Paper - Multiple Regression Model 1

*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, 11 Dec 2010 16:09:46 +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/11/t1292084100k9c0exbdndi8buy.htm/, Retrieved Sat, 11 Dec 2010 17:17:13 +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/11/t1292084100k9c0exbdndi8buy.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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25.94 23688100 39.18 3940.35 0,0274 144.7 5,45 28.66 13741000 35.78 4696.69 0,0322 140.8 5,73 33.95 14143500 42.54 4572.83 0,0376 137.1 5,85 31.01 16763800 27.92 3860.66 0,0307 137.7 6,02 21.00 16634600 25.05 3400.91 0,0319 144.7 6,27 26.19 13693300 32.03 3966.11 0,0373 139.2 6,53 25.41 10545800 27.95 3766.99 0,0366 143.0 6,54 30.47 9409900 27.95 4206.35 0,0341 140.8 6,5 12.88 39182200 24.15 3672.82 0,0345 142.5 6,52 9.78 37005800 27.57 3369.63 0,0345 135.8 6,51 8.25 15818500 22.97 2597.93 0,0345 132.6 6,51 7.44 16952000 17.37 2470.52 0,0339 128.6 6,4 10.81 24563400 24.45 2772.73 0,0373 115.7 5,98 9.12 14163200 23.62 2151.83 0,0353 109.2 5,49 11.03 18184800 21.90 1840.26 0,0292 116.9 5,31 12.74 20810300 27.12 2116.24 0,0327 109.9 4,8 9.98 12843000 27.70 2110.49 0,0362 116.1 4,21 11.62 13866700 29.23 2160.54 0,0325 118.9 3,97 9.40 15119200 26.50 2027.13 0,0272 116.3 3,77 9.27 8301600 22.84 1805.43 0,0272 114.0 3,65 7.76 14039600 20.49 1498.80 0,0265 97.0 3,07 8.78 1213 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Apple[t] = + 23.0147069811754 + 1.24781152025288e-06Volume[t] + 6.92603621715411Microsoft[t] + 0.0288863890196798NASDAQ[t] -605.881541364844Inflatie[t] -2.26245296320921Consumentenvertrouwen[t] + 3.36290179952850Federal_funds_rate[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) 23.0147069811754 29.401198 0.7828 0.43526 0.21763 Volume 1.24781152025288e-06 0 3.288 0.001316 0.000658 Microsoft 6.92603621715411 1.398225 4.9535 2e-06 1e-06 NASDAQ 0.0288863890196798 0.010781 2.6793 0.008388 0.004194 Inflatie -605.881541364844 320.051667 -1.8931 0.060698 0.030349 Consumentenvertrouwen -2.26245296320921 0.264611 -8.5501 0 0 Federal_funds_rate 3.36290179952850 4.016463 0.8373 0.404059 0.20203 Multiple Linear Regression - Regression Statistics Multiple R 0.85090925788459 R-squared 0.724046565153704 Adjusted R-squared 0.71058542199047 F-TEST (value) 53.787895751029 F-TEST (DF numerator) 6 F-TEST (DF denominator) 123 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 41.230775839512 Sum Squared Residuals 209097.155788354 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.94 112.107289813531 -86.167289813531 2 28.66 104.851539835078 -76.191539835078 3 33.95 154.098784512411 -120.148784512411 4 31.01 38.9325600394035 -7.92256003940354 5 21 -10.1104016462637 31.1104016462637 6 26.19 60.935815641033 -34.7458156410331 7 25.41 14.8586681702056 10.5513318297944 8 30.47 32.4903872445281 -2.02038724452809 9 12.88 23.8886487920865 -11.0086487920865 10 9.78 51.2266972107048 -41.4466972107048 11 8.25 -22.1226033354754 30.3726033354754 12 7.44 -54.1310050386215 61.5710050386215 13 10.81 38.8459068386761 -28.0359068386761 14 9.12 16.4541339250055 -7.33413392500552 15 11.03 -13.7709145238127 24.8009145238127 16 12.74 45.6326947477349 -32.8926947477349 17 9.98 21.4101044631145 -11.4301044631144 18 11.62 29.8298852732554 -18.2098852732554 19 9.4 17.0519266840979 -7.65192668409787 20 9.27 -18.4084645373875 27.6784645373875 21 7.76 0.553193800192326 7.20680619980767 22 8.78 50.705773238009 -41.925773238009 23 10.65 72.3251752953616 -61.6751752953616 24 10.95 56.3578358273064 -45.4078358273064 25 12.36 52.2727176696082 -39.9127176696082 26 10.85 38.2892794234369 -27.4392794234369 27 11.84 2.0738761005611 9.7661238994389 