iraq en bel20

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 23 Nov 2008 05:49:18 -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/Nov/23/t1227445016q1u7slmp0b9hwh9.htm/, Retrieved Sun, 23 Nov 2008 12:56:56 +0000

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/Nov/23/t1227445016q1u7slmp0b9hwh9.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

3045.78 3110.52 3013.24 2987.1 2995.55 2833.18 2848.96 2794.83 2845.26 2915.02 2892.63 2604.42 2641.65 2659.81 2638.53 2720.25 2745.88 2735.7 2811.7 2799.43 2555.28 2304.98 2214.95 2065.81 1940.49 2042 1995.37 1946.81 1765.9 1635.25 1833.42 1910.43 1959.67 1969.6 2061.41 2093.48 2120.88 2174.56 2196.72 2350.44 2440.25 2408.64 2472.81 2407.6 2454.62 2448.05 2497.84 2645.64 2756.76 2849.27 2921.44 2981.85 3080.58 3106.22 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12 3701.61 3862.27 3970.1 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98 4591.27 4696.96 4621.4 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68 3844.49 3720.98 3674.4 3857.62 3801.06 3504.37 3032.6 3047.03 2962.34

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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation bel20[t] = + 2111.55555263158 + 19.9465672137819t + 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) 2111.55555263158 117.479618 17.9738 0 0 t 19.9465672137819 2.103166 9.4841 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.699274346948648 R-squared 0.488984612300459 Adjusted R-squared 0.483548278388762 F-TEST (value) 89.9474940728568 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 2.33146835171283e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 571.039688639098 Sum Squared Residuals 30652114.6440976 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3045.78 2131.50211984536 914.277880154644 2 3110.52 2151.44868705914 959.071312940857 3 3013.24 2171.39525427292 841.844745727075 4 2987.1 2191.34182148671 795.758178513293 5 2995.55 2211.28838870049 784.261611299512 6 2833.18 2231.23495591427 601.94504408573 7 2848.96 2251.18152312805 597.778476871948 8 2794.83 2271.12809034183 523.701909658166 9 2845.26 2291.07465755562 554.185342444384 10 2915.02 2311.0212247694 603.998775230602 11 2892.63 2330.96779198318 561.66220801682 12 2604.42 2350.91435919696 253.505640803039 13 2641.65 2370.86092641074 270.789073589257 14 2659.81 2390.80749362453 269.002506375475 15 2638.53 2410.75406083831 227.775939161693 16 2720.25 2430.70062805209 289.549371947911 17 2745.88 2450.64719526587 295.232804734129 18 2735.7 2470.59376247965 265.106237520347 19 2811.7 2490.54032969343 321.159670306565 20 2799.43 2510.48689690722 288.943103092783 21 2555.28 2530.433464121 24.8465358790017 22 2304.98 2550.38003133478 -245.400031334780 23 2214.95 2570.32659854856 -355.376598548563 24 2065.81 2590.27316576234 -524.463165762344 25 1940.49 2610.21973297613 -669.729732976126 26 2042 2630.16630018991 -588.166300189908 27 1995.37 2650.11286740369 -654.74286740369 28 1946.81 2670.05943461747 -723.249434617472 29 1765.9 2690.00600183125 -924.106001831253 30 1635.25 2709.95256904504 -1074.70256904504 31 1833.42 2729.89913625882 -896.479136258817 32 1910.43 2749.8457034726 -839.415703472599 33 1959.67 2769.79227068638 -810.122270686381 34 1969.6 2789.73883790016 -820.138837900163 35 2061.41 2809.68540511394 -748.275405113945 36 2093.48 2829.63197232773 -736.151972327727 37 2120.88 2849.57853954151 -728.698539541508 38 2174.56 2869.52510675529 -694.96510675529 39 2196.72 2889.47167396907 -692.751673969072 40 2350.44 2909.41824118285 -558.978241182854 41 2440.25 2929.36480839664 -489.114808396636 42 2408.64 2949.31137561042 -540.671375610418 43 2472.81 2969.2579428242 -496.4479428242 44 2407.6 2989.20451003798 -581.604510037982 45 2454.62 3009.15107725176 -554.531077251764 46 2448.05 3029.09764446555 -581.047644465545 47 2497.84 3049.04421167933 -551.204211679327 48 2645.64 3068.99077889311 -423.350778893109 49 2756.76 3088.93734610689 -332.177346106891 50 2849.27 3108.88391332067 -259.613913320673 51 2921.44 3128.83048053445 -207.390480534455 52 2981.85 3148.77704774824 -166.927047748237 53 3080.58 3168.72361496202 -88.1436149620186 54 3106.22 3188.6701821758 -82.4501821758006 55 3119.31 3208.61674938958 -89.3067493895823 56 3061.26 3228.56331660336 -167.303316603364 57 3097.31 3248.50988381715 -151.199883817146 