Case - Multiple Regression 2

*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 13:42:11 -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/t1261946708hfcazvofsvbd395.htm/, Retrieved Sun, 27 Dec 2009 21:45:21 +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/t1261946708hfcazvofsvbd395.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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 12617.1577967151 + 0.751444966508494X[t] + 44.4638308275148t + 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) 12617.1577967151 412.52413 30.5853 0 0 X 0.751444966508494 0.461603 1.6279 0.106236 0.053118 t 44.4638308275148 10.940199 4.0643 8.8e-05 4.4e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.781533035081483 R-squared 0.610793884923674 Adjusted R-squared 0.604140788939464 F-TEST (value) 91.8059631746223 F-TEST (DF numerator) 2 F-TEST (DF denominator) 117 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1711.90625887954 Sum Squared Residuals 342882895.58534 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11881.4 12979.7834263624 -1098.38342636237 2 10374.2 13009.7443693362 -2635.54436933618 3 13828 13126.2717724519 701.728227548131 4 13490.5 13150.1460111971 340.353988802946 5 13092.2 13212.2687987375 -120.068798737517 6 13184.4 13306.2528528579 -121.852852857943 7 12398.4 13255.1328839456 -856.732883945577 8 13882.3 13377.2961243101 505.003875689929 9 15861.5 13451.3668868180 2410.13311318198 10 13286.1 13451.1197421383 -165.019742138278 11 15634.9 13576.0633288789 2058.83667112115 12 14211 13572.4346818498 638.565318150175 13 13646.8 13588.4187484467 58.3812515533315 14 12224.6 13642.5010748455 -1417.90107484549 15 15916.4 13740.4677872884 2175.93221271159 16 16535.9 13807.4749671112 2728.42503288882 17 15796 13829.0948709568 1966.90512904316 18 14418.6 13908.1251702437 510.474829756258 19 15044.5 13925.7624157669 1118.73758423310 20 14944.2 14010.2031188127 933.99688118733 21 16754.8 14099.3779251474 2655.42207485256 22 14254 14104.2406062400 149.759393760042 23 15454.9 14173.3518319690 1281.54816803105 24 15644.8 14318.7347217986 1326.06527820144 25 14568.3 14290.9846913446 277.315308655394 26 12520.2 14264.5872618304 -1744.38726183037 27 14803 14375.7042611872 427.295738812812 28 15873.2 14504.0293502771 1369.17064972295 29 14755.3 14514.0770016385 241.222998361523 30 12875.1 15317.9511156195 -2442.85111561947 31 14291.1 15450.7097300117 -1159.60973001174 32 14205.3 15517.4163318479 -1312.11633184791 33 15859.4 15649.7992237569 209.600776243086 34 15258.9 15346.344035091 -87.4440350909955 35 15498.6 15321.2992065165 177.300793483526 36 15106.5 15566.6994213884 -460.199421388360 37 15023.6 15528.5794503966 -504.979450396591 38 12083 15698.3091571411 -3615.30915714107 39 15761.3 15977.6746844991 -216.374684499144 40 16943 15884.9246644422 1058.07533555779 41 15070.3 15737.5445953201 -667.244595320103 42 13659.6 16096.1875666448 -2436.58756664482 43 14768.9 15850.2930624135 -1081.39306241345 44 14725.1 15830.6586375978 -1105.55863759779 45 15998.1 15998.6600209193 -0.560020919302559 46 15370.6 15861.8753258250 -491.275325824968 47 14956.9 15793.5472671796 -836.647267179559 48 15469.7 16177.9648008555 -708.264800855516 49 15101.8 16271.7985659826 -1169.99856598264 50 11703.7 16159.8115547831 -4456.11155478309 51 16283.6 16579.5470018849 -295.947001884942 52 16726.5 16342.8952707416 383.604729258370 53 14968.9 16565.2261251417 -1596.32612514171 54 14861 16262.9732484222 -1401.9732484222 55 14583.3 16222.9746650142 -1639.67466501416 56 15305.8 16684.3401632606 -1378.54016326059 57 17903.9 16754.653700936 1149.24629906401 58 16379.4 16671.5221764504 -292.122176450368 59 15420.3 16594.0264892136 -1173.72648921355 60 17870.5 17062.4555701452 808.044429854839 61 15912.8 16784.6246548372 -871.824654837183 62 13866.5 16627.0249341706 -2760.52493417056 63 17823.2 16908.8702299181 914.329770081889 64 17872 17009.7675777304 862.232422269586 65 17420.4 17347.0695120063 73.3304879937121 66 16704.4 16948.0305236005 -243.630523600489 67 15991.2 16817.6331107215 -826.433110721478 68 16583.6 17241.7269386291 -658.126938629086 69 19123.5 17575.5722260590 1547.92777394098 70 17838.7 17405.4985189484 433.20148105164 71 17209.4 17342.2802860752 -132.880286075207 72 18586.5 17498.0331164426 1088.46688355737 73 16258.1 17441.2021657848 -1183.1021657848 74 15141.6 17627.2382283025 -2485.63822830252 75 19202.1 17823.0430753848 1379.05692461516 