.2448114667880 0.755188533212036 106 18 14.6506184828741 3.34938151712589 107 16 15.1155946172844 0.884405382715643 108 14 12.3346016721198 1.66539832788022 109 14 13.1782142881395 0.821785711860506 110 14 13.2237871928536 0.776212807146425 111 14 14.348938141212 -0.348938141211985 112 12 13.7332718180091 -1.73327181800905 113 14 14.5052954315628 -0.505295431562779 114 15 14.3465288033002 0.653471196699786 115 15 14.9997548705920 0.000245129408036393 116 13 13.6097461096947 -0.609746109694739 117 17 14.9031351886011 2.09686481139894 118 17 15.382167334217 1.617832665783 119 19 14.9767898207555 4.02321017924451 120 15 13.8842209859511 1.11577901404888 121 13 14.0475514263170 -1.04755142631702 122 9 12.2024559719639 -3.20245597196392 123 15 15.0654718210993 -0.0654718210993186 124 15 14.5895060100700 0.41049398992995 125 16 13.8901066620519 2.10989333794814 126 11 13.3104871668195 -2.31048716681952 127 14 14.6190363894438 -0.619036389443828 128 11 12.6696218413593 -1.66962184135928 129 15 13.4092875684140 1.59071243158604 130 13 13.4213314060550 -0.42133140605503 131 16 12.7207767437889 3.27922325621113 132 14 14.7040927994066 -0.704092799406585 133 15 14.8253465477149 0.174653452285116 134 16 13.0549500241579 2.94504997584208 135 16 15.4706574542867 0.529342545713309 136 11 12.4370137923298 -1.43701379232980 137 13 14.0417290804259 -1.04172908042588 138 16 14.5747010178424 1.42529898215757 139 12 14.0188756070208 -2.01887560702079 140 9 11.4175931514257 -2.41759315142568 141 13 11.487566079174 1.51243392082600 142 13 14.5452660368803 -1.54526603688032 143 19 14.9767898207555 4.02321017924451 144 13 15.5205134149569 -2.52051341495688 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.290779630480904 0.581559260961807 0.709220369519096 13 0.187883584731516 0.375767169463032 0.812116415268484 14 0.178308138533829 0.356616277067657 0.821691861466171 15 0.137998000379250 0.275996000758501 0.86200199962075 16 0.172672998386777 0.345345996773554 0.827327001613223 17 0.833921665080817 0.332156669838367 0.166078334919183 18 0.78720934746902 0.42558130506196 0.21279065253098 19 0.767747430857905 0.464505138284191 0.232252569142095 20 0.812335050671468 0.375329898657064 0.187664949328532 21 0.793618405077571 0.412763189844858 0.206381594922429 22 0.77602215990357 0.447955680192860 0.223977840096430 23 0.785059837315354 0.429880325369293 0.214940162684646 24 0.899159647310014 0.201680705379972 0.100840352689986 25 0.871442408276538 0.257115183446923 0.128557591723462 26 0.867671896553391 0.264656206893218 0.132328103446609 27 0.926044574458823 0.147910851082353 0.0739554255411766 28 0.900855846579912 0.198288306840176 0.0991441534200878 29 0.889384898392732 0.221230203214535 0.110615101607268 30 0.903464642311989 0.193070715376022 0.096535357688011 31 0.87368218817357 0.252635623652859 0.126317811826429 32 0.844166316558119 0.311667366883762 0.155833683441881 33 0.853896575813262 0.292206848373475 0.146103424186738 34 0.88243044231252 0.235139115374960 0.117569557687480 35 0.86137384480167 0.277252310396658 0.138626155198329 36 0.846342096531941 0.307315806936118 0.153657903468059 37 0.818210419789273 0.363579160421453 0.181789580210727 38 0.793236079327734 0.413527841344531 0.206763920672266 39 0.78888020981107 0.422239580377858 0.211119790188929 40 0.786233596071662 0.427532807856676 0.213766403928338 41 0.755380524419074 0.489238951161853 0.244619475580926 42 0.79525994990422 0.409480100191559 0.204740050095779 43 0.75664160795373 0.486716784092539 0.243358392046270 44 0.737028264568997 0.525943470862006 0.262971735431003 45 0.69779075109785 0.604418497804299 0.302209248902150 46 0.734685860979064 0.530628278041873 0.265314139020936 47 0.70392694310056 0.592146113798879 0.296073056899439 48 0.831832538776803 0.336334922446394 0.168167461223197 49 0.815440532873867 0.369118934252266 0.184559467126133 50 0.824826953440399 0.350346093119202 0.175173046559601 51 0.801102589058004 0.397794821883992 0.198897410941996 52 0.777298063539023 0.445403872921953 0.222701936460977 