*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 24 Dec 2008 05:50:55 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/24/t1230123118xp364sn7916tx6i.htm/, Retrieved Wed, 24 Dec 2008 13:51:59 +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/2008/Dec/24/t1230123118xp364sn7916tx6i.htm/}, year = {2008}, } @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 = {2008}, 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 «

1778.8 0 1264.9 0 1749.1 0 1795.6 0 1759 0 1645.1 0 1589.9 0 1712.6 0 1782.5 0 1606.6 0 1882.1 0 1846.9 0 1873.2 0 1368.3 0 1843.5 0 2074.5 0 1848.5 0 1909.3 0 1932.9 0 2119.1 0 2202 0 2260.8 0 2097.1 0 2026.2 0 2475.2 0 1732.3 0 2385.2 0 2362.2 0 2119 0 2260.3 0 2006.5 0 2073.2 0 2207.8 0 2018.9 0 2082.8 0 2314.3 0 2252.7 0 1633.1 0 2161.1 0 1987.9 0 1870.3 0 1984.6 0 1735.9 0 1910 0 2410.1 0 1994.6 0 2152.3 0 2554 0 2754.5 0 1812.3 0 2549.9 0 2558.4 0 2279.2 0 2591.8 0 2442.4 0 2607.7 0 3106.7 0 2447.5 0 3129.5 0 2606.6 0 2964.4 0 2211.6 0 3246.1 0 3141.8 0 3125.9 0 2890.5 0 2554.3 0 2771.1 0 2950 0 2512.1 0 2800 0 2877.2 0 3048.7 0 2082.7 0 2454.8 0 2807.8 0 2627.6 0 2515.9 0 2690.3 0 2770.8 0 2907.7 0 2906.3 0 3104.6 0 2862.1 0 3189.1 0 2071.8 0 2907.7 0 3194.5 0 2722.9 0 2854.8 0 2803 0 2744.9 0 2574.2 0 2740.9 0 2635.9 0 2612.7 0 3094.2 0 2029 0 2931.1 0 2952.2 0 2601.9 0 2874 0 2570.9 0 2849.8 0 3171.5 0 2843.6 0 2831.5 0 3284.4 0 3230.1 0 2412.2 0 3052.7 0 3048.9 0 2819.9 0 2962.7 0 2796.6 0 2857.2 0 3213.1 0 3116.2 0 3340.1 0 3602 0 3626.4 0 2741.6 1 3756.2 1 3140 1 3421.6 1 3243.7 1 3085.2 1 3152.8 1 3543.6 1 2959.3 1 3594.1 1 3207.9 1 3366.7 1 2658.4 1 3340.4 1 3368.4 1 3422.1 1 3268 1 3234.4 1 3365.1 1 3923.6 1 3147.3 1 3447.7 1 3719.8 1 4090.4 1 3386.7 1 3436.8 1 3744.9 1 3325.8 1 3322.1 1 3338.6 1 3464.2 1 3404.1 1 3942 1 3859.9 1 3895.4 1 4472.2 1 3025.5 1 4285.9 1

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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation x[t] = + 1727.19307028029 -0.550357948481514y[t] + 12.2663376908394t + 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) 1727.19307028029 55.613765 31.0569 0 0 y -0.550357948481514 84.8266 -0.0065 0.994832 0.497416 t 12.2663376908394 0.788173 15.563 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.879490983971027 R-squared 0.773504390886325 Adjusted R-squared 0.770600601025894 F-TEST (value) 266.377536965200 F-TEST (DF numerator) 2 F-TEST (DF denominator) 156 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 307.477860150593 Sum Squared Residuals 14748650.9793149 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1778.8 1739.45940797113 39.3405920288723 2 1264.9 1751.72574566196 -486.825745661964 3 1749.1 1763.99208335280 -14.8920833528025 4 1795.6 1776.25842104364 19.3415789563570 5 1759 1788.52475873448 -29.5247587344822 6 1645.1 1800.79109642532 -155.691096425322 7 1589.9 1813.05743411616 -223.157434116161 8 1712.6 1825.323771807 -112.723771807000 9 1782.5 1837.59010949784 -55.0901094978397 10 1606.6 1849.85644718868 -243.256447188679 11 1882.1 1862.12278487952 19.9772151204815 12 1846.9 1874.38912257036 -27.4891225703577 13 1873.2 1886.65546026120 -13.4554602611971 14 1368.3 1898.92179795204 -530.621797952037 15 1843.5 1911.18813564288 -67.6881356428759 16 2074.5 1923.45447333372 151.045526666285 17 1848.5 1935.72081102455 -87.2208110245547 18 1909.3 1947.98714871539 -38.6871487153941 19 1932.9 1960.25348640623 -27.3534864062334 20 2119.1 1972.51982409707 146.580175902927 21 2202 1984.78616178791 217.213838212088 22 2260.8 1997.05249947875 263.747500521248 23 2097.1 2009.31883716959 87.7811628304088 24 2026.2 2021.58517486043 4.61482513956962 25 2475.2 2033.85151255127 441.34848744873 26 1732.3 