58 3161.69 3268.45645103093 -106.766451030928 59 3257.16 3288.40301824471 -31.2430182447099 60 3277.01 3308.34958545849 -31.3395854584914 61 3295.32 3328.29615267227 -32.9761526722734 62 3363.99 3348.24271988606 15.7472801139444 63 3494.17 3368.18928709984 125.980712900163 64 3667.03 3388.13585431362 278.894145686381 65 3813.06 3408.0824215274 404.977578472599 66 3917.96 3428.02898874118 489.931011258817 67 3895.51 3447.97555595496 447.534444045035 68 3801.06 3467.92212316875 333.137876831253 69 3570.12 3487.86869038253 82.2513096174713 70 3701.61 3507.81525759631 193.794742403690 71 3862.27 3527.76182481009 334.508175189908 72 3970.1 3547.70839202387 422.391607976126 73 4138.52 3567.65495923766 570.865040762344 74 4199.75 3587.60152645144 612.148473548562 75 4290.89 3607.54809366522 683.34190633478 76 4443.91 3627.494660879 816.415339120998 77 4502.64 3647.44122809278 855.198771907217 78 4356.98 3667.38779530657 689.592204693434 79 4591.27 3687.33436252035 903.935637479653 80 4696.96 3707.28092973413 989.679070265871 81 4621.4 3727.22749694791 894.172503052089 82 4562.84 3747.17406416169 815.665935838307 83 4202.52 3767.12063137547 435.399368624526 84 4296.49 3787.06719858926 509.422801410743 85 4435.23 3807.01376580304 628.216234196961 86 4105.18 3826.96033301682 278.21966698318 87 4116.68 3846.9069002306 269.773099769398 88 3844.49 3866.85346744438 -22.3634674443844 89 3720.98 3886.80003465817 -165.820034658166 90 3674.4 3906.74660187195 -232.346601871948 91 3857.62 3926.69316908573 -69.0731690857299 92 3801.06 3946.63973629951 -145.579736299512 93 3504.37 3966.58630351329 -462.216303513294 94 3032.6 3986.53287072708 -953.932870727075 95 3047.03 4006.47943794086 -959.449437940857 96 2962.34 4026.42600515464 -1064.08600515464 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000600283257993121 0.00120056651598624 0.999399716742007 6 0.000258833839493934 0.000517667678987869 0.999741166160506 7 2.49977377467723e-05 4.99954754935446e-05 0.999975002262253 8 2.32636987289196e-06 4.65273974578393e-06 0.999997673630127 9 5.55822520641877e-07 1.11164504128375e-06 0.99999944417748 10 7.75368488592025e-07 1.55073697718405e-06 0.999999224631511 11 2.80210347721051e-07 5.60420695442103e-07 0.999999719789652 12 3.78192751899761e-07 7.56385503799522e-07 0.999999621807248 13 8.22557341690124e-08 1.64511468338025e-07 0.999999917744266 14 1.42488962808499e-08 2.84977925616999e-08 0.999999985751104 15 2.44987591334826e-09 4.89975182669652e-09 0.999999997550124 16 1.25989317025791e-09 2.51978634051583e-09 0.999999998740107 17 1.03594394083590e-09 2.07188788167180e-09 0.999999998964056 18 6.44966101492039e-10 1.28993220298408e-09 0.999999999355034 19 1.42991072736787e-09 2.85982145473575e-09 0.99999999857009 20 1.65850969298617e-09 3.31701938597234e-09 0.99999999834149 21 6.00210829695387e-10 1.20042165939077e-09 0.99999999939979 22 3.43329995552012e-09 6.86659991104024e-09 0.9999999965667 23 1.32718814721881e-08 2.65437629443762e-08 0.999999986728119 24 8.15572293057562e-08 1.63114458611512e-07 0.99999991844277 25 4.0802400476543e-07 8.1604800953086e-07 0.999999591975995 26 2.71215633838407e-07 5.42431267676814e-07 0.999999728784366 27 1.57676498524009e-07 3.15352997048018e-07 0.999999842323501 28 8.39834953541934e-08 1.67966990708387e-07 0.999999916016505 29 9.80868405187019e-08 1.96173681037404e-07 0.99999990191316 30 1.78215220297132e-07 3.56430440594263e-07 0.99999982178478 31 7.50180394609104e-08 1.50036078921821e-07 0.99999992498196 32 3.08973305669547e-08 6.17946611339093e-08 0.99999996910267 33 1.58108849018924e-08 3.16217698037848e-08 0.999999984189115 34 9.65234076102028e-09 1.93046815220406e-08 0.99999999034766 35 1.10101812419155e-08 2.20203624838310e-08 0.999999988989819 36 1.62541497349022e-08 3.25082994698044e-08 0.99999998374585 37 2.86019787583343e-08 5.72039575166687e-08 0.999999971398021 38 6.76882320576793e-08 1.35376464115359e-07 0.999999932311768 39 1.61776120930283e-07 3.23552241860565e-07 0.99999983822388 40 8.53859232697465e-07 1.70771846539493e-06 0.999999146140767 41 4.79956057469606e-06 9.59912114939211e-06 0.999995200439425 42 1.40061168772118e-05 2.80122337544236e-05 0.999985993883123 43 4.0738837929889e-05 8.1477675859778e-05 0.99995926116207 44 7.62357068094616e-05 0.000152471413618923 0.99992376429319 45 0.000148582544671868 