Seatbelt law Q3 (1)

*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, 20 Nov 2008 07:47:19 -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/Nov/20/t122719252692vthtv4xgxay8b.htm/, Retrieved Thu, 20 Nov 2008 14:48:46 +0000

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/Nov/20/t122719252692vthtv4xgxay8b.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}, }

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Dataseries X:

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1593 0 1477,9 0 1733,7 0 1569,7 0 1843,7 0 1950,3 0 1657,5 0 1772,1 0 1568,3 0 1809,8 0 1646,7 0 1808,5 0 1763,9 0 1625,5 0 1538,8 0 1342,4 0 1645,1 0 1619,9 0 1338,1 0 1505,5 0 1529,1 0 1511,9 0 1656,7 0 1694,4 0 1662,3 0 1588,7 0 1483,3 0 1585,6 0 1658,9 0 1584,4 0 1470,6 0 1618,7 0 1407,6 0 1473,9 0 1515,3 0 1485,4 0 1496,1 0 1493,5 0 1298,4 0 1375,3 0 1507,9 0 1455,3 0 1363,3 0 1392,8 0 1348,8 0 1880,3 0 1669,2 0 1543,6 0 1701,2 0 1516,5 0 1466,8 0 1484,1 0 1577,2 0 1684,5 0 1414,7 0 1674,5 0 1598,7 0 1739,1 0 1674,6 0 1671,8 0 1802 0 1526,8 0 1580,9 0 1634,8 0 1610,3 0 1712 0 1678,8 0 1708,1 0 1680,6 0 2056 1 1624 1 2021,4 1 1861,1 1 1750,8 1 1767,5 1 1710,3 1 2151,5 1 2047,9 1 1915,4 1 1984,7 1 1896,5 1 2170,8 1 2139,9 1 2330,5 1 2121,8 1 2226,8 1 1857,9 1 2155,9 1 2341,7 1 2290,2 1 2006,5 1 2111,9 1 1731,3 1 1762,2 1 1863,2 1 1943,5 1 1975,2 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 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation M[t] = + 1589.85072463768 + 403.592132505176D[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) 1589.85072463768 18.890404 84.1618 0 0 D 403.592132505176 35.159938 11.4787 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.762271606752324 R-squared 0.58105800246077 Adjusted R-squared 0.576648086697199 F-TEST (value) 131.761701042169 F-TEST (DF numerator) 1 F-TEST (DF denominator) 95 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 156.915479798427 Sum Squared Residuals 2339134.44103520 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1593 1589.85072463768 3.14927536232137 2 1477.9 1589.85072463768 -111.950724637681 3 1733.7 1589.85072463768 143.849275362319 4 1569.7 1589.85072463768 -20.1507246376811 5 1843.7 1589.85072463768 253.849275362319 6 1950.3 1589.85072463768 360.449275362319 7 1657.5 1589.85072463768 67.6492753623188 8 1772.1 1589.85072463768 182.249275362319 9 1568.3 1589.85072463768 -21.5507246376812 10 1809.8 1589.85072463768 219.949275362319 11 1646.7 1589.85072463768 56.8492753623188 12 1808.5 1589.85072463768 218.649275362319 13 1763.9 1589.85072463768 174.049275362319 14 1625.5 1589.85072463768 35.6492753623188 15 1538.8 1589.85072463768 -51.0507246376812 16 1342.4 1589.85072463768 -247.450724637681 17 1645.1 1589.85072463768 55.2492753623187 18 1619.9 1589.85072463768 30.0492753623189 19 1338.1 1589.85072463768 -251.750724637681 20 1505.5 1589.85072463768 -84.3507246376812 21 1529.1 1589.85072463768 -60.7507246376813 22 1511.9 1589.85072463768 -77.9507246376811 23 1656.7 1589.85072463768 66.8492753623188 24 1694.4 1589.85072463768 104.549275362319 25 1662.3 1589.85072463768 72.4492753623188 26 1588.7 1589.85072463768 -1.15072463768115 27 1483.3 1589.85072463768 -106.550724637681 28 1585.6 1589.85072463768 -4.25072463768129 29 1658.9 1589.85072463768 69.0492753623189 30 1584.4 1589.85072463768 -5.4507246376811 31 1470.6 1589.85072463768 -119.250724637681 32 1618.7 1589.85072463768 28.8492753623188 33 1407.6 1589.85072463768 -182.250724637681 34 1473.9 1589.85072463768 -115.950724637681 35 1515.3 1589.85072463768 -74.5507246376812 36 1485.4 1589.85072463768 -104.450724637681 37 1496.1 1589.85072463768 -93.7507246376813 38 1493.5 1589.85072463768 -96.3507246376812 39 1298.4 1589.85072463768 -291.450724637681 40 1375.3 1589.85072463768 -214.550724637681 41 1507.9 1589.85072463768 -81.9507246376811 42 1455.3 1589.85072463768 -134.550724637681 43 1363.3 1589.85072463768 -226.550724637681 44 1392.8 1589.85072463768 -197.050724637681 45 1348.8 1589.85072463768 -241.050724637681 46 1880.3 1589.85072463768 290.449275362319 47 1669.2 1589.85072463768 79.3492753623188 48 1543.6 1589.85072463768 -46.2507246376813 49 1701.2 1589.85072463768 111.349275362319 50 1516.5 1589.85072463768 -73.3507246376812 51 1466.8 1589.85072463768 -123.050724637681 52 1484.1 