paper

*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: Sat, 13 Dec 2008 08:01:35 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229180577r3udhyg5h12rzt3.htm/, Retrieved Sat, 13 Dec 2008 16:03:07 +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/2008/Dec/13/t1229180577r3udhyg5h12rzt3.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6340.5 0 7901.5 0 8191.1 0 7181.7 0 7594.4 0 7384.7 0 7876.7 0 8463.4 0 8317.2 0 7778.7 0 8532.8 0 7272.2 0 6680.1 0 8427.6 0 8752.8 0 7952.7 0 8694.3 0 7787 0 8474.2 0 9154.7 0 8557.2 0 7951.1 0 9156.7 0 7865.7 0 7337.4 0 9131.7 0 8814.6 0 8598.8 0 8439.6 0 7451.8 0 8016.2 0 9544.1 0 8270.7 0 8102.2 0 9369 0 7657.7 0 7816.6 0 9391.3 0 9445.4 0 9533.1 0 10068.7 0 8955.5 0 10423.9 0 11617.2 0 9391.1 0 10872 0 10230.4 0 9221 0 9428.6 0 10934.5 0 10986 0 11724.6 0 11180.9 0 11163.2 0 11240.9 0 12107.1 0 10762.3 0 11340.4 0 11266.8 0 9542.7 0 9227.7 0 10571.9 0 10774.4 0 10392.8 0 9920.2 0 9884.9 1 10174.5 1 11395.4 1 10760.2 1 10570.1 1 10536 1 9902.6 1 8889 1 10837.3 1 11624.1 1 10509 1 10984.9 1 10649.1 1 10855.7 1 11677.4 1 10760.2 1 10046.2 1 10772.8 1 9987.7 1 8638.7 1 11063.7 1 11855.7 1 10684.5 1 11337.4 1 10478 1 11123.9 1 12909.3 1 11339.9 1 10462.2 1 12733.5 1 10519.2 1 10414.9 1 12476.8 1 12384.6 1 12266.7 1 12919.9 1 11497.3 1 12142 1 13919.4 1 12656.8 1 12034.1 1 13199.7 1 10881.3 1 11301.2 1 13643.9 1 12517 1 13981.1 1 14275.7 1 13435 1 13565.7 1 16216.3 1 12970 1 14079.9 1 14235 1 12213.4 1 12581 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation y[t] = + 9116.21538461539 + 2593.99532967033x[t] + 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) 9116.21538461539 178.239826 51.1458 0 0 x 2593.99532967033 262.001285 9.9007 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.672066250063232 R-squared 0.451673044474055 Adjusted R-squared 0.44706525493182 F-TEST (value) 98.023800855928 F-TEST (DF numerator) 1 F-TEST (DF denominator) 119 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1437.01541671755 Sum Squared Residuals 245736583.638187 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6340.5 9116.21538461535 -2775.71538461535 2 7901.5 9116.21538461538 -1214.71538461538 3 8191.1 9116.21538461539 -925.115384615385 4 7181.7 9116.21538461539 -1934.51538461539 5 7594.4 9116.21538461539 -1521.81538461539 6 7384.7 9116.21538461539 -1731.51538461539 7 7876.7 9116.21538461539 -1239.51538461539 8 8463.4 9116.21538461539 -652.815384615386 9 8317.2 9116.21538461539 -799.015384615385 10 7778.7 9116.21538461539 -1337.51538461539 11 8532.8 9116.21538461539 -583.415384615386 12 7272.2 9116.21538461539 -1844.01538461539 13 6680.1 9116.21538461539 -2436.11538461538 14 8427.6 9116.21538461539 -688.615384615385 15 8752.8 9116.21538461539 -363.415384615386 16 7952.7 9116.21538461539 -1163.51538461539 17 8694.3 9116.21538461539 -421.915384615386 18 7787 9116.21538461539 -1329.21538461539 19 8474.2 9116.21538461539 -642.015384615385 20 9154.7 9116.21538461539 38.4846153846155 21 8557.2 9116.21538461539 -559.015384615385 22 7951.1 9116.21538461539 -1165.11538461538 23 9156.7 9116.21538461539 40.4846153846155 24 7865.7 9116.21538461539 -1250.51538461539 25 7337.4 9116.21538461539 -1778.81538461539 26 9131.7 9116.21538461539 15.4846153846154 27 8814.6 9116.21538461539 -301.615384615385 28 8598.8 9116.21538461539 -517.415384615386 29 8439.6 9116.21538461539 -676.615384615385 30 7451.8 9116.21538461539 -1664.41538461539 31 8016.2 9116.21538461539 -1100.01538461539 32 9544.1 9116.21538461539 427.884615384615 33 8270.7 9116.21538461539 -845.515384615385 34 8102.2 9116.21538461539 -1014.01538461539 35 9369 9116.21538461539 252.784615384615 36 7657.7 9116.21538461539 -1458.51538461539 37 7816.6 9116.21538461539 -1299.61538461538 38 9391.3 9116.21538461539 275.084615384614 39 9445.4 9116.21538461539 329.184615384614 40 9533.1 9116.21538461539 416.884615384615 41 10068.7 9116.21538461539 952.484615384615 42 8955.5 9116.21538461539 -160.715384615385 43 10423.9 9116.21538461539 1307.68461538461 44 11617.2 9116.21538461539 2500.98461538462 45 9391.1 9116.21538461539 274.884615384615 46 10872 9116.21538461539 1755.78461538461 47 10230.4 9116.21538461539 1114.18461538461 48 9221 9116.21538461539 104.784615384615 49 9428.6 9116.21538461539 312.384615384615 50 10934.5 9116.21538461539 1818.28461538462 51 10986 9116.21538461539 1869.78461538462 52 11724.6 9116.21538461539 2608.38461538462 53 11180.9 9116.21538461539 2064.68461538461 54 11163.2 9116.21538461539 2046.98461538462 55 11240.9 9116.21538461539 2124.68461538461 56 12107.1 9116.21538461539 2990.88461538462 57 10762.3 9116.21538461539 1646.08461538461 58 11340.4 9116.21538461539 2224.18461538461 59 11266.8 9116.21538461539 2150.58461538461 60 9542.7 9116.21538461539 426.484615384615 61 9227.7 9116.21538461539 111.484615384615 62 10571.9 9116.21538461539 1455.68461538461 63 10774.4 9116.21538461539 1658.18461538461 64 10392.8 9116.21538461539 1276.58461538461 65 9920.2 9116.21538461539 803.984615384615 66 9884.9 11710.2107142857 -1825.31071428571 67 10174.5 11710.2107142857 -1535.71071428571 68 11395.4 11710.2107142857 -314.810714285715 69 10760.2 11710.2107142857 -950.010714285714 70 10570.1 11710.2107142857 -1140.11071428571 71 10536 11710.2107142857 -1174.21071428571 72 9902.6 11710.2107142857 -1807.61071428571 73 8889 11710.2107142857 -2821.21071428571 74 10837.3 11710.2107142857 -872.910714285715 75 11624.1 11710.2107142857 -86.1107142857143 76 10509 11710.2107142857 -1201.21071428571 77 10984.9 11710.2107142857 -725.310714285715 78 10649.1 11710.2107142857 -1061.11071428571 79 10855.7 11710.2107142857 -854.510714285714 80 11677.4 11710.2107142857 -32.810714285715 81 10760.2 11710.2107142857 -950.010714285714 82 10046.2 11710.2107142857 -1664.01071428571 83 10772.8 11710.2107142857 -937.410714285715 84 9987.7 11710.2107142857 -1722.51071428571 85 8638.7 11710.2107142857 -3071.51071428571 86 11063.7 11710.2107142857 -646.510714285714 87 11855.7 11710.2107142857 145.489285714286 88 10684.5 11710.2107142857 -1025.71071428571 89 11337.4 11710.2107142857 -372.810714285715 90 10478 11710.2107142857 -1232.21071428571 91 11123.9 11710.2107142857 -586.310714285715 92 12909.3 11710.2107142857 1199.08928571428 93 11339.9 11710.2107142857 -370.310714285715 94 10462.2 11710.2107142857 -1248.01071428571 95 12733.5 11710.2107142857 1023.28928571429 96 10519.2 11710.2107142857 -1191.01071428571 97 10414.9 11710.2107142857 -1295.31071428571 98 12476.8 11710.2107142857 766.589285714285 99 12384.6 11710.2107142857 674.389285714286 100 12266.7 11710.2107142857 556.489285714286 101 12919.9 11710.2107142857 1209.68928571429 102 11497.3 11710.2107142857 -212.910714285715 103 12142 11710.2107142857 431.789285714285 104 13919.4 11710.2107142857 2209.18928571428 105 12656.8 11710.2107142857 946.589285714285 106 12034.1 11710.2107142857 323.889285714286 107 13199.7 11710.2107142857 1489.48928571429 108 10881.3 11710.2107142857 -828.910714285715 109 11301.2 11710.2107142857 -409.010714285714 110 13643.9 11710.2107142857 1933.68928571429 111 12517 11710.2107142857 806.789285714285 112 13981.1 11710.2107142857 2270.88928571429 113 14275.7 11710.2107142857 2565.48928571429 114 13435 