28 12.14 -19.2737140272183 31.4137140272183 29 11.65 -27.1269854246633 38.7769854246633 30 8.86 -4.13006140668965 12.9900614066896 31 7.63 -11.8839174968014 19.5139174968014 32 7.38 -10.93612626234 18.31612626234 33 7.25 -24.7883777880585 32.0383777880585 34 8.03 36.5906476378518 -28.5606476378518 35 7.75 35.1653652103447 -27.4153652103447 36 7.16 22.1719225461455 -15.0119225461455 37 7.18 16.4556029585752 -9.27560295857524 38 7.51 43.2052415736729 -35.6952415736729 39 7.07 52.5524889793364 -45.4824889793364 40 7.11 32.5745960284295 -25.4645960284295 41 8.98 32.4132598271319 -23.4332598271319 42 9.53 28.4163724064607 -18.8863724064607 43 10.54 47.6190898599333 -37.0790898599333 44 11.31 38.0705951634602 -26.7605951634602 45 10.36 56.3762006163700 -46.01620061637 46 11.44 45.3650111959884 -33.9250111959884 47 10.45 18.7004367711592 -8.25043677115923 48 10.69 29.8586312706496 -19.1686312706496 49 11.28 29.0726853240445 -17.7926853240445 50 11.96 35.6482024961838 -23.6882024961838 51 13.52 35.9881554657129 -22.4681554657129 52 12.89 23.7105218869591 -10.8205218869591 53 14.03 13.5601293530061 0.469870646993944 54 16.27 11.2737845531828 4.99621544681723 55 16.17 4.97494142581267 11.1950585741873 56 17.25 11.7547561479336 5.49524385206644 57 19.38 21.3824388850511 -2.00243888505107 58 26.2 49.2918057854151 -23.0918057854151 59 33.53 66.1151775542645 -32.5851775542645 60 32.2 41.0096266626957 -8.80962666269573 61 38.45 59.8638894332298 -21.4138894332298 62 44.86 45.9330704932263 -1.07307049322628 63 41.67 20.1197837849420 21.5502162150580 64 36.06 47.3383881719415 -11.2783881719415 65 39.76 33.1141635446581 6.6458364553419 66 36.81 17.1680511039041 19.6419488960959 67 42.65 28.4066372929859 14.2433627070141 68 46.89 28.0083844537446 18.8816155462554 69 53.61 60.7097258028333 -7.09972580283328 70 57.59 81.6255742932287 -24.0355742932287 71 67.82 62.6508344816764 5.16916551832363 72 71.89 39.7251622611629 32.1648377388371 73 75.51 67.2363104386734 8.27368956132659 74 68.49 67.4822451780628 1.00775482193724 75 62.72 61.7864707289808 0.933529271019213 76 70.39 38.6718484505559 31.7181515494441 77 59.77 19.0441821696927 40.7258178303073 78 57.27 23.6195628854509 33.6504371145491 79 67.96 28.1748047712401 39.7851952287599 80 67.85 54.1740020390498 13.6759979609502 81 76.98 69.2458688879707 7.73413111202934 82 81.08 77.2235104385821 3.85648956141791 83 91.66 80.4761811863609 11.1838188136391 84 84.84 77.963203854614 6.876796145386 85 85.73 110.610419358969 -24.880419358969 86 84.61 60.1242439410145 24.4857560589855 87 92.91 62.0695191702572 30.8404808297428 88 99.8 81.8652520403299 17.9347479596701 89 121.19 90.3648957017614 30.8251042982386 90 122.04 103.685347732840 18.3546522671601 91 131.76 88.9177861793445 42.8422138206555 92 138.48 99.3250352198197 39.1549647801803 93 153.47 117.518095468747 35.9519045312526 94 189.95 170.303839046355 19.6461609536450 95 182.22 167.037264092396 15.1827359076045 96 198.08 155.579887886392 42.5001121136085 97 135.36 170.210572672005 -34.8505726720047 98 125.02 137.309333586984 -12.2893335869840 99 143.5 162.695137803889 -19.1951378038885 100 173.95 170.403405502899 3.5465944971014 101 188.75 174.303344567796 14.4466554322042 102 167.44 174.733135116124 -7.29313511612399 103 158.95 155.616268362422 3.33373163757773 104 169.53 141.942972426113 27.5870275738871 105 113.66 150.923166132943 -37.263166132943 106 107.59 196.551523085261 -88.9615230852614 107 92.67 150.733153409489 -58.0631534094887 108 85.35 152.302181993855 -66.9521819938553 109 90.13 212.101808696183 -121.971808696183 110 89.31 146.813987310572 -57.5039873105722 111 105.12 164.206110512103 -59.0861105121028 112 125.83 146.642303239864 -20.8123032398636 