1589.85072463768 -105.750724637681 53 1577.2 1589.85072463768 -12.6507246376811 54 1684.5 1589.85072463768 94.6492753623188 55 1414.7 1589.85072463768 -175.150724637681 56 1674.5 1589.85072463768 84.6492753623188 57 1598.7 1589.85072463768 8.84927536231885 58 1739.1 1589.85072463768 149.249275362319 59 1674.6 1589.85072463768 84.7492753623187 60 1671.8 1589.85072463768 81.9492753623188 61 1802 1589.85072463768 212.149275362319 62 1526.8 1589.85072463768 -63.0507246376812 63 1580.9 1589.85072463768 -8.9507246376811 64 1634.8 1589.85072463768 44.9492753623188 65 1610.3 1589.85072463768 20.4492753623188 66 1712 1589.85072463768 122.149275362319 67 1678.8 1589.85072463768 88.9492753623188 68 1708.1 1589.85072463768 118.249275362319 69 1680.6 1589.85072463768 90.7492753623187 70 2056 1993.44285714286 62.5571428571428 71 1624 1993.44285714286 -369.442857142857 72 2021.4 1993.44285714286 27.9571428571429 73 1861.1 1993.44285714286 -132.342857142857 74 1750.8 1993.44285714286 -242.642857142857 75 1767.5 1993.44285714286 -225.942857142857 76 1710.3 1993.44285714286 -283.142857142857 77 2151.5 1993.44285714286 158.057142857143 78 2047.9 1993.44285714286 54.4571428571429 79 1915.4 1993.44285714286 -78.0428571428571 80 1984.7 1993.44285714286 -8.7428571428571 81 1896.5 1993.44285714286 -96.9428571428572 82 2170.8 1993.44285714286 177.357142857143 83 2139.9 1993.44285714286 146.457142857143 84 2330.5 1993.44285714286 337.057142857143 85 2121.8 1993.44285714286 128.357142857143 86 2226.8 1993.44285714286 233.357142857143 87 1857.9 1993.44285714286 -135.542857142857 88 2155.9 1993.44285714286 162.457142857143 89 2341.7 1993.44285714286 348.257142857143 90 2290.2 1993.44285714286 296.757142857143 91 2006.5 1993.44285714286 13.0571428571429 92 2111.9 1993.44285714286 118.457142857143 93 1731.3 1993.44285714286 -262.142857142857 94 1762.2 1993.44285714286 -231.242857142857 95 1863.2 1993.44285714286 -130.242857142857 96 1943.5 1993.44285714286 -49.9428571428572 97 1975.2 1993.44285714286 -18.2428571428571 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.654780171267909 0.690439657464182 0.345219828732091 6 0.84580691769369 0.308386164612619 0.154193082306309 7 0.756181903886681 0.487636192226638 0.243818096113319 8 0.681068700731367 0.637862598537266 0.318931299268633 9 0.62965514471398 0.74068971057204 0.37034485528602 10 0.595724995719785 0.80855000856043 0.404275004280215 11 0.502932415441526 0.994135169116949 0.497067584558474 12 0.473012968751064 0.946025937502128 0.526987031248936 13 0.407677817197457 0.815355634394913 0.592322182802543 14 0.343373386283124 0.686746772566248 0.656626613716876 15 0.344786455055222 0.689572910110444 0.655213544944778 16 0.631516219065689 0.736967561868622 0.368483780934311 17 0.556992466729255 0.88601506654149 0.443007533270745 18 0.48471062305177 0.96942124610354 0.51528937694823 19 0.685563167419902 0.628873665160196 0.314436832580098 20 0.659599520246294 0.680800959507412 0.340400479753706 21 0.616286931868167 0.767426136263667 0.383713068131833 22 0.579231459261855 0.84153708147629 0.420768540738145 23 0.514558757114691 0.970882485770618 0.485441242885309 24 0.46289202613823 0.92578405227646 0.53710797386177 25 0.4020201249312 0.8040402498624 0.5979798750688 26 0.341386790521469 0.682773581042937 0.658613209478531 27 0.32635308911485 0.6527061782297 0.67364691088515 28 0.271216082725484 0.542432165450968 0.728783917274516 29 0.224978450809198 0.449956901618395 0.775021549190802 30 0.181124049879438 0.362248099758876 0.818875950120562 31 0.174753693410588 0.349507386821176 0.825246306589412 32 0.137148715755363 0.274297431510727 0.862851284244637 33 0.162809075606229 0.325618151212458 0.837190924393771 34 0.151150526970041 0.302301053940082 0.848849473029959 35 0.126783984035122 0.253567968070243 0.873216015964878 36 0.112162996914216 0.224325993828431 0.887837003085784 37 0.0958140166621807 0.191628033324361 0.90418598333782 38 0.0815540243236342 0.163108048647268 0.918445975676366 39 0.159293149677117 0.318586299354234 0.840706850322883 40 0.1937212925739 0.3874425851478 0.8062787074261 41 0.164744798120273 0.329489596240545 0.835255201879727 42 0.154925453833672 0.309850907667344 0.845074546166328 43 0.197285803769834 0.394571607539668 0.802714196230166 44 0.223765619453335 0.44753123890667 0.776234380546665 