2046.11785024211 -313.817850242109 27 2385.2 2058.38418793295 326.815812067051 28 2362.2 2070.65052562379 291.549474376212 29 2119 2082.91686331463 36.0831366853727 30 2260.3 2095.18320100547 165.116798994533 31 2006.5 2107.44953869631 -100.949538696306 32 2073.2 2119.71587638715 -46.5158763871456 33 2207.8 2131.98221407798 75.8177859220152 34 2018.9 2144.24855176882 -125.348551768824 35 2082.8 2156.51488945966 -73.7148894596635 36 2314.3 2168.78122715050 145.518772849497 37 2252.7 2181.04756484134 71.6524351586574 38 1633.1 2193.31390253218 -560.213902532182 39 2161.1 2205.58024022302 -44.4802402230213 40 1987.9 2217.84657791386 -229.946577913860 41 1870.3 2230.1129156047 -359.8129156047 42 1984.6 2242.37925329554 -257.779253295539 43 1735.9 2254.64559098638 -518.745590986379 44 1910 2266.91192867722 -356.911928677218 45 2410.1 2279.17826636806 130.921733631942 46 1994.6 2291.44460405890 -296.844604058897 47 2152.3 2303.71094174974 -151.410941749736 48 2554 2315.97727944058 238.022720559424 49 2754.5 2328.24361713141 426.256382868585 50 1812.3 2340.50995482225 -528.209954822254 51 2549.9 2352.77629251309 197.123707486906 52 2558.4 2365.04263020393 193.357369796067 53 2279.2 2377.30896789477 -98.1089678947727 54 2591.8 2389.57530558561 202.224694414388 55 2442.4 2401.84164327645 40.5583567235488 56 2607.7 2414.10798096729 193.592019032709 57 3106.7 2426.37431865813 680.32568134187 58 2447.5 2438.64065634897 8.85934365103057 59 3129.5 2450.90699403981 678.593005960191 60 2606.6 2463.17333173065 143.426668269352 61 2964.4 2475.43966942149 488.960330578512 62 2211.6 2487.70600711233 -276.106007112327 63 3246.1 2499.97234480317 746.127655196834 64 3141.8 2512.23868249401 629.561317505994 65 3125.9 2524.50502018485 601.394979815155 66 2890.5 2536.77135787568 353.728642124316 67 2554.3 2549.03769556652 5.26230443347628 68 2771.1 2561.30403325736 209.795966742637 69 2950 2573.57037094820 376.429629051797 70 2512.1 2585.83670863904 -73.7367086390421 71 2800 2598.10304632988 201.896953670119 72 2877.2 2610.36938402072 266.830615979279 73 3048.7 2622.63572171156 426.06427828844 74 2082.7 2634.9020594024 -552.2020594024 75 2454.8 2647.16839709324 -192.368397093239 76 2807.8 2659.43473478408 148.365265215922 77 2627.6 2671.70107247492 -44.1010724749178 78 2515.9 2683.96741016576 -168.067410165757 79 2690.3 2696.23374785660 -5.9337478565963 80 2770.8 2708.50008554744 62.2999144525643 81 2907.7 2720.76642323828 186.933576761725 82 2906.3 2733.03276092911 173.267239070885 83 3104.6 2745.29909861995 359.300901380046 84 2862.1 2757.56543631079 104.534563689207 85 3189.1 2769.83177400163 419.268225998367 86 2071.8 2782.09811169247 -710.298111692472 87 2907.7 2794.36444938331 113.335550616688 88 3194.5 2806.63078707415 387.869212925849 89 2722.9 2818.89712476499 -95.9971247649902 90 2854.8 2831.16346245583 23.6365375441705 91 2803 2843.42980014667 -40.429800146669 92 2744.9 2855.69613783751 -110.796137837508 93 2574.2 2867.96247552835 -293.762475528348 94 2740.9 2880.22881321919 -139.328813219187 95 2635.9 2892.49515091003 -256.595150910026 96 2612.7 2904.76148860087 -292.061488600866 97 3094.2 2917.02782629171 177.172173708295 98 2029 2929.29416398254 -900.294163982545 99 2931.1 2941.56050167338 -10.4605016733842 100 2952.2 2953.82683936422 -1.62683936422369 101 2601.9 2966.09317705506 -364.193177055063 102 2874 2978.3595147459 -104.359514745902 103 2570.9 2990.62585243674 -419.725852436742 104 2849.8 3002.89219012758 -153.092190127581 105 3171.5 3015.15852781842 156.341472181580 106 2843.6 3027.42486550926 -183.824865509260 107 2831.5 3039.6912032001 -208.191203200099 108 3284.4 3051.95754089094 232.442459109062 109 3230.1 3064.22387858178 165.876121418222 110 