113 135.81 121.058253601411 14.7517463985887 114 142.43 160.054954981629 -17.6249549816289 115 163.39 167.191619488664 -3.80161948866433 116 168.21 151.74060750317 16.4693924968301 117 185.35 167.877751433817 17.4722485661826 118 188.5 188.381541243985 0.118458756015183 119 199.91 179.064691576325 20.8453084236754 120 210.73 181.823933068456 28.9060669315444 121 192.06 171.379745875497 20.6802541245032 122 204.62 192.08102740532 12.5389725946801 123 235 184.560880846088 50.4391191539116 124 261.09 186.259247243487 74.8307527565126 125 256.88 153.55716533577 103.322834664230 126 251.53 148.082028962810 103.447971037190 127 257.25 176.765918642905 80.4840813570952 128 243.1 139.224251011955 103.875748988045 129 283.75 170.344051583193 113.405948416807 130 300.98 186.482473148243 114.497526851757 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.00265787514148279 0.00531575028296559 0.997342124858517 11 0.000785950465255726 0.00157190093051145 0.999214049534744 12 9.45105180145493e-05 0.000189021036029099 0.999905489481985 13 1.65499756536042e-05 3.30999513072084e-05 0.999983450024346 14 1.86935219100851e-06 3.73870438201703e-06 0.99999813064781 15 3.60583871968644e-07 7.21167743937287e-07 0.999999639416128 16 4.51245599911661e-08 9.02491199823323e-08 0.99999995487544 17 6.0142966730951e-09 1.20285933461902e-08 0.999999993985703 18 6.65214944748358e-10 1.33042988949672e-09 0.999999999334785 19 1.11340835863573e-10 2.22681671727147e-10 0.99999999988866 20 1.38933705726805e-11 2.77867411453610e-11 0.999999999986107 21 1.46353595036417e-12 2.92707190072834e-12 0.999999999998537 22 2.55232757362905e-13 5.1046551472581e-13 0.999999999999745 23 5.67539247561588e-14 1.13507849512318e-13 0.999999999999943 24 1.12154246086377e-14 2.24308492172753e-14 0.999999999999989 25 1.49975328372772e-15 2.99950656745545e-15 0.999999999999998 26 1.66673847794097e-16 3.33347695588194e-16 1 27 1.54821804800823e-17 3.09643609601646e-17 1 28 2.36151854034228e-18 4.72303708068457e-18 1 29 2.72597295534779e-19 5.45194591069557e-19 1 30 2.90652269543322e-20 5.81304539086644e-20 1 31 4.43332188901885e-21 8.86664377803771e-21 1 32 6.41793414546555e-22 1.28358682909311e-21 1 33 2.61909393137559e-22 5.23818786275119e-22 1 34 3.34591276978108e-23 6.69182553956215e-23 1 35 3.26179967193763e-24 6.52359934387525e-24 1 36 4.48720590291242e-25 8.97441180582483e-25 1 37 1.18226771299603e-25 2.36453542599206e-25 1 38 1.69337869303771e-26 3.38675738607542e-26 1 39 2.01653258482605e-27 4.03306516965210e-27 1 40 3.05942245766981e-28 6.11884491533963e-28 1 41 5.34545332063736e-29 1.06909066412747e-28 1 42 5.83822448945639e-30 1.16764489789128e-29 1 43 5.32259377760811e-31 1.06451875552162e-30 1 44 5.05011635935353e-32 1.01002327187071e-31 1 45 5.14853638251029e-33 1.02970727650206e-32 1 46 8.3454270485772e-34 1.66908540971544e-33 1 47 1.92184844952564e-34 3.84369689905129e-34 1 48 6.70367926551195e-35 1.34073585310239e-34 1 49 1.87011578771349e-35 3.74023157542698e-35 1 50 1.17086367664616e-35 2.34172735329232e-35 1 51 2.42784048076936e-35 4.85568096153871e-35 1 52 1.00715687718142e-35 2.01431375436284e-35 1 53 4.62677177348697e-36 9.25354354697393e-36 1 54 5.29022967865568e-36 1.05804593573114e-35 1 55 2.62044385831133e-36 5.24088771662265e-36 1 56 2.51212896350387e-36 5.02425792700773e-36 1 57 1.40648584224928e-35 2.81297168449856e-35 1 58 7.9365911398017e-31 1.58731822796034e-30 1 59 2.26827449967408e-26 4.53654899934817e-26 1 60 5.94742554854412e-24 1.18948510970882e-23 1 61 3.24116233171389e-22 6.48232466342778e-22 1 62 9.25530905943946e-20 1.85106181188789e-19 1 63 1.29065878887416e-17 2.58131757774832e-17 1 64 1.30666398916899e-17 2.61332797833798e-17 1 65 7.02459226681049e-16 