Case - Multiple Regression 3

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 27 Dec 2009 13:50:43 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/27/t12619472073b16l0xkyejxh88.htm/, Retrieved Sun, 27 Dec 2009 21:53:39 +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/2009/Dec/27/t12619472073b16l0xkyejxh88.htm/}, year = {2009}, } @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 = {2009}, 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 «

11881.4 423.4 10374.2 404.1 13828 500 13490.5 472.6 13092.2 496.1 13184.4 562 12398.4 434.8 13882.3 538.2 15861.5 577.6 13286.1 518.1 15634.9 625.2 14211 561.2 13646.8 523.3 12224.6 536.1 15916.4 607.3 16535.9 637.3 15796 606.9 14418.6 652.9 15044.5 617.2 14944.2 670.4 16754.8 729.9 14254 677.2 15454.9 710 15644.8 844.3 14568.3 748.2 12520.2 653.9 14803 742.6 15873.2 854.2 14755.3 808.4 12875.1 1819 14291.1 1936.5 14205.3 1966.1 15859.4 2083.1 15258.9 1620.1 15498.6 1527.6 15106.5 1795 15023.6 1685.1 12083 1851.8 15761.3 2164.4 16943 1981.8 15070.3 1726.5 13659.6 2144.6 14768.9 1758.2 14725.1 1672.9 15998.1 1837.3 15370.6 1596.1 14956.9 1446 15469.7 1898.4 15101.8 1964.1 11703.7 1755.9 16283.6 2255.3 16726.5 1881.2 14968.9 2117.9 14861 1656.5 14583.3 1544.1 15305.8 2098.9 17903.9 2133.3 16379.4 1963.5 15420.3 1801.2 17870.5 2365.4 15912.8 1936.5 13866.5 1667.6 17823.2 1983.5 17872 2058.6 17420.4 2448.3 16704.4 1858.1 15991.2 1625.4 16583.6 213 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y(Totale_export_België)[t] = + 14089.4720911269 -0.106970112958017`X(Export_farma_België)`[t] -1122.31209757918M1[t] -3382.67546988105M2[t] + 148.350628600380M3[t] + 469.703387358281M4[t] -676.882502217039M5[t] -2096.74617633866M6[t] -1643.94855487694M7[t] -1373.48568576860M8[t] + 528.958286622583M9[t] -1086.88490381533M10[t] -838.104704988366M11[t] + 62.121157076025t + 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) 14089.4720911269 571.972218 24.6331 0 0 `X(Export_farma_België)` -0.106970112958017 0.406747 -0.263 0.793069 0.396535 M1 -1122.31209757918 632.236753 -1.7751 0.078744 0.039372 M2 -3382.67546988105 635.620823 -5.3218 1e-06 0 M3 148.350628600380 631.173627 0.235 0.814632 0.407316 M4 469.703387358281 629.88861 0.7457 0.457503 0.228752 M5 -676.882502217039 630.180469 -1.0741 0.285213 0.142606 M6 -2096.74617633866 634.788403 -3.3031 0.001305 0.000652 M7 -1643.94855487694 632.652952 -2.5985 0.010696 0.005348 M8 -1373.48568576860 629.450286 -2.182 0.031315 0.015657 M9 528.958286622583 631.780017 0.8373 0.404335 0.202168 M10 -1086.88490381533 631.23077 -1.7219 0.088014 0.044007 M11 -838.104704988366 637.924653 -1.3138 0.19175 0.095875 t 62.121157076025 9.543155 6.5095 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.872840011786236 R-squared 0.761849686174996 Adjusted R-squared 0.732642572215326 F-TEST (value) 26.0843877702868 F-TEST (DF numerator) 13 F-TEST (DF denominator) 106 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1406.87570345254 Sum Squared Residuals 209805719.966299 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11881.4 12983.9900047974 -1102.59000479742 2 10374.2 10787.8123127516 -413.61231275156 3 13828 14370.7011344764 -542.701134476354 4 13490.5 14757.1060314053 -1266.60603140533 5 13092.2 13670.1275012515 -577.927501251521 6 13184.4 12305.3356537620 879.064346238015 7 12398.4 12833.861030668 -435.461030667998 8 13882.3 13155.3843471725 726.915652827491 9 15861.5 15115.7348541892 745.765145810837 10 13286.1 13568.3775425483 -282.277542548279 11 15634.9 13867.8223993535 1767.07760064654 