76 17746.5 17930.4028499091 -183.902849909117 77 19090.1 17952.3984762380 1137.70152376197 78 18040.3 17591.7583256208 448.541674379186 79 17515.5 17925.7539020441 -410.253902044051 80 17751.8 17931.4431725997 -179.643172599728 81 21072.4 18266.0399049962 2806.36009500383 82 17170 17985.8795102920 -815.879510292018 83 19439.5 18100.6034454881 1338.89655451192 84 19795.4 18214.1250687377 1581.27493126228 85 17574.9 18152.7103037842 -577.810303784188 86 16165.4 18398.6365301326 -2233.23653013263 87 19464.6 18409.8113489438 1054.78865105618 88 19932.1 18663.5526029439 1268.54739705605 89 19961.2 18383.3170637431 1577.88293625686 90 17343.4 18185.8156153549 -842.415615354923 91 18924.2 18719.0192523996 205.180747600436 92 18574.1 18810.8992606138 -236.799260613767 93 21350.6 19046.6058354177 2303.99416458231 94 18594.6 18603.9830389544 -9.3830389544023 95 19823.1 18698.7185380413 1124.38146195866 96 20844.4 18935.1014133151 1909.29858668488 97 19640.2 19027.2068550193 612.99314498073 98 17735.4 18767.1100409209 -1031.71004092089 99 19813.6 19428.6604782452 384.939521754815 100 22160 19096.6503808519 3063.34961914806 101 20664.3 19751.8886804576 912.411319542437 102 17877.4 18621.4683061490 -744.068306149044 103 20906.5 19138.891598897 1767.60840110299 104 21164.1 19146.9103488489 2017.18965115114 105 21374.4 19304.1660691493 2070.2339308507 106 22952.3 19483.965138445 3468.33486155500 107 21343.5 19055.4695073521 2288.03049264794 108 23899.3 19624.6673582925 4274.63264170754 109 22392.9 19469.0213945388 2923.87860546124 110 18274.1 19290.0054923267 -1015.90549232665 111 22786.7 19931.6426380385 2855.05736196153 112 22321.5 19766.6787567001 2554.82124329994 113 17842.2 19980.217704992 -2138.01770499199 114 16373.5 19693.2943055893 -3319.79430558926 115 15993.8 19558.4633674078 -3564.66336740785 116 16446.1 19888.2508520186 -3442.15085201864 117 17729 20145.6741863547 -2416.67418635466 118 16643 20015.201628979 -3372.20162897900 119 16196.7 19867.5961263669 -3670.89612636694 120 18252.1 20103.8287126474 -1851.72871264742 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.207732647984047 0.415465295968095 0.792267352015953 7 0.118186614080597 0.236373228161193 0.881813385919403 8 0.052584363265953 0.105168726531906 0.947415636734047 9 0.0435058409625457 0.0870116819250913 0.956494159037454 10 0.0238629573687289 0.0477259147374577 0.976137042631271 11 0.0108915016683788 0.0217830033367576 0.989108498331621 12 0.00478415905978684 0.00956831811957368 0.995215840940213 13 0.00187250153788246 0.00374500307576492 0.998127498462118 14 0.00325848735821651 0.00651697471643303 0.996741512641784 15 0.00241386111891682 0.00482772223783363 0.997586138881083 16 0.00154842251127777 0.00309684502255555 0.998451577488722 17 0.000869094248472462 0.00173818849694492 0.999130905751528 18 0.00222039743699742 0.00444079487399484 0.997779602563003 19 0.00110317278099533 0.00220634556199066 0.998896827219005 20 0.00107264545262519 0.00214529090525037 0.998927354547375 21 0.00071226785492725 0.0014245357098545 0.999287732145073 22 0.00106539331484927 0.00213078662969853 0.99893460668515 23 0.000766003215801352 0.00153200643160270 0.999233996784199 24 0.00469766591303012 0.00939533182606024 0.99530233408697 25 0.00557079063721973 0.0111415812744395 0.99442920936278 26 0.00741974446275705 0.0148394889255141 0.992580255537243 27 0.00536519042900775 0.0107303808580155 0.994634809570992 28 0.0058410155442004 0.0116820310884008 0.9941589844558 29 0.00603973373562448 0.0120794674712490 0.993960266264376 30 0.114460290229797 0.228920580459594 0.885539709770203 31 0.0886404076501614 0.177280815300323 0.911359592349839 32 0.0680161456324371 0.136032291264874 0.931983854367563 33 0.063454384087891 0.126908768175782 0.93654561591211 34 0.046677743429846 0.093355486859692 0.953322256570154 35 0.0347473439333064 0.0694946878666127 0.965252656066694 36 0.0244553940587041 0.0489107881174082 0.975544605941296 37 0.0172838141258918 0.0345676282517837 0.982716185874108 38 0.0537713911626514 0.107542782325303 0.946228608837349 39 0.0443990363994742 0.0887980727989484 0.955600963600526 40 0.0460143436180472 0.0920286872360944 0.953985656381953 41 0.0347212258220821 0.0694424516441643 0.965278774177918 42 0.0419618680094734 0.0839237360189468 0.958038131990527 43 0.033301964744676 0.066603929489352 0.966698035255324 44 0.0267056102446272 0.0534112204892543 0.973294389755373 45 0.0194760087523131 