53 0.736969264762087 0.526061470475826 0.263030735237913 54 0.767724868934725 0.46455026213055 0.232275131065275 55 0.768054159159622 0.463891681680756 0.231945840840378 56 0.894447369972184 0.211105260055631 0.105552630027816 57 0.907516675603997 0.184966648792005 0.0924833243960027 58 0.94309961367395 0.113800772652101 0.0569003863260505 59 0.929361607554557 0.141276784890887 0.0706383924454433 60 0.925721379648822 0.148557240702357 0.0742786203511783 61 0.957111209959232 0.0857775800815369 0.0428887900407684 62 0.96157264993067 0.076854700138658 0.038427350069329 63 0.957980887083947 0.084038225832106 0.042019112916053 64 0.954350181743478 0.0912996365130441 0.0456498182565221 65 0.94179529360297 0.116409412794060 0.0582047063970298 66 0.925607145125994 0.148785709748013 0.0743928548740063 67 0.936231371199482 0.127537257601037 0.0637686288005185 68 0.922136576227811 0.155726847544378 0.0778634237721892 69 0.90225578111051 0.195488437778979 0.0977442188894894 70 0.89721661610838 0.205566767783239 0.102783383891619 71 0.894608666453212 0.210782667093576 0.105391333546788 72 0.872113217040825 0.25577356591835 0.127886782959175 73 0.855108018488725 0.289783963022549 0.144891981511275 74 0.848412425981181 0.303175148037639 0.151587574018819 75 0.822979148585482 0.354041702829036 0.177020851414518 76 0.811357720532735 0.37728455893453 0.188642279467265 77 0.776285841062497 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0.290920760150902 95 0.675801376398512 0.648397247202977 0.324198623601488 96 0.707526800681546 0.584946398636909 0.292473199318454 97 0.717231637419171 0.565536725161658 0.282768362580829 98 0.904924042353916 0.190151915292168 0.0950759576460841 99 0.890213909749804 0.219572180500393 0.109786090250196 100 0.887117254402918 0.225765491194164 0.112882745597082 101 0.897056404431083 0.205887191137834 0.102943595568917 102 0.867970066720104 0.264059866559792 0.132029933279896 103 0.891725614212195 0.216548771575611 0.108274385787805 104 0.920178500783419 0.159642998433161 0.0798214992165806 105 0.913696786878292 0.172606426243416 0.0863032131217082 106 0.914464581653407 0.171070836693186 0.0855354183465928 107 0.897732152060325 0.204535695879351 0.102267847939675 108 0.923986132112931 0.152027735774137 0.0760138678870685 109 0.905852174447105 0.188295651105790 0.0941478255528952 110 0.907774169491522 0.184451661016957 0.0922258305084784 111 0.88103082835466 0.237938343290681 0.118969171645341 112 0.868523311464361 0.262953377071277 0.131476688535639 113 0.833666691429483 0.332666617141035 0.166333308570517 114 0.787234413894714 0.425531172210572 0.212765586105286 115 0.749509330426372 0.500981339147256 0.250490669573628 116 0.689184159122917 0.621631681754167 0.310815840877083 117 0.74280699126987 0.514386017460261 0.257193008730130 118 0.763351025980554 0.473297948038892 0.236648974019446 119 0.769998729828486 0.460002540343028 0.230001270171514 120 0.77688592130833 0.44622815738334 0.22311407869167 121 0.791061730743556 0.417876538512889 0.208938269256444 122 0.748624681643359 0.502750636713283 0.251375318356641 123 0.693839973144876 0.612320053710249 0.306160026855124 124 0.615922410368186 0.768155179263627 0.384077589631814 125 0.543402695802292 0.913194608395416 0.456597304197708 126 0.458501767774394 0.917003535548787 0.541498232225606 127 0.374299950852689 0.748599901705378 0.625700049147311 128 0.31636802311401 0.63273604622802 0.68363197688599 129 0.248029179099755 0.496058358199511 0.751970820900244 130 0.285723457089042 0.571446914178084 0.714276542910958 131 0.759825364923038 0.480349270153925 0.240174635076962 132 0.695616816753318 0.608766366493364 0.304383183246682 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 4 0.0330578512396694 OK

Charts produced by software:

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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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Software written by Ed van Stee & Patrick Wessa

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