2412.2 3076.49021627262 -664.290216272617 111 3052.7 3088.75655396346 -36.0565539634569 112 3048.9 3101.02289165430 -52.122891654296 113 2819.9 3113.28922934514 -293.389229345135 114 2962.7 3125.55556703597 -162.855567035975 115 2796.6 3137.82190472681 -341.221904726814 116 2857.2 3150.08824241765 -292.888242417654 117 3213.1 3162.35458010849 50.745419891507 118 3116.2 3174.62091779933 -58.4209177993325 119 3340.1 3186.88725549017 153.212744509828 120 3602 3199.15359318101 402.846406818989 121 3626.4 3211.41993087185 414.980069128149 122 2741.6 3223.13591061421 -481.535910614208 123 3756.2 3235.40224830505 520.797751694952 124 3140 3247.66858599589 -107.668585995887 125 3421.6 3259.93492368673 161.665076313274 126 3243.7 3272.20126137757 -28.5012613775659 127 3085.2 3284.46759906840 -199.267599068405 128 3152.8 3296.73393675924 -143.933936759244 129 3543.6 3309.00027445008 234.599725549916 130 2959.3 3321.26661214092 -361.966612140923 131 3594.1 3333.53294983176 260.567050168237 132 3207.9 3345.7992875226 -137.899287522602 133 3366.7 3358.06562521344 8.6343747865584 134 2658.4 3370.33196290428 -711.931962904281 135 3340.4 3382.59830059512 -42.1983005951201 136 3368.4 3394.86463828596 -26.4646382859595 137 3422.1 3407.1309759768 14.9690240232010 138 3268 3419.39731366764 -151.397313667638 139 3234.4 3431.66365135848 -197.263651358478 140 3365.1 3443.92998904932 -78.8299890493173 141 3923.6 3456.19632674016 467.403673259843 142 3147.3 3468.46266443100 -321.162664430996 143 3447.7 3480.72900212184 -33.0290021218355 144 3719.8 3492.99533981267 226.804660187326 145 4090.4 3505.26167750351 585.138322496486 146 3386.7 3517.52801519435 -130.828015194354 147 3436.8 3529.79435288519 -92.9943528851927 148 3744.9 3542.06069057603 202.839309423968 149 3325.8 3554.32702826687 -228.527028266871 150 3322.1 3566.59336595771 -244.493365957711 151 3338.6 3578.85970364855 -240.259703648550 152 3464.2 3591.12604133939 -126.92604133939 153 3404.1 3603.39237903023 -199.292379030229 154 3942 3615.65871672107 326.341283278932 155 3859.9 3627.92505441191 231.974945588092 156 3895.4 3640.19139210275 255.208607897253 157 4472.2 3652.45772979359 819.742270206413 158 3025.5 3664.72406748443 -639.224067484426 159 4285.9 3676.99040517527 608.909594824734 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.432353971309647 0.864707942619294 0.567646028690353 7 0.295186128263676 0.590372256527352 0.704813871736324 8 0.174740284236581 0.349480568473162 0.825259715763419 9 0.099680737443638 0.199361474887276 0.900319262556362 10 0.0615078542371236 0.123015708474247 0.938492145762876 11 0.0405055926907192 0.0810111853814383 0.95949440730928 12 0.0211643538176496 0.0423287076352991 0.97883564618235 13 0.0104975100181520 0.0209950200363039 0.989502489981848 14 0.0440625352117399 0.0881250704234798 0.95593746478826 15 0.027538767774096 0.055077535548192 0.972461232225904 16 0.0301359608980456 0.0602719217960912 0.969864039101954 17 0.0174563684129887 0.0349127368259774 0.982543631587011 18 0.00989160255289067 0.0197832051057813 0.99010839744711 19 0.0054413544661951 0.0108827089323902 0.994558645533805 20 0.00419358575380474 0.00838717150760948 0.995806414246195 21 0.00359959520001872 0.00719919040003743 0.996400404799981 22 0.0030651325364088 0.0061302650728176 0.996934867463591 23 0.00163452366887737 0.00326904733775474 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0.598515528915216 0.700742235542392 92 0.27762409012327 0.55524818024654 0.72237590987673 93 0.27636829509776 0.55273659019552 0.72363170490224 94 0.253210464349447 0.506420928698894 0.746789535650553 95 0.24133571518598 0.48267143037196 0.75866428481402 96 0.233223884525388 0.466447769050776 0.766776115474612 