*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: Thu, 19 Nov 2009 01:16:57 -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/Nov/19/t1258618644vlc5duzmktnh927.htm/, Retrieved Thu, 19 Nov 2009 09:17:37 +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/Nov/19/t1258618644vlc5duzmktnh927.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 «

110,3672031 0 102,1880309 114,0150276 108,1560276 100 96,8602511 0 110,3672031 102,1880309 114,0150276 108,1560276 94,1944583 0 96,8602511 110,3672031 102,1880309 114,0150276 99,51621961 0 94,1944583 96,8602511 110,3672031 102,1880309 94,06333487 0 99,51621961 94,1944583 96,8602511 110,3672031 97,5541476 0 94,06333487 99,51621961 94,1944583 96,8602511 78,15062422 0 97,5541476 94,06333487 99,51621961 94,1944583 81,2434643 0 78,15062422 97,5541476 94,06333487 99,51621961 92,36262465 0 81,2434643 78,15062422 97,5541476 94,06333487 96,06324371 0 92,36262465 81,2434643 78,15062422 97,5541476 114,0523777 0 96,06324371 92,36262465 81,2434643 78,15062422 110,6616666 0 114,0523777 96,06324371 92,36262465 81,2434643 104,9171949 0 110,6616666 114,0523777 96,06324371 92,36262465 90,00187193 0 104,9171949 110,6616666 114,0523777 96,06324371 95,7008067 0 90,00187193 104,9171949 110,6616666 114,0523777 86,02741157 0 95,7008067 90,00187193 104,9171949 110,6616666 84,85287668 0 86,02741157 95,7008067 90,001871 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 23.6616041005634 -6.28343467972651X[t] + 0.259129910030104Y1[t] + 0.0678592356499644Y2[t] + 0.300566138661765Y3[t] + 0.11954465131137Y4[t] + 0.447120338298871M1[t] + 3.13191196265063M2[t] -10.6673105547517M3[t] -9.76717630491025M4[t] -8.14521510308711M5[t] + 3.22707368037767M6[t] -16.2457824226025M7[t] -2.16575180482806M8[t] -2.42715733581733M9[t] -1.16358422500642M10[t] + 11.8259988011420M11[t] + 0.177520173264150t + 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) 23.6616041005634 11.844879 1.9976 0.048918 0.024459 X -6.28343467972651 4.411284 -1.4244 0.157949 0.078975 Y1 0.259129910030104 0.106645 2.4298 0.017185 0.008592 Y2 0.0678592356499644 0.105736 0.6418 0.522724 0.261362 Y3 0.300566138661765 0.106042 2.8344 0.005721 0.00286 Y4 0.11954465131137 0.10777 1.1093 0.270411 0.135206 M1 0.447120338298871 6.06951 0.0737 0.941447 0.470723 M2 3.13191196265063 5.980129 0.5237 0.60182 0.30091 M3 -10.6673105547517 6.020446 -1.7718 0.079962 0.039981 M4 -9.76717630491025 6.293168 -1.552 0.124328 0.062164 M5 -8.14521510308711 6.263951 -1.3003 0.196961 0.098481 M6 3.22707368037767 6.315967 0.5109 0.610703 0.305351 M7 -16.2457824226025 5.835107 -2.7841 0.006599 0.003299 M8 -2.16575180482806 6.512214 -0.3326 0.74027 0.370135 M9 -2.42715733581733 6.296107 -0.3855 0.700818 0.350409 M10 -1.16358422500642 6.445088 -0.1805 0.857155 0.428577 M11 11.8259988011420 6.103349 1.9376 0.055951 0.027975 t 0.177520173264150 0.078389 2.2646 0.026051 0.013026 Multiple Linear Regression - Regression Statistics Multiple R 0.811769033341268 R-squared 0.658968963491817 Adjusted R-squared 0.591555851623921 F-TEST (value) 9.77508596225523 F-TEST (DF numerator) 17 F-TEST (DF denominator) 86 p-value 9.9698027611339e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.8679156254582 Sum Squared Residuals 12112.8782311977 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.3672031 102.965697210807 7.40150588919329 2 96.8602511 109.88093269058 -13.0206815905799 3 94.1944583 90.4598248455911 3.73463345440887 4 99.51621961 90.9746491906345 8.54157041936552 5 94.06333487 90.8903033143095 3.17303155569052 6 97.5541476 98.972306478278 -1.41815887827806 7 78.15062422 81.492375923757 -3.34175170375705 8 81.2434643 89.956012920021 -8.71254862002103 9 92.36262465 89.7542235666438 2.60840108335619 10 96.06324371 88.8717675274823 7.19147618251767 11 114.0523777 102.362365097059 11.6900126029414 12 110.6616666 99.3383059028582 11.3233606971418 13 104.9171949 102.746557563145 2.17063733685543 14 90.00187193 109.739527618820 -19.7376556888205 15 95.7008067 92.9943753242048 2.70643137579516 16 86.02741157 92.4047167316436 -6.37730516164357 17 84.85287668 86.914495556189 -2.06161887618898 18 100.04328 97.4333779275421 2.60990207245786 19 80.91713823 79.7684089515314 1.14872927846861 20 74.06539709 88.591185440573 -14.5257883505730 21 77.30281369 89.8592351629742 -12.5564214729742 22 97.23043249 87.7415468915675 9.48888559843252 23 90.75515676 101.946351447309 -11.1911946873085 24 100.5614455 90.126176983432 10.4352685165680 25 92.01293267 99.229096226296 -7.21616355629606 26 99.24012138 100.977671539818 -1.73755015981768 27 105.8672755 