0.000297165089343737 0.999851417455328 46 0.000267779558700988 0.000535559117401977 0.9997322204413 47 0.000514170988194096 0.00102834197638819 0.999485829011806 48 0.00120656005384061 0.00241312010768121 0.99879343994616 49 0.00294354535921056 0.00588709071842112 0.99705645464079 50 0.0067896232901136 0.0135792465802272 0.993210376709886 51 0.0140189797865353 0.0280379595730707 0.985981020213465 52 0.0257392857325569 0.0514785714651138 0.974260714267443 53 0.0442519756360027 0.0885039512720053 0.955748024363997 54 0.0669076308252819 0.133815261650564 0.933092369174718 55 0.0923845577252573 0.184769115450515 0.907615442274743 56 0.119951383402243 0.239902766804485 0.880048616597757 57 0.154911907393636 0.309823814787273 0.845088092606364 58 0.198996447831339 0.397992895662677 0.801003552168661 59 0.251810325816202 0.503620651632404 0.748189674183798 60 0.315356699462528 0.630713398925056 0.684643300537472 61 0.394800942585832 0.789601885171664 0.605199057414168 62 0.48728779984582 0.97457559969164 0.51271220015418 63 0.578723183886651 0.842553632226698 0.421276816113349 64 0.655322818782319 0.689354362435362 0.344677181217681 65 0.71445028464086 0.571099430718279 0.285549715359140 66 0.757055704928144 0.485888590143712 0.242944295071856 67 0.789623385701093 0.420753228597813 0.210376614298907 68 0.827749776584679 0.344500446830642 0.172250223415321 69 0.90926789185617 0.181464216287660 0.0907321081438298 70 0.961579364252949 0.0768412714941027 0.0384206357470514 71 0.985876998385024 0.0282460032299529 0.0141230016149764 72 0.996011782308035 0.00797643538393068 0.00398821769196534 73 0.998781444295433 0.00243711140913421 0.00121855570456710 74 0.999715407249553 0.000569185500893639 0.000284592750446819 75 0.999938756632605 0.000122486734789696 6.1243367394848e-05 76 0.999974036810423 5.19263791540698e-05 2.59631895770349e-05 77 0.9999838159659 3.2368068200675e-05 1.61840341003375e-05 78 0.999998017556669 3.96488666300752e-06 1.98244333150376e-06 79 0.999997771181342 4.45763731653723e-06 2.22881865826861e-06 80 0.999994109969362 1.17800612759533e-05 5.89003063797666e-06 81 0.99998287594944 3.42481011180742e-05 1.71240505590371e-05 82 0.999947911242264 0.000104177515471984 5.20887577359919e-05 83 0.999947125281536 0.000105749436927902 5.2874718463951e-05 84 0.999866036170399 0.000267927659202034 0.000133963829601017 85 0.999628511735742 0.000742976528516271 0.000371488264258136 86 0.99890413078079 0.00219173843841830 0.00109586921920915 87 0.996506853906795 0.00698629218641075 0.00349314609320537 88 0.992112164803152 0.015775670393695 0.0078878351968475 89 0.988870694714256 0.0222586105714871 0.0111293052857435 90 0.991290641829322 0.0174187163413552 0.00870935817067761 91 0.965029216093004 0.0699415678139914 0.0349707839069957 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.701149425287356 NOK 5% type I error level 67 0.770114942528736 NOK 10% type I error level 71 0.816091954022989 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/10w20u1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/10w20u1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/1cod01227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/1cod01227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/2vna91227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/2vna91227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/31pns1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/31pns1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/4k6wx1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/4k6wx1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/5c7re1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/5c7re1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/6l9ya1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/6l9ya1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/74gjq1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/74gjq1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/8a8bc1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/8a8bc1227444546.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/9er9r1227444546.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445016q1u7slmp0b9hwh9/9er9r1227444546.ps (open in new window)

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') }

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