12 14211 14774.8943486472 -563.894348647167 13 13646.8 13718.7575754251 -71.9575754251279 14 12224.6 11519.1461427534 705.453857246584 15 15916.4 15104.6771262683 811.722873731742 16 16535.9 15484.9419387134 1050.95806128656 17 15796 14403.7290976481 1392.27090235193 18 14418.6 13041.0659554064 1377.53404459360 19 15044.5 13559.8035669768 1484.69643302324 20 14944.2 13886.6967831518 1057.50321684825 21 16754.8 15844.8971908980 909.90280910204 22 14254 14296.8124824890 -42.8124824889603 23 15454.9 14604.2052186869 850.694781313076 24 15644.8 15490.0649945811 154.735005418945 25 14568.3 14440.1538819332 128.146118066832 26 12520.2 12251.9989483593 268.201051640741 27 14803 15835.6579548973 -1032.65795489734 28 15873.2 16207.1940061251 -333.994006125149 29 14755.3 15127.6285047993 -372.328504799333 30 12875.1 13661.7819915984 -786.68199159836 31 14291.1 14164.1317818635 126.968218136457 32 14205.3 14493.5494927044 -288.249492704354 33 15859.4 16445.5991189555 -586.19911895547 34 15258.9 14941.4042478931 317.495752106854 35 15498.6 15262.2003392447 236.399660755252 36 15106.5 16133.8223931042 -1027.32239310417 37 15023.6 15085.3874680151 -61.7874680151016 38 12083 12869.3133349592 -786.31333495915 39 15761.3 16429.0217332059 -667.721733205931 40 16943 16832.028391666 110.971608334010 41 15070.3 15774.8731290049 -704.573129004878 42 13659.6 14372.4064077315 -712.806407731529 43 14768.9 14928.6584379163 -159.758437916259 44 14725.1 15270.3670147359 -545.267014735943 45 15998.1 17217.3462576328 -1219.24625763285 46 15370.6 15689.4254155164 -318.825415516439 47 14956.9 16016.3829853744 -1059.48298537442 48 15469.7 16868.2155683366 -1398.51556833661 49 15101.8 15800.9966914121 -699.196691412116 50 11703.7 13625.0256537041 -1921.32565370412 51 16283.6 17164.7520348503 -881.152034850346 52 16726.5 17588.2434699419 -861.743469941866 53 14968.9 16478.4589117054 -1509.55891170541 54 14861 15170.0724047786 -309.072404778636 55 14583.3 15697.0146240129 -1113.71462401287 56 15305.8 15970.2516315281 -664.451631528129 57 17903.9 17931.1369891096 -27.2369891095757 58 16379.4 16395.5784809280 -16.1784809279634 59 15420.3 16723.8410861640 -1303.54108616404 60 17870.5 17563.7144104975 306.785589502486 61 15912.8 16549.4029514421 -636.602951442057 62 13866.5 14379.9249995906 -513.424999590617 63 17823.2 17939.2803964646 -116.080396464635 64 17872 18314.7208568154 -442.720856815414 65 17420.4 17188.5698712964 231.830128703621 66 16704.4 15893.9611149186 810.4388850814 67 15991.2 16433.7718387417 -442.571838741683 68 16583.6 16712.3145638597 -128.714563859661 69 19123.5 18635.6855028267 487.814497173268 70 17838.7 17112.5034367144 726.196563285641 71 17209.4 17438.7336098042 -229.333609804228 72 18586.5 18323.1171981395 263.382801860461 73 16258.1 17277.3458288631 -1019.24582886313 74 15141.6 15058.950444356 82.649555644012 75 19202.1 18630.5539191637 571.546080836299 76 17746.5 19005.0744365430 -1258.57443654304 77 19090.1 17923.8081104212 1166.29188957881 78 18040.3 16623.7331812713 1416.56681872874 79 17515.5 17097.4363752863 418.063624713716 80 17751.8 17435.5400592993 316.259940700715 81 21072.4 19358.8040281534 1713.59597184660 82 17170 17851.2930835894 -681.293083589376 83 19439.5 18152.1927339308 1287.30726606921 84 19795.4 19042.5880426143 752.811957385664 85 17574.9 17997.4691910270 -422.569191026969 86 16165.4 15770.5482885171 394.851711482924 87 19464.6 19368.4343200786 96.165679921426 88 19932.1 19722.1170594537 209.982940546308 89 19961.2 18683.8741127636 1277.32588723645 90 17343.4 17360.5759720904 -17.1759720904340 91 18924.2 17805.9213891603 1118.27861083971 92 18574.1 18131.755601217 442.34439878299 93 21350.6 20069.0968369364 1281.50316306360 94 18594.6 18584.7128307939 9.88716920609968 95 19823.1 18888.45788614 934.642113860005 96 20844.4 19761.3635813549 1083.03641864509 97 19640.2 18694.3907356902 945.809264309782 98 17735.4 16539.5035072463 1195.89649275375 99 19813.6 20044.8069060426 -231.206906042587 100 22160 20481.8728484685 1678.12715153152 101 20664.3 19310.4628081569 1353.83719184309 102 17877.4 18119.9680627212 -242.568062721164 103 20906.5 18567.5598521631 2338.94014783686 104 21164.1 18905.3319288260 2258.76807117403 105 21374.4 20853.8408443382 520.559155661824 106 22952.3 19280.8534936326 3671.44650636745 107 21343.5 19659.0818386313 1684.41816136869 108 23899.3 20484.6104708171 3414.68952918288 109 22392.9 19452.9056713947 2939.99432860531 110 18274.1 17286.4763677626 987.623632237442 111 22786.7 20794.6144745523 1992.08552544772 112 22321.5 21207.9009608676 1113.5990391324 113 17842.2 20099.3679529528 -2257.16795295275 114 16373.5 18788.7992557216 -2415.29925572164 115 15993.8 19329.2411032112 -3335.44110321117 116 16446.1 19621.2085775054 -3175.10857750538 117 17729 21555.4583769603 -3826.45837696028 118 16643 20026.6389858950 -3383.63898589502 119 16196.7 20364.8819026701 -4168.18190267008 120 18252.1 21237.8089919076 -2985.70899190759 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0173564448593172 0.0347128897186344 0.982643555140683 18 0.00586744633828972 0.0117348926765794 0.99413255366171 19 0.00183450344843321 0.00366900689686641 0.998165496551567 20 0.00307116610121611 0.00614233220243222 0.996928833898784 21 0.0050131268324591 0.0100262536649182 0.99498687316754 22 0.00397458209267364 0.00794916418534728 0.996025417907326 23 0.00587330003495239 0.0117466000699048 0.994126699965048 24 0.00453855674714582 0.00907711349429164 0.995461443252854 25 0.00258622008158393 0.00517244016316787 0.997413779918416 26 0.00188049258449452 0.00376098516898904 0.998119507415505 27 0.00554281764071965 0.0110856352814393 0.99445718235928 28 0.00463914401736893 0.00927828803473785 0.99536085598263 29 0.00476773041458559 0.00953546082917119 0.995232269585414 30 0.00277528125821688 0.00555056251643376 0.997224718741783 31 0.00236847283912621 0.00473694567825243 0.997631527160874 32 0.00116892622004637 0.00233785244009274 0.998831073779954 33 0.000567135284265463 0.00113427056853093 0.999432864715735 34 0.000454086006555357 0.000908172013110713 0.999545913993445 35 0.000267661684899114 0.000535323369798228 0.999732338315101 36 0.000127534178453370 0.000255068356906740 0.999872465821547 37 7.73138132044735e-05 0.000154627626408947 0.999922686186796 38 3.71757000782268e-05 7.43514001564536e-05 0.999962824299922 39 1.99637303927023e-05 3.99274607854045e-05 0.999980036269607 40 1.33665613327342e-05 2.67331226654685e-05 0.999986633438667 41 6.78838873962318e-06 1.35767774792464e-05 0.99999321161126 42 4.7399571099948e-06 9.4799142199896e-06 0.99999526004289 43 2.3551063061521e-06 4.7102126123042e-06 0.999997644893694 44 2.01736667827823e-06 4.03473335655646e-06 0.999997982633322 45 2.67231940008537e-06 5.34463880017073e-06 0.9999973276806 46 1.15675231685200e-06 2.31350463370400e-06 0.999998843247683 47 3.08937080647812e-06 6.17874161295624e-06 0.999996910629193 48 1.76424128077106e-06 3.52848256154212e-06 0.99999823575872 49 8.65014898262523e-07 1.73002979652505e-06 0.999999134985102 50 1.37296844426694e-06 2.74593688853388e-06 0.999998627031556 51 7.56550741460362e-07 