11710.2107142857 1724.78928571429 115 13565.7 11710.2107142857 1855.48928571429 116 16216.3 11710.2107142857 4506.08928571429 117 12970 11710.2107142857 1259.78928571429 118 14079.9 11710.2107142857 2369.68928571428 119 14235 11710.2107142857 2524.78928571429 120 12213.4 11710.2107142857 503.189285714285 121 12581 11710.2107142857 870.789285714285 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.207447482937940 0.414894965875879 0.79255251706206 6 0.097726428916831 0.195452857833662 0.902273571083169 7 0.0494232593291551 0.0988465186583102 0.950576740670845 8 0.0438648567191954 0.0877297134383908 0.956135143280805 9 0.0284590667139702 0.0569181334279404 0.97154093328603 10 0.0130574351066348 0.0261148702132697 0.986942564893365 11 0.0101433973811993 0.0202867947623985 0.9898566026188 12 0.00596475685600473 0.0119295137120095 0.994035243143995 13 0.00758766156322215 0.0151753231264443 0.992412338436778 14 0.00565685403083582 0.0113137080616716 0.994343145969164 15 0.0058337343797473 0.0116674687594946 0.994166265620253 16 0.00306806274525519 0.00613612549051038 0.996931937254745 17 0.00266637116770002 0.00533274233540004 0.9973336288323 18 0.00140787528304366 0.00281575056608731 0.998592124716956 19 0.000934297704667468 0.00186859540933494 0.999065702295333 20 0.00142534073118144 0.00285068146236288 0.998574659268818 21 0.00094369371113694 0.00188738742227388 0.999056306288863 22 0.000514590494443833 0.00102918098888767 0.999485409505556 23 0.000657524857016777 0.00131504971403355 0.999342475142983 24 0.000382060886950992 0.000764121773901984 0.99961793911305 25 0.000333316639249439 0.000666633278498878 0.99966668336075 26 0.000398095333279024 0.000796190666558048 0.99960190466672 27 0.000311505010426738 0.000623010020853477 0.999688494989573 28 0.000205362676433416 0.000410725352866831 0.999794637323567 29 0.000125607135230406 0.000251214270460811 0.99987439286477 30 0.000116891403326812 0.000233782806653624 0.999883108596673 31 7.47341059858488e-05 0.000149468211971698 0.999925265894014 32 0.00015296002426339 0.00030592004852678 0.999847039975737 33 9.95769657677395e-05 0.000199153931535479 0.999900423034232 34 6.8130375932401e-05 0.000136260751864802 0.999931869624068 35 9.57125730004929e-05 0.000191425146000986 0.999904287427 36 9.51167039233458e-05 0.000190233407846692 0.999904883296077 37 8.80962160372902e-05 0.000176192432074580 0.999911903783963 38 0.000127487183529461 0.000254974367058921 0.99987251281647 39 0.000180861057910513 0.000361722115821026 0.99981913894209 40 0.000261342443438735 0.000522684886877469 0.999738657556561 41 0.000658838909288603 0.00131767781857721 0.999341161090711 42 0.000579105488169881 0.00115821097633976 0.99942089451183 43 0.00178670342360818 0.00357340684721636 0.998213296576392 44 0.0185669547816911 0.0371339095633823 0.98143304521831 45 0.0172576891844746 0.0345153783689491 0.982742310815525 46 0.03465431263586 0.06930862527172 0.96534568736414 47 0.040540703606816 0.081081407213632 0.959459296393184 48 0.0364686991057045 0.072937398211409 0.963531300894296 49 0.0336510970100727 0.0673021940201455 0.966348902989927 50 0.053627744844233 0.107255489688466 0.946372255155767 51 0.0782208221593884 0.156441644318777 0.921779177840612 52 0.151280501551932 0.302561003103864 0.848719498448068 53 0.192748941619152 0.385497883238304 0.807251058380848 54 0.230802224526570 0.461604449053141 0.76919777547343 55 0.271346714928411 0.542693429856823 0.728653285071589 56 0.399309186198553 0.798618372397107 0.600690813801447 57 0.397918774712805 0.79583754942561 0.602081225287195 58 0.437004431596229 