90.822008171181 15.0452673288190 28 90.9920463 92.7102838223847 -1.71823752238474 29 93.30624423 92.255181224768 1.05106300523195 30 91.17419413 106.251116277723 -15.0769221477229 31 77.33295039 82.8815927261962 -5.54864233619617 32 91.1277721 92.3250994300588 -1.19732733005881 33 85.01249943 94.512436678903 -9.49993724890296 34 83.90390242 90.8899015923685 -6.98599917236852 35 104.8626302 105.846366057431 -0.98373585743099 36 110.9039108 99.3647453857263 11.5391654142737 37 95.43714373 101.912850781344 -6.47570705134366 38 111.6238727 107.344174340609 4.27969835939079 39 108.8925403 101.188682813707 7.70385748629318 40 96.17511682 98.7304027003058 -2.55528588030577 41 101.9740205 100.065286492016 1.90873400798395 42 99.11953031 113.368861038547 -14.2493307285474 43 86.78158147 89.578407445564 -2.79682597556398 44 118.4195003 100.667777273347 17.7517230266529 45 118.7441447 107.780244021966 10.9639006780336 46 106.5296192 107.402778693284 -0.873159493284235 47 134.7772694 125.461114425716 9.31615497428449 48 104.6778714 124.183299576964 -19.5054281769645 49 105.2954304 115.292686480784 -9.9972560807843 50 139.4139849 123.302610099755 16.1113748002447 51 103.6060491 112.893948455889 -9.28789935588896 52 99.78182974 103.595350012062 -3.81352027206218 53 103.4610301 112.302670672971 -8.84164057297146 54 120.0594945 117.862399589339 2.19709491066137 55 96.71377168 97.6878119246435 -0.974040244643523 56 107.1308929 107.670824844534 -0.539931944533667 57 105.3608372 114.130869341622 -8.77003214162232 58 111.6942359 110.787510011797 0.906725888202717 59 132.0519998 125.815849226604 6.23615057339612 60 126.8037879 120.585748038344 6.21803986165644 61 154.4824253 122.923886670643 31.5585386293567 62 141.5570984 139.478400065832 2.07869833416769 63 109.9506882 125.241837104027 -15.2911489040269 64 127.904198 124.944087996815 2.96011000318453 65 133.0888617 128.674991372773 4.41387032722707 66 120.0796299 131.741642851762 -11.6620129517616 67 117.5557142 111.044893001384 6.51082119861605 68 143.0362309 127.470205635242 15.5660252647582 69 159.982927 124.244042852636 35.7388841473638 70 128.5991124 129.491832684023 -0.89272028402333 71 149.7373327 143.033300567204 6.70403213279556 72 126.8169313 142.872347887312 -16.0554165873124 73 140.9639674 131.585025224154 9.37894217584638 74 137.6691981 139.159562374336 -1.49036427433584 75 117.9402337 121.281958444108 -3.34172474410787 76 122.3095247 118.535776191719 3.77374850828066 77 127.7804207 120.829585517564 6.95083518243584 78 136.1677176 127.769833313526 8.39788428647392 79 116.2405856 109.973916450410 6.26666914958952 80 123.1576893 121.803598330747 1.35409096925263 81 116.3400234 125.334855283980 -8.99483188397974 82 108.6119282 120.491912150278 -11.8799839502782 83 125.8982264 130.890658242176 -4.99243184217618 84 112.8003105 121.974897113968 -9.17458661396786 85 107.5182447 117.240691613604 -9.72244691360394 86 135.0955413 122.117271069856 12.9782702301440 87 115.5096488 113.412928648912 2.09672015108772 88 115.8640759 108.133270868367 7.73080503163253 89 104.5883906 116.352870050945 -11.7644794509451 90 163.7213386 122.414725067645 41.3066135323553 91 113.4482275 115.442485342446 -1.99425784244561 92 98.0428844 117.338766702915 -19.2958823029154 93 116.7868521 126.276815261274 -9.48996316127435 94 126.5330444 123.488269169199 3.04477523080139 95 113.0336597 129.812647596502 -16.7789878965019 96 124.3392163 119.119619411395 5.21959688860474 97 109.8298759 126.927926329224 -17.0980504292238 98 124.4434777 123.905267710393 0.538209989606686 99 111.5039454 114.870082192380 -3.36613679238022 100 102.0350019 110.576887026067 -8.541885126067 101 116.8726598 111.702454978464 5.17020482153624 102 112.2073122 124.312382295638 -12.1050700956385 103 101.1513902 100.422091724068 0.729298475932147 104 124.4255108 114.825871512562 9.59963928743815 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.101298808199809 0.202597616399617 0.898701191800191 22 0.0455331256275007 0.0910662512550014 0.9544668743725 23 0.153679177269815 0.307358354539631 0.846320822730184 24 0.0871958875357022 0.174391775071404 0.912804112464298 25 0.053211084862781 0.106422169725562 0.94678891513722 26 0.0667792145675758 0.133558429135152 0.933220785432424 27 0.129966154977016 0.259932309954031 0.870033845022984 28 0.0833525233000579 0.166705046600116 0.916647476699942 29 0.0580599326981458 0.116119865396292 0.941940067301854 30 0.0372795311947863 0.0745590623895726 0.962720468805214 31 0.0240083600187689 0.0480167200375378 0.975991639981231 32 0.0388405749041345 