1.40491845336210e-15 1 66 1.9353374864287e-14 3.8706749728574e-14 0.99999999999998 67 3.45197915025787e-12 6.90395830051573e-12 0.999999999996548 68 1.54218957709823e-10 3.08437915419646e-10 0.999999999845781 69 1.88504878956779e-09 3.77009757913558e-09 0.999999998114951 70 7.5323736796553e-09 1.50647473593106e-08 0.999999992467626 71 8.85397858636577e-07 1.77079571727315e-06 0.999999114602141 72 3.43206090658253e-05 6.86412181316506e-05 0.999965679390934 73 0.000104871834537249 0.000209743669074498 0.999895128165463 74 0.000210343744325837 0.000420687488651675 0.999789656255674 75 0.000632173260846302 0.00126434652169260 0.999367826739154 76 0.00170953147452144 0.00341906294904288 0.998290468525479 77 0.00175429191833309 0.00350858383666617 0.998245708081667 78 0.00138637528635595 0.00277275057271190 0.998613624713644 79 0.00294928141268402 0.00589856282536804 0.997050718587316 80 0.00519032002052601 0.0103806400410520 0.994809679979474 81 0.0128818865984262 0.0257637731968524 0.987118113401574 82 0.0272954154724496 0.0545908309448991 0.97270458452755 83 0.0475113645000773 0.0950227290001547 0.952488635499923 84 0.0468405694223227 0.0936811388446455 0.953159430577677 85 0.0427853135858319 0.0855706271716638 0.957214686414168 86 0.0483748480749466 0.0967496961498931 0.951625151925053 87 0.0638226905372858 0.127645381074572 0.936177309462714 88 0.0823292101713035 0.164658420342607 0.917670789828697 89 0.133601097195362 0.267202194390724 0.866398902804638 90 0.145535290315399 0.291070580630797 0.854464709684601 91 0.165017966743053 0.330035933486106 0.834982033256947 92 0.194902107542546 0.389804215085092 0.805097892457454 93 0.280672225573413 0.561344451146826 0.719327774426587 94 0.425361243815584 0.850722487631168 0.574638756184416 95 0.479245282301644 0.958490564603289 0.520754717698356 96 0.913936717206397 0.172126565587206 0.086063282793603 97 0.977461076913231 0.0450778461735379 0.0225389230867690 98 0.9708042052244 0.0583915895512016 0.0291957947756008 99 0.984902099710335 0.0301958005793301 0.0150979002896650 100 0.98781091679096 0.0243781664180796 0.0121890832090398 101 0.989066768792405 0.0218664624151910 0.0109332312075955 102 0.991265287903431 0.0174694241931371 0.00873471209656855 103 0.989008563900015 0.0219828721999697 0.0109914360999849 104 0.986204291938416 0.0275914161231684 0.0137957080615842 105 0.984385603836956 0.0312287923260875 0.0156143961630438 106 0.988859446705986 0.0222811065880270 0.0111405532940135 107 0.986582763682281 0.0268344726354373 0.0134172363177186 108 0.991989946309648 0.0160201073807035 0.00801005369035176 109 0.988400181775845 0.0231996364483095 0.0115998182241548 110 0.979273854348304 0.0414522913033924 0.0207261456516962 111 0.964785809448222 0.0704283811035567 0.0352141905517784 112 0.969031355989144 0.0619372880217129 0.0309686440108564 113 0.976985449199651 0.0460291016006972 0.0230145508003486 114 0.979008663460044 0.0419826730799118 0.0209913365399559 115 0.988008017334569 0.0239839653308622 0.0119919826654311 116 0.9856848715258 0.0286302569484003 0.0143151284742001 117 0.99777706612066 0.00444586775867853 0.00222293387933927 118 0.9994133231524 0.00117335369519848 0.000586676847599239 119 0.997507891716975 0.00498421656604946 0.00249210828302473 120 0.9995171574455 0.000965685109001374 0.000482842554500687 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 74 0.666666666666667 NOK 5% type I error level 93 0.837837837837838 NOK 10% type I error level 101 0.90990990990991 NOK

Charts produced by software:

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Parameters (Session):

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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