*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, 20 Dec 2009 11:09:00 -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/20/t1261332735n4530u2khclw4rm.htm/, Retrieved Sun, 20 Dec 2009 19:12:28 +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/20/t1261332735n4530u2khclw4rm.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 «

128,7 0 136,9 0 156,9 0 109,1 0 122,3 0 123,9 0 90,9 0 77,9 0 120,3 0 118,9 0 125,5 0 98,9 0 102,9 0 105,9 0 117,6 0 113,6 0 115,9 0 118,9 0 77,6 0 81,2 0 123,1 0 136,6 0 112,1 0 95,1 0 96,3 0 105,7 0 115,8 0 105,7 0 105,7 0 111,1 0 82,4 0 60 0 107,3 0 99,3 0 113,5 0 108,9 0 100,2 0 103,9 0 138,7 0 120,2 0 100,2 0 143,2 0 70,9 0 85,2 0 133 0 136,6 0 117,9 0 106,3 0 122,3 0 125,5 0 148,4 0 126,3 0 99,6 0 140,4 0 80,3 0 92,6 0 138,5 0 110,9 0 119,6 0 105 0 109 0 129,4 0 148,6 0 101,4 0 134,8 0 143,7 0 81,6 0 90,3 0 141,5 0 140,7 0 140,2 0 100,2 0 125,7 0 119,6 0 134,7 0 109 0 116,3 0 146,9 0 97,4 0 89,4 0 132,1 1 139,8 1 129 1 112,5 1 121,9 1 121,7 1 123,1 1 131,6 1 119,3 1 132,5 1 98,3 1 85,1 1 131,7 1 129,3 1 90,7 1 78,6 1 68,9 1 79,1 1 83,5 1 74,1 1 59,7 1 93,3 1 61,3 1 56,6 1 98,5 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation autoprod[t] = + 100.936662011173 -16.1842178770950crisis[t] + 7.6478358421271M1[t] + 13.3330785743844M2[t] + 28.7738768621974M3[t] + 9.11467515001035M4[t] + 7.13325121560109M5[t] + 27.0740495034140M6[t] -18.9073744309952M7[t] -21.4665761431823M8[t] + 25.5613574643079M9[t] + 25.9656256465963M10[t] + 17.9453128232982M11[t] + 0.0703128232981583t + 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) 100.936662011173 6.456249 15.6339 0 0 crisis -16.1842178770950 5.392988 -3.001 0.003473 0.001736 M1 7.6478358421271 7.690489 0.9945 0.322639 0.161319 M2 13.3330785743844 7.688218 1.7342 0.086265 0.043132 M3 28.7738768621974 7.686693 3.7433 0.000318 0.000159 M4 9.11467515001035 7.685916 1.1859 0.238752 0.119376 M5 7.13325121560109 7.685887 0.9281 0.355812 0.177906 M6 27.0740495034140 7.686604 3.5222 0.000671 0.000336 M7 -18.9073744309952 7.68807 -2.4593 0.015811 0.007905 M8 -21.4665761431823 7.690282 -2.7914 0.006395 0.003198 M9 25.5613574643079 7.69182 3.3232 0.001283 0.000642 M10 25.9656256465963 7.909401 3.2829 0.001459 0.000729 M11 17.9453128232982 7.908312 2.2692 0.025621 0.012811 t 0.0703128232981583 0.075797 0.9276 0.356045 0.178022 Multiple Linear Regression - Regression Statistics Multiple R 0.75620610559094 R-squared 0.571847674133016 Adjusted R-squared 0.510683056152018 F-TEST (value) 9.34932143793777 F-TEST (DF numerator) 13 F-TEST (DF denominator) 91 p-value 5.83000314691162e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15.8158969812649 Sum Squared Residuals 22762.9763563004 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 128.7 108.654810676598 20.0451893234017 2 136.9 114.410366232154 22.4896337678461 3 156.9 129.921477343265 26.9785226567349 4 109.1 110.332588454376 -1.23258845437614 5 122.3 108.421477343265 13.8785226567350 6 123.9 128.432588454376 -4.53258845437615 7 90.9 82.521477343265 8.37852265673495 8 77.9 80.0325884543762 -2.13258845437617 9 120.3 127.130834885164 -6.83083488516444 10 118.9 127.605415890751 -8.70541589075111 11 125.5 119.655415890751 5.84458410924892 12 98.9 101.780415890751 -2.88041589075106 13 102.9 109.498564556176 -6.5985645561763 14 105.9 115.254120111732 -9.35412011173182 15 117.6 130.765231222843 -13.1652312228429 16 113.6 111.176342333954 2.42365766604592 17 115.9 109.265231222843 6.63476877715704 18 118.9 129.276342333954 -10.3763423339541 19 77.6 83.365231222843 -5.76523122284295 20 81.2 80.876342333954 0.323657666045935 21 123.1 127.974588764742 -4.87458876474241 22 136.6 128.449169770329 8.15083022967101 23 112.1 120.499169770329 -8.399169770329 24 95.1 102.624169770329 -7.52416977032899 25 96.3 110.342318435754 -14.0423184357542 26 105.7 116.097873991310 -10.3978739913097 27 115.8 131.608985102421 -15.8089851024208 28 105.7 112.020096213532 -6.32009621353197 29 105.7 110.108985102421 -4.40898510242087 30 111.1 130.120096213532 -19.0200962135320 31 82.4 84.2089851024209 -1.80898510242084 32 60 81.720096213532 -21.7200962135320 33 107.3 128.818342644320 -21.5183426443203 34 99.3 129.292923649907 -29.9929236499069 35 113.5 121.342923649907 -7.84292364990689 36 108.9 103.467923649907 5.43207635009313 37 100.2 111.186072315332 -10.9860723153321 