0.077681149808269 0.961159425095865 33 0.0260847981203916 0.0521695962407831 0.973915201879608 34 0.0223062062520292 0.0446124125040584 0.97769379374797 35 0.0141335873214194 0.0282671746428389 0.98586641267858 36 0.0103956026912548 0.0207912053825095 0.989604397308745 37 0.00624190359949097 0.0124838071989819 0.99375809640051 38 0.0119056839603030 0.0238113679206059 0.988094316039697 39 0.00893977300290678 0.0178795460058136 0.991060226997093 40 0.00558347298549548 0.0111669459709910 0.994416527014504 41 0.00331049849906298 0.00662099699812597 0.996689501500937 42 0.00283771662099467 0.00567543324198934 0.997162283379005 43 0.00193910376994642 0.00387820753989285 0.998060896230054 44 0.0138240042926694 0.0276480085853389 0.98617599570733 45 0.0170191094616752 0.0340382189233503 0.982980890538325 46 0.0121326800392462 0.0242653600784925 0.987867319960754 47 0.00853981385882133 0.0170796277176427 0.991460186141179 48 0.0268150278942892 0.0536300557885784 0.973184972105711 49 0.0218398952738579 0.0436797905477158 0.978160104726142 50 0.0390831869256892 0.0781663738513785 0.96091681307431 51 0.0298194846369666 0.0596389692739333 0.970180515363033 52 0.0218000679474995 0.0436001358949991 0.9781999320525 53 0.0239756790568834 0.0479513581137667 0.976024320943117 54 0.0313503303594864 0.0627006607189728 0.968649669640514 55 0.0267977649901678 0.0535955299803356 0.973202235009832 56 0.0246674916182160 0.0493349832364319 0.975332508381784 57 0.037504829671087 0.075009659342174 0.962495170328913 58 0.0358829600926190 0.0717659201852379 0.964117039907381 59 0.0310044068111917 0.0620088136223834 0.968995593188808 60 0.0234720244472488 0.0469440488944975 0.976527975552751 61 0.121438276736431 0.242876553472863 0.878561723263569 62 0.0957286725098613 0.191457345019723 0.904271327490139 63 0.0913074120518481 0.182614824103696 0.908692587948152 64 0.0672330263337453 0.134466052667491 0.932766973666255 65 0.0516318425136341 0.103263685027268 0.948368157486366 66 0.0676447256811783 0.135289451362357 0.932355274318822 67 0.0561579690180069 0.112315938036014 0.943842030981993 68 0.0476412004424604 0.0952824008849208 0.95235879955754 69 0.160187502246349 0.320375004492698 0.839812497753651 70 0.284651138370100 0.569302276740199 0.7153488616299 71 0.369721157646260 0.739442315292521 0.63027884235374 72 0.421997812349855 0.84399562469971 0.578002187650145 73 0.442636542795373 0.885273085590746 0.557363457204627 74 0.363160191391225 0.726320382782451 0.636839808608775 75 0.288064507115483 0.576129014230965 0.711935492884517 76 0.214394161434061 0.428788322868122 0.785605838565939 77 0.154686239391888 0.309372478783775 0.845313760608112 78 0.107559446375643 0.215118892751286 0.892440553624357 79 0.0665925565834213 0.133185113166843 0.933407443416579 80 0.0391650460353018 0.0783300920706036 0.960834953964698 81 0.0250037028401406 0.0500074056802811 0.97499629715986 82 0.0206972482534763 0.0413944965069527 0.979302751746524 83 0.0162524764013677 0.0325049528027355 0.983747523598632 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0476190476190476 NOK 5% type I error level 22 0.349206349206349 NOK 10% type I error level 37 0.587301587301587 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/10fkwl1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/10fkwl1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/1tptx1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/1tptx1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/2cw3t1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/2cw3t1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/3qrgd1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/3qrgd1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/4n1yu1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/4n1yu1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/5t6bt1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/5t6bt1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/6flqj1258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/6flqj1258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/7yld41258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/7yld41258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/8v5e91258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/8v5e91258618612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/954991258618612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618644vlc5duzmktnh927/954991258618612.ps (open in new window)

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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