0.0389520175046262 0.980523991247687 46 0.0141905834577722 0.0283811669155444 0.985809416542228 47 0.0113250477618352 0.0226500955236703 0.988674952238165 48 0.00783092902899449 0.0156618580579890 0.992169070971005 49 0.0057447756731932 0.0114895513463864 0.994255224326807 50 0.0418335755072124 0.0836671510144248 0.958166424492788 51 0.0355532213386741 0.0711064426773482 0.964446778661326 52 0.0295922880851849 0.0591845761703699 0.970407711914815 53 0.0266996446420493 0.0533992892840985 0.97330035535795 54 0.0215568757553431 0.0431137515106862 0.978443124244657 55 0.0180997577194394 0.0361995154388788 0.98190024228056 56 0.0154116344095327 0.0308232688190654 0.984588365590467 57 0.0182794593948807 0.0365589187897614 0.98172054060512 58 0.0135513291556521 0.0271026583113043 0.986448670844348 59 0.0102777864740973 0.0205555729481946 0.989722213525903 60 0.0107297126044817 0.0214594252089633 0.989270287395518 61 0.0079062506955594 0.0158125013911188 0.99209374930444 62 0.0117510232695964 0.0235020465391929 0.988248976730404 63 0.0112839104158053 0.0225678208316105 0.988716089584195 64 0.0102924482594157 0.0205848965188313 0.989707551740584 65 0.00874227378380486 0.0174845475676097 0.991257726216195 66 0.00609190685694032 0.0121838137138806 0.99390809314306 67 0.00427241079351311 0.00854482158702622 0.995727589206487 68 0.00315328961986701 0.00630657923973401 0.996846710380133 69 0.00394985757838224 0.00789971515676447 0.996050142421618 70 0.00296822634111225 0.00593645268222449 0.997031773658888 71 0.00202178905960969 0.00404357811921938 0.99797821094039 72 0.00172152582352818 0.00344305164705635 0.998278474176472 73 0.00141499168263417 0.00282998336526835 0.998585008317366 74 0.00293934152703181 0.00587868305406361 0.997060658472968 75 0.00291960690253952 0.00583921380507904 0.99708039309746 76 0.00249608557067982 0.00499217114135963 0.99750391442932 77 0.00219826221885388 0.00439652443770775 0.997801737781146 78 0.00146611821468965 0.00293223642937931 0.99853388178531 79 0.00117031086176326 0.00234062172352651 0.998829689138237 80 0.00087898605683896 0.00175797211367792 0.99912101394316 81 0.00160388650509229 0.00320777301018457 0.998396113494908 82 0.00143916400699043 0.00287832801398085 0.99856083599301 83 0.00110085005100206 0.00220170010200411 0.998899149948998 84 0.00088326398940199 0.00176652797880398 0.999116736010598 85 0.000730146127470086 0.00146029225494017 0.99926985387253 86 0.00259275891181616 0.00518551782363232 0.997407241088184 87 0.00200598212770594 0.00401196425541188 0.997994017872294 88 0.00173043538851925 0.00346087077703849 0.99826956461148 89 0.00126716952902877 0.00253433905805754 0.998732830470971 90 0.00133141262894536 0.00266282525789073 0.998668587371055 91 0.00129127714593817 0.00258255429187635 0.998708722854062 92 0.00181303724903987 0.00362607449807973 0.99818696275096 93 0.00188091172741900 0.00376182345483799 0.998119088272581 94 0.00194369870934024 0.00388739741868047 0.99805630129066 95 0.00149235979359184 0.00298471958718368 0.998507640206408 96 0.00117678001149663 0.00235356002299326 0.998823219988503 97 0.00122385397228603 0.00244770794457207 0.998776146027714 98 0.00392127272555002 0.00784254545110005 0.99607872727445 99 0.0111765591120916 0.0223531182241831 0.988823440887908 100 0.0102667414781160 0.0205334829562319 0.989733258521884 101 0.128900042603003 0.257800085206006 0.871099957396997 102 0.158027285785271 0.316054571570542 0.841972714214729 103 0.186626126659450 0.373252253318899 0.81337387334055 104 0.185267963711386 0.370535927422771 0.814732036288614 105 0.203887606757009 0.407775213514018 0.796112393242991 106 0.183797750282427 0.367595500564853 0.816202249717573 107 0.146851368248448 0.293702736496896 0.853148631751552 108 0.157913865066722 0.315827730133445 0.842086134933278 109 0.195380505461279 0.390761010922558 0.804619494538721 110 0.143287835304959 0.286575670609918 0.856712164695041 111 0.168124358383158 0.336248716766316 0.831875641616842 112 0.980650220279678 0.0386995594406436 0.0193497797203218 113 0.959109663564554 0.0817806728708915 0.0408903364354457 114 0.905789996030442 0.188420007939117 0.0942100039695584 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 45 0.412844036697248 NOK 5% type I error level 75 0.688073394495413 NOK 10% type I error level 89 0.81651376146789 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') }

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