45 0.294551606992505 0.589103213985009 0.705448393007495 46 0.421661799808155 0.84332359961631 0.578338200191845 47 0.377918663440729 0.755837326881458 0.622081336559271 48 0.330518046032244 0.661036092064489 0.669481953967756 49 0.301495423236540 0.602990846473081 0.69850457676346 50 0.266105106560124 0.532210213120249 0.733894893439876 51 0.254626422783374 0.509252845566748 0.745373577216626 52 0.237787768416067 0.475575536832133 0.762212231583933 53 0.198431904478564 0.396863808957128 0.801568095521436 54 0.170115995196466 0.340231990392933 0.829884004803534 55 0.196459926141822 0.392919852283643 0.803540073858178 56 0.165570297442067 0.331140594884134 0.834429702557933 57 0.134493932870799 0.268987865741598 0.865506067129201 58 0.123052615748521 0.246105231497043 0.876947384251479 59 0.0998843154826676 0.199768630965335 0.900115684517332 60 0.0796022622679982 0.159204524535996 0.920397737732002 61 0.0879302029590378 0.175860405918076 0.912069797040962 62 0.073583442056071 0.147166884112142 0.926416557943929 63 0.0570479466079143 0.114095893215829 0.942952053392086 64 0.0426321009120357 0.0852642018240713 0.957367899087964 65 0.0318302847785683 0.0636605695571366 0.968169715221432 66 0.0247083842005039 0.0494167684010079 0.975291615799496 67 0.0179709081543901 0.0359418163087802 0.98202909184561 68 0.01336286449971 0.02672572899942 0.98663713550029 69 0.00934248106777619 0.0186849621355524 0.990657518932224 70 0.00625331601082272 0.0125066320216454 0.993746683989177 71 0.0249498736724259 0.0498997473448518 0.975050126327574 72 0.0190951511077119 0.0381903022154237 0.980904848892288 73 0.0154796695216888 0.0309593390433776 0.984520330478311 74 0.0214497233933455 0.042899446786691 0.978550276606655 75 0.0284002254162148 0.0568004508324295 0.971599774583785 76 0.0598645227793062 0.119729045558612 0.940135477220694 77 0.0669612264122143 0.133922452824429 0.933038773587786 78 0.0520194343419842 0.104038868683968 0.947980565658016 79 0.0422045837769188 0.0844091675538375 0.957795416223081 80 0.0303126441198341 0.0606252882396682 0.969687355880166 81 0.0263198557543212 0.0526397115086423 0.973680144245679 82 0.0252143237690588 0.0504286475381176 0.974785676230941 83 0.0202945657868559 0.0405891315737117 0.979705434213144 84 0.0544044045007042 0.108808809001408 0.945595595499296 85 0.0409951382685264 0.0819902765370528 0.959004861731474 86 0.0515917283317312 0.103183456663462 0.948408271668269 87 0.0435439661145991 0.0870879322291982 0.9564560338854 88 0.0359881190126633 0.0719762380253266 0.964011880987337 89 0.147562262379634 0.295124524759267 0.852437737620366 90 0.482387390362681 0.964774780725362 0.517612609637319 91 0.391819834875792 0.783639669751584 0.608180165124208 92 0.592681662853807 0.814636674292386 0.407318337146193 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 10 0.113636363636364 NOK 10% type I error level 20 0.227272727272727 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/10mf561227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/10mf561227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/1uzwe1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/1uzwe1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/2id5z1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/2id5z1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/3rdio1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/3rdio1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/403q71227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/403q71227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/57uso1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/57uso1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/6t77i1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/6t77i1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/7saz01227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/7saz01227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/8ue8w1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/8ue8w1227192425.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/9bdgv1227192425.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t122719252692vthtv4xgxay8b/9bdgv1227192425.ps (open in new window)

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') }

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