Seatbelt law Q3 (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: Thu, 20 Nov 2008 07:52:49 -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/t1227192833mrhkgf31vfway33.htm/, Retrieved Thu, 20 Nov 2008 14:54:03 +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/t1227192833mrhkgf31vfway33.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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Original text written by user:

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User-defined keywords:

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation M[t] = + 1670.00057339450 + 404.14629204893D[t] -21.2191760639659M1[t] -112.754478848114M2[t] -172.484702312691M3[t] -155.964925777268M4[t] + 28.9798507581553M5[t] + 30.1746272935779M6[t] -157.105596170999M7[t] -41.5108196355759M8[t] -167.266043100153M9[t] -12.2270530708459M10[t] -88.8572765354229M11[t] -0.169776535423036t + 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) 1670.00057339450 61.718619 27.0583 0 0 D 404.14629204893 53.565459 7.5449 0 0 M1 -21.2191760639659 71.42265 -0.2971 0.767138 0.383569 M2 -112.754478848114 73.626977 -1.5314 0.129466 0.064733 M3 -172.484702312691 73.592136 -2.3438 0.02148 0.01074 M4 -155.964925777268 73.567462 -2.12 0.03699 0.018495 M5 28.9798507581553 73.552965 0.394 0.694592 0.347296 M6 30.1746272935779 73.548651 0.4103 0.682667 0.341333 M7 -157.105596170999 73.554522 -2.1359 0.035633 0.017817 M8 -41.5108196355759 73.570575 -0.5642 0.574118 0.287059 M9 -167.266043100153 73.596804 -2.2727 0.025627 0.012813 M10 -12.2270530708459 73.452185 -0.1665 0.868198 0.434099 M11 -88.8572765354229 73.436885 -1.21 0.229721 0.11486 t -0.169776535423036 0.865512 -0.1962 0.844967 0.422483 Multiple Linear Regression - Regression Statistics Multiple R 0.824238679270694 R-squared 0.679369400405898 Adjusted R-squared 0.629150149867063 F-TEST (value) 13.5280672872753 F-TEST (DF numerator) 13 F-TEST (DF denominator) 83 p-value 1.66533453693773e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 146.863569535586 Sum Squared Residuals 1790219.36870891 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1593 1648.61162079511 -55.6116207951106 2 1477.9 1556.90654147553 -79.0065414755345 3 1733.7 1497.00654147553 236.693458524465 4 1569.7 1513.35654147553 56.3434585244659 5 1843.7 1698.13154147553 145.568458524466 6 1950.3 1699.15654147554 251.143458524464 7 1657.5 1511.70654147554 145.793458524465 8 1772.1 1627.13154147553 144.968458524465 9 1568.3 1501.20654147554 67.0934585244646 10 1809.8 1656.07575496942 153.724245030581 11 1646.7 1579.27575496942 67.4242450305815 12 1808.5 1667.96325496942 140.536745030581 13 1763.9 1646.57430237003 117.325697629970 14 1625.5 1554.86922305046 70.6307769495411 15 1538.8 1494.96922305046 43.830776949541 16 1342.4 1511.31922305046 -168.919223050459 17 1645.1 1696.09422305046 -50.9942230504589 18 1619.9 1697.11922305046 -77.2192230504584 19 1338.1 1509.66922305046 -171.569223050459 20 1505.5 1625.09422305046 -119.594223050459 21 1529.1 1499.16922305046 29.9307769495413 22 1511.9 1654.03843654434 -142.138436544342 23 1656.7 1577.23843654434 79.4615634556576 24 1694.4 1665.92593654434 28.4740634556576 25 1662.3 1644.53698394495 17.7630160550462 26 1588.7 1552.83190462538 35.8680953746177 27 1483.3 1492.93190462538 -9.63190462538255 28 1585.6 1509.28190462538 76.3180953746176 29 1658.9 1694.05690462538 -35.1569046253822 30 1584.4 1695.08190462538 -110.681904625382 31 1470.6 1507.63190462538 -37.0319046253823 32 1618.7 1623.05690462538 -4.3569046253822 33 1407.6 1497.13190462538 -89.5319046253823 34 1473.9 1652.00111811927 -178.101118119266 35 1515.3 1575.20111811927 -59.9011181192661 36 1485.4 1663.88861811927 -178.488618119266 37 1496.1 1642.49966551988 -146.399665519877 38 1493.5 1550.79458620031 -57.2945862003059 39 1298.4 1490.89458620031 -192.494586200306 40 1375.3 1507.24458620031 -131.944586200306 41 1507.9 1692.01958620031 -184.119586200306 42 1455.3 1693.04458620031 -237.744586200306 43 1363.3 1505.59458620031 -142.294586200306 44 1392.8 1621.01958620031 -228.219586200306 45 1348.8 1495.09458620031 -146.294586200306 46 1880.3 1649.96379969419 230.336200305810 47 1669.2 1573.16379969419 96.0362003058104 48 1543.6 1661.85129969419 -118.251299694190 49 1701.2 1640.4623470948 60.7376529051992 50 1516.5 1548.75726777523 -32.2572677752295 51 1466.8 1488.85726777523 -22.0572677752297 52 1484.1 1505.20726777523 -21.1072677752295 53 1577.2 1689.98226777523 -112.782267775229 54 1684.5 1691.00726777523 -6.5072677752292 55 1414.7 1503.55726777523 -88.8572677752293 56 1674.5 1618.98226777523 55.5177322247706 57 1598.7 1493.05726777523 105.642732224771 58 1739.1 1647.92648126911 91.1735187308868 59 1674.6 1571.12648126911 103.473518730887 60 1671.8 1659.81398126911 11.9860187308868 61 1802 1638.42502866972 163.574971330276 62 1526.8 1546.71994935015 -19.9199493501531 63 1580.9 1486.81994935015 94.0800506498469 64 1634.8 1503.16994935015 131.630050649847 65 1610.3 1687.94494935015 -77.6449493501531 66 1712 1688.96994935015 23.0300506498473 67 1678.8 1501.51994935015 177.280050649847 68 1708.1 1616.94494935015 91.155050649847 69 1680.6 1491.01994935015 189.580050649847 70 2056 2050.03545489297 5.96454510703359 71 1624 1973.23545489297 -349.235454892966 72 2021.4 2061.92295489297 -40.5229548929663 73 1861.1 2040.53400229358 -179.434002293578 74 1750.8 1948.82892297401 -198.028922974006 75 1767.5 1888.92892297401 -121.428922974007 76 1710.3 1905.27892297401 -194.978922974006 77 2151.5 2090.05392297401 61.4460770259937 78 2047.9 2091.07892297401 -43.1789229740059 79 1915.4 1903.62892297401 11.771077025994 80 1984.7 2019.05392297401 -34.3539229740061 81 1896.5 1893.12892297401 3.37107702599388 82 2170.8 2047.99813646789 122.801863532110 83 2139.9 1971.19813646789 168.70186353211 84 2330.5 2059.88563646789 270.61436353211 85 2121.8 2038.4966838685 83.303316131499 86 2226.8 1946.79160454893 280.00839545107 87 1857.9 1886.89160454893 -28.9916045489300 88 2155.9 1903.24160454893 252.658395451070 89 2341.7 2088.01660454893 253.68339545107 90 2290.2 2089.04160454893 201.158395451070 91 2006.5 1901.59160454893 104.908395451070 92 2111.9 2017.01660454893 94.8833954510703 93 1731.3 1891.09160454893 -159.791604548930 94 1762.2 2045.96081804281 -283.760818042813 95 1863.2 1969.16081804281 -105.960818042813 96 1943.5 2057.84831804281 -114.348318042814 97 1975.2 2036.45936544342 -61.2593654434247 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.746255578634733 0.507488842730533 0.253744421365267 18 0.761073246982132 0.477853506035736 0.238926753017868 19 0.73107408734672 0.537851825306561 0.268925912653281 20 0.650292033001798 0.699415933996404 0.349707966998202 21 0.572626792519194 0.854746414961612 0.427373207480806 22 0.514284946010965 0.97143010797807 0.485715053989035 23 0.485741516864572 0.971483033729144 0.514258483135428 24 0.400483503373308 0.800967006746615 0.599516496626692 25 0.402525045164783 0.805050090329567 0.597474954835217 26 0.421140708511903 0.842281417023806 0.578859291488097 27 0.351439156076430 0.702878312152859 0.64856084392357 28 0.448009964047914 0.896019928095827 0.551990035952086 29 0.375563955647169 0.751127911294338 0.624436044352831 30 0.319236738531445 0.63847347706289 0.680763261468555 31 0.269052519404154 0.538105038808309 0.730947480595846 32 0.234204590393939 0.468409180787878 0.765795409606061 33 0.184612568943649 0.369225137887299 0.81538743105635 34 0.148291810164092 0.296583620328184 0.851708189835908 35 0.112102961780979 0.224205923561958 0.887897038219021 36 0.102472543653460 0.204945087306921 0.89752745634654 37 0.0727708478656928 0.145541695731386 0.927229152134307 38 0.0557409341734179 0.111481868346836 0.944259065826582 39 0.0479862220737179 0.0959724441474358 0.952013777926282 