38 103.9 116.941627870888 -13.0416278708876 39 138.7 132.452738981999 6.24726101800124 40 120.2 112.863850093110 7.33614990689013 41 100.2 110.952738981999 -10.7527389819988 42 143.2 130.96385009311 12.2361499068901 43 70.9 85.0527389819988 -14.1527389819988 44 85.2 82.5638500931099 2.63614990689013 45 133 129.662096523898 3.33790347610179 46 136.6 130.136677529485 6.46332247051521 47 117.9 122.186677529485 -4.28667752948478 48 106.3 104.311677529485 1.98832247051522 49 122.3 112.02982619491 10.2701738050900 50 125.5 117.785381750466 7.71461824953444 51 148.4 133.296492861577 15.1035071384234 52 126.3 113.707603972688 12.5923960273122 53 99.6 111.796492861577 -12.1964928615767 54 140.4 131.807603972688 8.59239602731223 55 80.3 85.8964928615766 -5.59649286157665 56 92.6 83.4076039726878 9.19239602731222 57 138.5 130.505850403476 7.9941495965239 58 110.9 130.980431409063 -20.0804314090627 59 119.6 123.030431409063 -3.4304314090627 60 105 105.155431409063 -0.155431409062682 61 109 112.873580074488 -3.87358007448792 62 129.4 118.629135630043 10.7708643699566 63 148.6 134.140246741155 14.4597532588454 64 101.4 114.551357852266 -13.1513578522657 65 134.8 112.640246741155 22.1597532588454 66 143.7 132.651357852266 11.0486421477343 67 81.6 86.7402467411546 -5.14024674115456 68 90.3 84.2513578522657 6.04864214773432 69 141.5 131.349604283054 10.150395716946 70 140.7 131.824185288641 8.8758147113594 71 140.2 123.874185288641 16.3258147113594 72 100.2 105.999185288641 -5.79918528864058 73 125.7 113.717333954066 11.9826660459342 74 119.6 119.472889509621 0.127110490378640 75 134.7 134.984000620732 -0.284000620732461 76 109 115.395111731844 -6.39511173184358 77 116.3 113.484000620732 2.81599937926753 78 146.9 133.495111731844 13.4048882681564 79 97.4 87.5840006207325 9.81599937926755 80 89.4 85.0951117318436 4.30488826815643 81 132.1 116.009140285537 16.0908597144631 82 139.8 116.483721291124 23.3162787088765 83 129 108.533721291124 20.4662787088765 84 112.5 90.6587212911235 21.8412787088765 85 121.9 98.3768699565487 23.5231300434513 86 121.7 104.132425512104 17.5675744878957 87 123.1 119.643536623215 3.45646337678462 88 131.6 100.054647734326 31.5453522656735 89 119.3 98.1435366232154 21.1564633767846 90 132.5 118.154647734327 14.3453522656735 91 98.3 72.2435366232154 26.0564633767846 92 85.1 69.7546477343265 15.3453522656735 93 131.7 116.852894165115 14.8471058348851 94 129.3 117.327475170701 11.9725248292986 95 90.7 109.377475170701 -18.6774751707014 96 78.6 91.5024751707014 -12.9024751707014 97 68.9 99.2206238361266 -30.3206238361266 98 79.1 104.976179391682 -25.8761793916822 99 83.5 120.487290502793 -36.9872905027933 100 74.1 100.898401613904 -26.7984016139044 101 59.7 98.9872905027933 -39.2872905027933 102 93.3 118.998401613904 -25.6984016139044 103 61.3 73.0872905027933 -11.7872905027933 104 56.6 70.5984016139044 -13.9984016139044 105 98.5 117.696648044693 -19.1966480446927 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.511977936851894 0.976044126296212 0.488022063148106 18 0.398160043751654 0.796320087503308 0.601839956248346 19 0.261021748351371 0.522043496702742 0.738978251648629 20 0.235721147094319 0.471442294188637 0.764278852905681 21 0.194157894416898 0.388315788833797 0.805842105583102 22 0.259566464923473 0.519132929846946 0.740433535076527 23 0.17934637481528 0.35869274963056 0.82065362518472 24 0.120929620203706 0.241859240407411 0.879070379796294 25 0.0802319399007964 0.160463879801593 0.919768060099204 26 0.0490249361654568 0.0980498723309136 0.950975063834543 27 0.0316013172908773 0.0632026345817547 0.968398682709123 28 0.0215983544681205 0.0431967089362411 0.97840164553188 29 0.0120390387566802 0.0240780775133603 0.98796096124332 30 0.00772755485686549 0.0154551097137310 0.992272445143135 31 0.00561251718461891 0.0112250343692378 0.994387482815381 32 0.00398748255609895 0.0079749651121979 0.996012517443901 33 0.00277299675724252 0.00554599351448505 0.997227003242757 34 0.00418882664602364 0.00837765329204728 0.995811173353976 35 0.00302180566395805 0.0060436113279161 0.996978194336042 36 0.00641541998078736 0.0128308399615747 0.993584580019213 37 0.00479385221552569 0.00958770443105138 0.995206147784474 38 0.00349952935211247 0.00699905870422494 0.996500470647887 39 0.00604983757862607 0.0120996751572521 0.993950162421374 40 0.0092427878078388 