1.51310148292072e-06 0.999999243449259 52 3.51834081840877e-07 7.03668163681753e-07 0.999999648165918 53 2.34733630147842e-07 4.69467260295684e-07 0.99999976526637 54 1.04847811536299e-07 2.09695623072598e-07 0.999999895152188 55 8.06066784338784e-08 1.61213356867757e-07 0.999999919393322 56 3.78376602886799e-08 7.56753205773598e-08 0.99999996216234 57 1.96564983272976e-08 3.93129966545953e-08 0.999999980343502 58 1.05204268311707e-08 2.10408536623413e-08 0.999999989479573 59 1.11147827433950e-08 2.22295654867900e-08 0.999999988885217 60 2.53008800787030e-08 5.06017601574059e-08 0.99999997469912 61 1.39411199898488e-08 2.78822399796976e-08 0.99999998605888 62 7.05722691463868e-09 1.41144538292774e-08 0.999999992942773 63 4.20090468598373e-09 8.40180937196747e-09 0.999999995799095 64 2.02858832943575e-09 4.0571766588715e-09 0.999999997971412 65 2.12939201832490e-09 4.25878403664979e-09 0.999999997870608 66 1.27171105151317e-09 2.54342210302634e-09 0.999999998728289 67 5.51490079170906e-10 1.10298015834181e-09 0.99999999944851 68 2.28277174860654e-10 4.56554349721308e-10 0.999999999771723 69 1.49484962570826e-10 2.98969925141653e-10 0.999999999850515 70 1.15307501768771e-10 2.30615003537542e-10 0.999999999884692 71 5.00160019110999e-11 1.00032003822200e-10 0.999999999949984 72 3.55667783502465e-11 7.1133556700493e-11 0.999999999964433 73 3.79806294459654e-11 7.59612588919309e-11 0.99999999996202 74 3.52511013757873e-11 7.05022027515746e-11 0.999999999964749 75 3.58749406435101e-11 7.17498812870201e-11 0.999999999964125 76 8.37228999438605e-11 1.67445799887721e-10 0.999999999916277 77 1.37858788474727e-10 2.75717576949454e-10 0.99999999986214 78 8.87125505973538e-11 1.77425101194708e-10 0.999999999911287 79 4.82171169958728e-11 9.64342339917457e-11 0.999999999951783 80 2.06513910997900e-11 4.13027821995799e-11 0.999999999979349 81 3.26572956537893e-11 6.53145913075785e-11 0.999999999967343 82 3.54946487487968e-11 7.09892974975937e-11 0.999999999964505 83 2.53727026465930e-11 5.07454052931861e-11 0.999999999974627 84 2.02164315260275e-11 4.0432863052055e-11 0.999999999979784 85 6.62987346210738e-11 1.32597469242148e-10 0.999999999933701 86 8.8629423503521e-11 1.77258847007042e-10 0.99999999991137 87 1.12767277733777e-10 2.25534555467555e-10 0.999999999887233 88 3.73189470098523e-10 7.46378940197046e-10 0.99999999962681 89 2.23138842414833e-10 4.46277684829666e-10 0.99999999977686 90 1.61391531148961e-10 3.22783062297921e-10 0.999999999838608 91 1.03244248136967e-10 2.06488496273934e-10 0.999999999896756 92 7.15165246291029e-11 1.43033049258206e-10 0.999999999928483 93 3.15619826438402e-11 6.31239652876805e-11 0.999999999968438 94 1.42067802830166e-10 2.84135605660332e-10 0.999999999857932 95 6.8511843717601e-11 1.37023687435202e-10 0.999999999931488 96 3.75645906373081e-10 7.51291812746162e-10 0.999999999624354 97 2.64623872483576e-08 5.29247744967153e-08 0.999999973537613 98 1.36333974713852e-07 2.72667949427703e-07 0.999999863666025 99 0.00146800237569548 0.00293600475139097 0.998531997624305 100 0.0789528008255164 0.157905601651033 0.921047199174484 101 0.606910461565024 0.786179076869952 0.393089538434976 102 0.757188025694256 0.485623948611487 0.242811974305744 103 0.80753778464364 0.384924430712721 0.192462215356360 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 78 0.896551724137931 NOK 5% type I error level 83 0.954022988505747 NOK 10% type I error level 83 0.954022988505747 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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