40 0.033383077437017 0.066766154874034 0.966616922562983 41 0.0238762479883789 0.0477524959767579 0.976123752011621 42 0.0210837447606776 0.0421674895213552 0.978916255239322 43 0.0148076867812376 0.0296153735624752 0.985192313218762 44 0.0129519782836504 0.0259039565673008 0.98704802171635 45 0.00893054707867535 0.0178610941573507 0.991069452921325 46 0.0911991025193988 0.182398205038798 0.908800897480601 47 0.107668068396814 0.215336136793628 0.892331931603186 48 0.0842096860719355 0.168419372143871 0.915790313928065 49 0.0988847493753527 0.197769498750705 0.901115250624647 50 0.0804415137474731 0.160883027494946 0.919558486252527 51 0.0618395325238595 0.123679065047719 0.93816046747614 52 0.0532049146115636 0.106409829223127 0.946795085388436 53 0.0450904330790064 0.0901808661580127 0.954909566920994 54 0.0386213761000865 0.077242752200173 0.961378623899914 55 0.0344712383839877 0.0689424767679754 0.965528761616012 56 0.0336159999175667 0.0672319998351335 0.966384000082433 57 0.0362647958875846 0.0725295917751692 0.963735204112415 58 0.0309197847223563 0.0618395694447125 0.969080215277644 59 0.0277441025533186 0.0554882051066372 0.972255897446681 60 0.0207110223064055 0.041422044612811 0.979288977693594 61 0.0267622711841254 0.0535245423682508 0.973237728815875 62 0.0194461818680166 0.0388923637360333 0.980553818131983 63 0.0158289385055486 0.0316578770110972 0.984171061494451 64 0.0146037411844474 0.0292074823688948 0.985396258815553 65 0.0174437068794627 0.0348874137589253 0.982556293120537 66 0.0148426819141343 0.0296853638282687 0.985157318085866 67 0.0150199167579217 0.0300398335158433 0.984980083242078 68 0.0123703655673069 0.0247407311346139 0.987629634432693 69 0.00978903521166974 0.0195780704233395 0.99021096478833 70 0.00777693010011692 0.0155538602002338 0.992223069899883 71 0.0138219032156399 0.0276438064312799 0.98617809678436 72 0.00931918478485852 0.0186383695697170 0.990680815215142 73 0.00615396549400843 0.0123079309880169 0.993846034505992 74 0.017989701570204 0.035979403140408 0.982010298429796 75 0.0108996278545125 0.021799255709025 0.989100372145488 76 0.0507412316861552 0.101482463372310 0.949258768313845 77 0.0767860162728623 0.153572032545725 0.923213983727138 78 0.177573364918238 0.355146729836476 0.822426635081762 79 0.238649858652406 0.477299717304812 0.761350141347594 80 0.632314390091186 0.735371219817628 0.367685609908814 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 20 0.3125 NOK 10% type I error level 30 0.46875 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/10y0f11227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/10y0f11227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/1gz3f1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/1gz3f1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/2bms91227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/2bms91227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/3v6bj1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/3v6bj1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/46qrn1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/46qrn1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/5oyz21227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/5oyz21227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/6zlwv1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/6zlwv1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/7nugi1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/7nugi1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/8ugyb1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/8ugyb1227192763.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/9tdep1227192763.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227192833mrhkgf31vfway33/9tdep1227192763.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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