0.874008863192458 0.562995568403771 59 0.468168692426088 0.936337384852175 0.531831307573912 60 0.423708590609768 0.847417181219536 0.576291409390232 61 0.386669926747714 0.773339853495427 0.613330073252286 62 0.365937044676937 0.731874089353873 0.634062955323063 63 0.354240254763459 0.708480509526918 0.645759745236541 64 0.326551282622711 0.653102565245423 0.673448717377289 65 0.287400580610201 0.574801161220401 0.712599419389799 66 0.278948591116278 0.557897182232556 0.721051408883722 67 0.262457393646337 0.524914787292673 0.737542606353663 68 0.234153210899294 0.468306421798587 0.765846789100706 69 0.205759255765603 0.411518511531206 0.794240744234397 70 0.183306677728507 0.366613355457013 0.816693322271493 71 0.163797070956804 0.327594141913608 0.836202929043196 72 0.166455766626581 0.332911533253161 0.83354423337342 73 0.241140356195709 0.482280712391419 0.758859643804291 74 0.217737238957251 0.435474477914503 0.782262761042749 75 0.193389924967858 0.386779849935716 0.806610075032142 76 0.180253772170874 0.360507544341749 0.819746227829126 77 0.15865618667707 0.31731237335414 0.84134381332293 78 0.145329938236967 0.290659876473934 0.854670061763033 79 0.129460744512423 0.258921489024846 0.870539255487577 80 0.110400506605410 0.220801013210820 0.88959949339459 81 0.099433441241514 0.198866882483028 0.900566558758486 82 0.110725559734909 0.221451119469817 0.889274440265091 83 0.102079754117069 0.204159508234138 0.89792024588293 84 0.122333047630008 0.244666095260016 0.877666952369992 85 0.30561568074106 0.61123136148212 0.69438431925894 86 0.294191369560413 0.588382739120826 0.705808630439587 87 0.266876846303175 0.533753692606351 0.733123153696825 88 0.280488242538791 0.560976485077583 0.719511757461209 89 0.263979293632104 0.527958587264208 0.736020706367896 90 0.306531219656129 0.613062439312258 0.693468780343871 91 0.308033519334865 0.616067038669729 0.691966480665136 92 0.297453547653814 0.594907095307629 0.702546452346186 93 0.28802531942261 0.57605063884522 0.71197468057739 94 0.363936878559733 0.727873757119466 0.636063121440267 95 0.336085862154789 0.672171724309579 0.663914137845211 96 0.431094282451947 0.862188564903894 0.568905717548053 97 0.583077921581356 0.833844156837288 0.416922078418644 98 0.546523522235721 0.906952955528557 0.453476477764279 99 0.509273965562536 0.98145206887493 0.490726034437464 100 0.474642258154819 0.949284516309639 0.52535774184518 101 0.43134243419186 0.86268486838372 0.56865756580814 102 0.457851055591378 0.915702111182757 0.542148944408622 103 0.433488577848507 0.866977155697013 0.566511422151493 104 0.429290996684188 0.858581993368375 0.570709003315812 105 0.376246405800887 0.752492811601774 0.623753594199113 106 0.357792505885104 0.715585011770208 0.642207494114896 107 0.302092478538548 0.604184957077096 0.697907521461452 108 0.480223085666545 0.96044617133309 0.519776914333455 109 0.660127745922905 0.67974450815419 0.339872254077095 110 0.588108960335414 0.823782079329173 0.411891039664586 111 0.575418236532497 0.849163526935006 0.424581763467503 112 0.494135213067946 0.988270426135892 0.505864786932054 113 0.425526811041772 0.851053622083544 0.574473188958228 114 0.32044918635775 0.6408983727155 0.67955081364225 115 0.218720632104928 0.437441264209856 0.781279367895072 116 0.657553710259155 0.68489257948169 0.342446289740845 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 28 0.25 NOK 5% type I error level 36 0.321428571428571 NOK 10% type I error level 43 0.383928571428571 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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