0.0184855756156776 0.990757212192161 41 0.00678455674851977 0.0135691134970395 0.99321544325148 42 0.0253135520388655 0.0506271040777309 0.974686447961135 43 0.0243300312112872 0.0486600624225745 0.975669968788713 44 0.0297140710777412 0.0594281421554823 0.970285928922259 45 0.0393542482195747 0.0787084964391495 0.960645751780425 46 0.049075491273728 0.098150982547456 0.950924508726272 47 0.0426683523208112 0.0853367046416225 0.957331647679189 48 0.0349622802794454 0.0699245605588907 0.965037719720555 49 0.0353869332731172 0.0707738665462345 0.964613066726883 50 0.0316007716330341 0.0632015432660683 0.968399228366966 51 0.0292538008984369 0.0585076017968738 0.970746199101563 52 0.0243717559849778 0.0487435119699557 0.975628244015022 53 0.0297582892148049 0.0595165784296098 0.970241710785195 54 0.0288860605662286 0.0577721211324571 0.971113939433771 55 0.0351054240677631 0.0702108481355262 0.964894575932237 56 0.0373425123300347 0.0746850246600693 0.962657487669965 57 0.0383946232955641 0.0767892465911282 0.961605376704436 58 0.118154256441907 0.236308512883814 0.881845743558093 59 0.134632021148489 0.269264042296978 0.865367978851511 60 0.131585459191288 0.263170918382575 0.868414540808712 61 0.153476216606896 0.306952433213791 0.846523783393105 62 0.135367849518512 0.270735699037024 0.864632150481488 63 0.110328634861046 0.220657269722093 0.889671365138954 64 0.224016513382009 0.448033026764017 0.775983486617991 65 0.22714736498283 0.45429472996566 0.77285263501717 66 0.227706659086239 0.455413318172478 0.772293340913761 67 0.516643423428261 0.966713153143478 0.483356576571739 68 0.683190859859989 0.633618280280023 0.316809140140012 69 0.697917224934721 0.604165550130558 0.302082775065279 70 0.70845440299411 0.583091194011781 0.291545597005890 71 0.687951167409098 0.624097665181805 0.312048832590902 72 0.700445028478821 0.599109943042358 0.299554971521179 73 0.654296918907246 0.691406162185508 0.345703081092754 74 0.583332473985036 0.833335052029928 0.416667526014964 75 0.539499059919288 0.921001880161424 0.460500940080712 76 0.544582627464642 0.910834745070715 0.455417372535358 77 0.463633597501245 0.927267195002489 0.536366402498755 78 0.441356812547053 0.882713625094106 0.558643187452947 79 0.365111474774241 0.730222949548481 0.63488852522576 80 0.283776129126659 0.567552258253319 0.716223870873341 81 0.720490034374283 0.559019931251434 0.279509965625717 82 0.919914755934544 0.160170488130911 0.0800852440654555 83 0.87144703433663 0.257105931326741 0.128552965663370 84 0.827600469350434 0.344799061299132 0.172399530649566 85 0.78204451682981 0.435910966340379 0.217955483170190 86 0.666033730403635 0.66793253919273 0.333966269596365 87 0.543593867034445 0.91281226593111 0.456406132965555 88 0.553140147426705 0.89371970514659 0.446859852573295 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.0833333333333333 NOK 5% type I error level 16 0.222222222222222 NOK 10% type I error level 32 0.444444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/10e4kj1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/10e4kj1261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/1rp791261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/1rp791261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/2t9251261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/2t9251261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/3ydbt1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/3ydbt1261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/42g6v1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/42g6v1261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/50oz51261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/50oz51261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/6wr7z1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/6wr7z1261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/7xel91261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/7xel91261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/8l3ot1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/8l3ot1261332533.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/97ahe1261332533.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261332735n4530u2khclw4rm/97ahe1261332533.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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