bouwvergunningen 1990-2008

*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: Fri, 21 Nov 2008 06:36:35 -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/Nov/21/t1227274687aq8w798byc587zz.htm/, Retrieved Fri, 21 Nov 2008 13:38:08 +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/21/t1227274687aq8w798byc587zz.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1929 0 1851 0 1607 0 1661 0 2259 0 1668 0 2011 0 1944 0 1958 0 1844 0 1868 0 1701 0 2338 0 2018 0 1302 0 2168 0 2139 0 1560 0 2093 0 1973 0 2090 0 2811 0 1984 0 1849 0 2433 0 2071 0 1855 0 1756 0 1898 0 1770 0 1969 0 1769 0 2139 0 3013 0 2061 0 2132 0 2973 0 2081 0 2257 0 2075 0 2084 0 1747 0 2092 0 1919 0 2551 0 2643 0 2153 0 2496 0 2645 0 2035 0 2294 0 2205 0 2044 0 1762 0 1897 0 1821 0 1905 0 2111 0 1643 0 1956 0 1977 0 1685 0 1393 0 1574 0 1793 0 1562 0 1510 0 1675 0 1965 0 2173 0 2395 0 2197 0 2257 0 2885 0 1594 0 1950 0 1772 0 1280 0 1724 0 1473 0 1461 0 1576 0 1900 0 1618 0 2303 0 1994 0 1575 0 1893 0 1788 0 1817 0 3233 0 727 1 1121 1 1665 1 1401 1 1415 1 2058 1 1544 1 1379 1 1402 1 1313 1 1296 1 1398 1 1288 1 1563 1 1972 1 1496 1 1481 1 1819 1 1479 1 1635 1 1511 1 1547 1 1388 1 1958 1 1390 1 1597 1 1842 1 1396 1 1671 1 1385 1 1632 1 1313 1 1300 1 1431 1 1398 1 1198 1 1292 1 1434 1 1660 1 1837 1 1455 1 1315 1 1642 1 1069 1 1209 1 1586 1 1122 1 1063 1 1125 1 1414 1 1347 1 1403 1 1299 1 1547 1 1515 1 1247 1 1639 1 1296 1 1063 1 1282 1 1365 1 1268 1 1532 1 1455 1 1393 1 1515 1 1510 1 1225 1 1577 1 1417 1 1224 1 1693 1 1633 1 1639 1 1914 1 1586 1 1552 1 2081 1 1500 1 1437 1 1470 1 1849 1 1387 1 1592 1 1589 1 1798 1 1935 1 1887 1 2027 1 2080 1 1556 1 1682 1 1785 1 1869 1 1781 1 2082 1 2570 1 1862 1 1936 1 1504 1 1765 1 1607 1 1577 1 1493 1 1615 1 1700 1 1335 1 1523 1 1623 1 1540 1 1637 1 1524 1 1419 1 1821 1 1593 1 1357 1 1263 1 1750 1 1405 1 1393 1 1639 1 1679 1 1551 1 1744 1 1429 1 1784 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation y[t] = + 1930.42953952992 -595.342844865146x[t] + 266.156710598825M1[t] + 52.8135046321413M2[t] -195.925306080324M3[t] -67.2752279038997M4[t] + 13.7637391614132M5[t] -263.641738217718M6[t] + 20.9527844031499M7[t] -108.155868261251M8[t] + 11.0497654707282M9[t] + 241.866510313819M10[t] + 22.5165884902428M11[t] + 1.29436626802048t + 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) 1930.42953952992 74.789881 25.8114 0 0 x -595.342844865146 73.673038 -8.0809 0 0 M1 266.156710598825 91.328573 2.9143 0.003965 0.001983 M2 52.8135046321413 92.594918 0.5704 0.569056 0.284528 M3 -195.925306080324 92.582299 -2.1162 0.035544 0.017772 M4 -67.2752279038997 92.573316 -0.7267 0.468232 0.234116 M5 13.7637391614132 92.567968 0.1487 0.881947 0.440974 M6 -263.641738217718 92.566256 -2.8481 0.00485 0.002425 M7 20.9527844031499 92.568181 0.2263 0.821157 0.410579 M8 -108.155868261251 92.570958 -1.1684 0.244033 0.122017 M9 11.0497654707282 92.55823 0.1194 0.905091 0.452545 M10 241.866510313819 92.549137 2.6134 0.009636 0.004818 M11 22.5165884902428 92.543681 0.2433 0.808013 0.404006 t 1.29436626802048 0.580192 2.2309 0.02678 0.01339 Multiple Linear Regression - Regression Statistics Multiple R 0.708473161496393 R-squared 0.501934220560694 Adjusted R-squared 0.470038382468521 F-TEST (value) 15.7366681856802 F-TEST (DF numerator) 13 F-TEST (DF denominator) 203 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 277.625587403754 Sum Squared Residuals 15646421.2565997 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1929 2197.88061639677 -268.880616396772 2 1851 1985.83177669810 -134.831776698102 3 1607 1738.38733225366 -131.387332253658 4 1661 1868.33177669810 -207.331776698103 5 2259 1950.66511003144 308.334889968564 6 1668 1674.55399892033 -6.55399892032504 7 2011 1960.44288780921 50.5571121907859 8 1944 1832.62860141283 111.371398587167 9 1958 1953.12860141283 4.87139858716674 10 1844 2185.23971252394 -341.239712523944 11 1868 1967.18415696839 -99.1841569683888 12 1701 1945.96193474617 -244.961934746167 13 2338 2213.41301161301 124.586988386987 14 2018 2001.36417191435 16.6358280856513 15 1302 1753.91972746990 -451.919727469904 16 2168 1883.86417191435 284.135828085651 17 2139 1966.19750524768 172.802494752318 18 1560 1690.08639413657 -130.086394136571 19 2093 1975.97528302546 117.024716974540 20 1973 1848.16099662908 124.839003370921 21 2090 1968.66099662908 121.339003370921 22 2811 2200.77210774019 610.22789225981 23 1984 1982.71655218463 1.28344781536544 24 1849 1961.49432996241 -112.494329962412 25 2433 2228.94540682926 204.054593170742 26 2071 2016.89656713059 54.1034328694055 27 1855 1769.45212268615 85.54787731385 28 1756 1899.39656713059 -143.396567130594 29 1898 1981.72990046393 -83.7299004639278 30 1770 1705.61878935282 64.3812106471833 31 1969 1991.50767824171 -22.5076782417056 32 1769 1863.69339184532 -94.6933918453248 33 2139 1984.19339184532 154.806608154675 34 3013 2216.30450295644 796.695497043564 35 2061 1998.24894740088 62.7510525991196 36 2132 1977.02672517866 154.973274821342 37 2973 2244.47780204550 728.522197954496 38 2081 2032.42896234684 48.5710376531597 39 2257 1784.98451790240 472.015482097604 40 2075 1914.92896234684 160.071037653160 41 2084 1997.26229568017 86.7377043198264 42 1747 1721.15118456906 25.8488154309376 43 2092 2007.04007345795 84.9599265420485 44 1919 1879.22578706157 39.7742129384294 45 2551 1999.72578706157 551.274212938429 46 2643 2231.83689817268 411.163101827318 47 2153 2013.78134261713 139.218657382874 48 2496 1992.55912039490 503.440879605096 49 2645 2260.01019726175 384.98980273825 50 2035 2047.96135756309 -12.9613575630860 51 2294 1800.51691311864 493.483086881358 52 2205 1930.46135756309 274.538642436914 53 2044 2012.79469089642 31.2053091035806 54 1762 1736.68357978531 25.3164202146918 55 1897 2022.57246867420 -125.572468674197 56 1821 1894.75818227782 -73.7581822778163 57 1905 2015.25818227782 -110.258182277816 58 2111 2247.36929338893 -136.369293388928 59 1643 2029.31373783337 -386.313737833372 60 1956 2008.09151561115 -52.0915156111496 61 1977 2275.54259247800 -298.542592477996 62 1685 2063.49375277933 -378.493752779332 63 1393 1816.04930833489 -423.049308334887 64 1574 1945.99375277933 -371.993752779332 65 1793 2028.32708611267 -235.327086112665 66 1562 1752.21597500155 -190.215975001554 67 1510 2038.10486389044 -528.104863890443 68 1675 1910.29057749406 -235.290577494062 69 1965 2030.79057749406 -65.7905774940621 70 2173 2262.90168860517 -89.9016886051732 71 2395 2044.84613304962 350.153866950382 72 2197 2023.62391082740 173.376089172605 73 2257 2291.07498769424 -34.0749876942415 74 2885 2079.02614799558 805.973852004422 75 1594 1831.58170355113 -237.581703551133 76 1950 1961.52614799558 -11.5261479955775 77 1772 2043.85948132891 -271.859481328911 78 1280 1767.7483702178 -487.7483702178 79 1724 2053.63725910669 -329.637259106689 80 1473 1925.82297271031 -452.822972710308 81 1461 2046.32297271031 -585.322972710308 82 1576 2278.43408382142 -702.434083821419 83 1900 2060.37852826586 -160.378528265863 84 1618 2039.15630604364 -421.156306043641 85 2303 2306.60738291049 -3.6073829104872 86 1994 2094.55854321182 -100.558543211823 87 1575 1847.11409876738 -272.114098767379 88 1893 1977.05854321182 -84.0585432118233 89 1788 2059.39187654516 -271.391876545157 90 1817 1783.28076543405 33.7192345659545 91 3233 2069.16965432293 1163.83034567707 92 727 1346.01252306141 -619.012523061408 93 1121 1466.51252306141 -345.512523061408 94 1665 1698.62363417252 -33.6236341725192 95 1401 1480.56807861696 -79.5680786169636 96 1415 1459.34585639474 -44.3458563947413 97 2058 1726.79693326159 331.203066738413 98 1544 1514.74809356292 29.2519064370765 99 1379 1267.30364911848 111.696350881521 100 1402 1397.24809356292 4.75190643707645 101 1313 1479.58142689626 -166.581426896257 102 1296 1203.47031578515 92.5296842148542 103 1398 1489.35920467403 -91.3592046740346 104 1288 1361.54491827765 -73.5449182776538 105 1563 1482.04491827765 80.9550817223462 106 1972 1714.15602938876 257.843970611235 107 1496 1496.10047383321 -0.100473833209372 108 1481 1474.87825161099 6.12174838901292 109 1819 1742.32932847783 76.6706715221668 110 1479 1530.28048877917 -51.2804887791693 111 1635 1282.83604433472 352.163955665275 112 1511 1412.78048877917 98.2195112208307 113 1547 1495.11382211250 51.8861778874974 114 1388 1219.00271100139 168.997288998608 115 1958 1504.89159989028 453.10840010972 116 1390 1377.0773134939 12.9226865061003 117 1597 1497.5773134939 99.4226865061004 118 1842 1729.68842460501 112.311575394989 119 1396 1511.63286904946 -115.632869049455 120 1671 1490.41064682723 180.589353172767 121 1385 1757.86172369408 -372.861723694079 122 1632 1545.81288399542 86.187116004585 123 1313 1298.36843955097 14.6315604490293 124 1300 1428.31288399542 -128.312883995415 125 1431 1510.64621732875 -79.6462173287484 126 1398 1234.53510621764 163.464893782363 127 1198 1520.42399510653 -322.423995106526 128 1292 1392.60970871015 -100.609708710145 129 1434 1513.10970871015 -79.1097087101454 130 1660 1745.22081982126 -85.2208198212564 131 1837 1527.1652642657 309.834735734299 132 1455 1505.94304204348 -50.9430420434786 133 1315 1773.39411891032 -458.394118910325 134 1642 1561.34527921166 80.6547207883392 135 1069 1313.90083476722 -244.900834767216 136 1209 1443.84527921166 -234.845279211661 137 1586 1526.17861254499 59.8213874550059 138 1122 1250.06750143388 -128.067501433883 139 1063 1535.95639032277 -472.956390322772 140 1125 1408.14210392639 -283.142103926391 141 1414 1528.64210392639 -114.642103926391 142 1347 1760.75321503750 -413.753215037502 143 1403 1542.69765948195 -139.697659481947 144 1299 1521.47543725972 -222.475437259724 145 1547 1788.92651412657 -241.926514126571 146 1515 1576.87767442791 -61.8776744279066 147 1247 1329.43322998346 -82.4332299834622 148 1639 1459.37767442791 179.622325572093 149 1296 1541.71100776124 -245.71100776124 150 1063 1265.59989665013 -202.599896650129 151 1282 1551.48878553902 -269.488785539018 152 1365 1423.67449914264 -58.674499142637 153 1268 1544.17449914264 -276.174499142637 154 1532 1776.28561025375 -244.285610253748 155 1455 1558.23005469819 -103.230054698192 156 1393 1537.00783247597 -144.007832475970 157 1515 1804.45890934282 -289.458909342816 158 1510 1592.41006964415 -82.4100696441524 159 1225 1344.96562519971 -119.965625199708 160 1577 1474.91006964415 102.089930355848 161 1417 1557.24340297749 -140.243402977486 162 1224 1281.13229186637 -57.1322918663746 163 1693 1567.02118075526 125.978819244736 164 1633 1439.20689435888 193.793105641117 165 1639 1559.70689435888 79.2931056411173 166 1914 1791.81800546999 122.181994530006 167 1586 1573.76244991444 12.2375500855618 168 1552 1552.54022769222 -0.54022769221593 169 2081 1819.99130455906 261.008695440938 170 1500 1607.94246486040 -107.942464860398 171 1437 1360.49802041595 76.5019795840463 172 1470 1490.44246486040 -20.4424648603982 173 1849 1572.77579819373 276.224201806269 174 1387 1296.66468708262 90.3353129173796 175 1592 1582.55357597151 9.44642402849077 176 1589 1454.73928957513 134.260710424871 177 1798 1575.23928957513 222.760710424872 178 1935 1807.35040068624 127.649599313761 179 1887 1589.29484513068 297.705154869316 180 2027 1568.07262290846 458.927377091538 181 2080 1835.52369977531 244.476300224692 182 1556 1623.47486007664 -67.4748600766439 183 1682 1376.0304156322 305.969584367801 184 1785 1505.97486007664 279.025139923356 185 1869 1588.30819340998 280.691806590023 186 1781 1312.19708229887 468.802917701134 187 2082 1598.08597118776 483.914028812245 188 2570 1470.27168479137 1099.72831520863 189 1862 1590.77168479137 271.228315208626 190 1936 1822.88279590249 113.117204097515 191 1504 1604.82724034693 -100.827240346930 192 1765 1583.60501812471 181.394981875292 193 1607 1851.05609499155 -244.056094991554 194 1577 1639.00725529289 -62.0072552928897 195 1493 1391.56281084845 101.437189151555 196 1615 1521.50725529289 93.4927447071103 197 1700 1603.84058862622 96.159411373777 198 1335 1327.72947751511 7.27052248488804 199 1523 1613.618366404 -90.6183664040008 200 1623 1485.80408000762 137.19591999238 201 1540 1606.30408000762 -66.30408000762 202 1637 1838.41519111873 -201.415191118731 203 1524 1620.35963556318 -96.3596355631755 204 1419 1599.13741334095 -180.137413340953 205 1821 1866.5884902078 -45.5884902077993 206 1593 1654.53965050914 -61.5396505091355 207 1357 1407.09520606469 -50.0952060646911 208 1263 1537.03965050914 -274.039650509136 209 1750 1619.37298384247 130.627016157531 210 1405 1343.26187273136 61.7381272686423 211 1393 1629.15076162025 -236.150761620247 212 1639 1501.33647522387 137.663524776134 213 1679 1621.83647522387 57.1635247761342 214 1551 1853.94758633498 -302.947586334977 215 1744 1635.89203077942 108.107969220579 216 1429 1614.6698085572 -185.669808557199 217 1784 1882.12088542405 -98.1208854240451

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/1f08n1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/1f08n1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/2o8541227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/2o8541227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/34v2t1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/34v2t1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/4v3hl1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/4v3hl1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/5qhe61227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/5qhe61227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/6py3x1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/6py3x1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/777fv1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/777fv1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/8rsqv1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/8rsqv1227274584.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/9dyvw1227274584.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t1227274687aq8w798byc587zz/9dyvw1227274584.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) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

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Software written by Ed van Stee & Patrick Wessa

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