97 0.230929158960071 0.461858317920143 0.769070841039929 98 0.507900179419977 0.984199641160045 0.492099820580023 99 0.468594514014802 0.937189028029604 0.531405485985198 100 0.430393597096817 0.860787194193633 0.569606402903183 101 0.428573771740855 0.85714754348171 0.571426228259145 102 0.38615425666254 0.77230851332508 0.61384574333746 103 0.401648433763394 0.803296867526789 0.598351566236606 104 0.361182879785543 0.722365759571085 0.638817120214457 105 0.340251784586628 0.680503569173256 0.659748215413372 106 0.304613503107272 0.609227006214543 0.695386496892728 107 0.273601046158436 0.547202092316873 0.726398953841564 108 0.269071237479048 0.538142474958096 0.730928762520952 109 0.253027105024206 0.506054210048412 0.746972894975794 110 0.374817385086525 0.74963477017305 0.625182614913475 111 0.328641689639271 0.657283379278542 0.671358310360729 112 0.28478280004808 0.56956560009616 0.71521719995192 113 0.273733909926085 0.547467819852169 0.726266090073915 114 0.243503244185448 0.487006488370896 0.756496755814552 115 0.260209445608578 0.520418891217156 0.739790554391422 116 0.281303320253831 0.562606640507662 0.718696679746169 117 0.246474151958924 0.492948303917847 0.753525848041076 118 0.236237074342647 0.472474148685293 0.763762925657353 119 0.212823481306866 0.425646962613732 0.787176518693134 120 0.195161649808765 0.390323299617531 0.804838350191235 121 0.177549316820611 0.355098633641222 0.822450683179389 122 0.181223694751897 0.362447389503794 0.818776305248103 123 0.314012999754147 0.628025999508294 0.685987000245853 124 0.267687417463416 0.535374834926832 0.732312582536584 125 0.25565283796216 0.51130567592432 0.74434716203784 126 0.217324901970165 0.434649803940331 0.782675098029835 127 0.181537303110016 0.363074606220033 0.818462696889984 128 0.146900705569923 0.293801411139846 0.853099294430077 129 0.155282491910552 0.310564983821104 0.844717508089448 130 0.140495935411717 0.280991870823434 0.859504064588283 131 0.156706070223049 0.313412140446097 0.843293929776951 132 0.124054412257120 0.248108824514241 0.87594558774288 133 0.102165521452976 0.204331042905952 0.897834478547024 134 0.183846567174957 0.367693134349914 0.816153432825043 135 0.144592255547112 0.289184511094223 0.855407744452888 136 0.111316790926801 0.222633581853601 0.8886832090732 137 0.0849210498740584 0.169842099748117 0.915078950125942 138 0.062963175243568 0.125926350487136 0.937036824756432 139 0.0480132867210323 0.0960265734420646 0.951986713278968 140 0.0335513797096289 0.0671027594192578 0.966448620290371 141 0.052822689974764 0.105645379949528 0.947177310025236 142 0.0456295932569002 0.0912591865138004 0.9543704067431 143 0.0303190826523743 0.0606381653047486 0.969680917347626 144 0.0243048525539140 0.0486097051078281 0.975695147446086 145 0.08708946940999 0.17417893881998 0.91291053059001 146 0.0592293222359251 0.118458644471850 0.940770677764075 147 0.0388127473377591 0.0776254946755181 0.96118725266224 148 0.0479433947063447 0.0958867894126894 0.952056605293655 149 0.0288456993575514 0.0576913987151027 0.971154300642449 150 0.0160130436499206 0.0320260872998413 0.98398695635008 151 0.00860701991969922 0.0172140398393984 0.9913929800803 152 0.00409651225715876 0.00819302451431753 0.995903487742841 153 0.00325855264357854 0.00651710528715708 0.996741447356421 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.162162162162162 NOK 5% type I error level 48 0.324324324324324 NOK 10% type I error level 63 0.425675675675676 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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') }

Copyright

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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