Home » date » 2007 » Nov » 17 » attachments

WS6Q2seasontrend

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 17 Nov 2007 05:13:00 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm.htm/, Retrieved Sat, 17 Nov 2007 13:07:17 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-183,9235445 0 -177,0726091 0 -228,6351091 0 -237,4476091 0 -127,7601091 0 -193,0101091 0 -220,6351091 0 -164,5101091 0 -268,3226091 0 -333,6976091 0 -34,26010911 0 -154,8851091 0 -97,74528053 0 101,1056549 0 2,543154874 0 -43,26934513 0 -163,5818451 0 -162,8318451 0 46,54315487 0 26,66815487 0 -107,1443451 0 42,48065487 0 76,91815487 0 196,2931549 0 201,4329835 0 12,28391886 0 -0,278581137 0 42,90891886 0 87,59641886 0 84,34641886 0 57,72141886 0 173,8464189 0 -185,9660811 0 47,65891886 0 89,09641886 0 -68,52858114 0 272,6112475 0 146,4621829 0 162,8996829 0 10,08718285 0 279,7746829 0 212,5246829 0 248,8996829 0 -41,97531715 0 -5,787817149 0 52,83718285 0 274,2746829 0 414,6496829 0 310,7895114 0 362,6404468 0 26,07794684 0 403,2654468 0 327,9529468 0 193,7029468 0 317,0779468 0 202,2029468 0 321,3904468 0 178,0154468 0 16,45294684 0 -68,17205316 0 -157,0322246 0 -76,18128917 0 -81,74378917 0 -134,5562892 0 77,13121083 0 199,8812108 0 105,2562108 0 198,3812108 0 262,5687108 0 196,1937108 0 11,63121083 0 -145,9937892 0 -166,8539606 0 -202,0030252 0 43,43447482 0 -113,3780252 0 -113,6905252 0 -155,9405252 0 -210,5655252 0 -124,4405252 0 -64,25302518 0 -298,6280252 0 -154,1905252 0 23,18447482 0 -249,6756966 0 118,1752388 0 -180,3872612 0 -79,19976119 0 -81,51226119 0 -246,7622612 0 -105,3872612 0 -319,2622612 0 -72,07476119 0 -90,44976119 0 -80,01226119 0 119,3627388 0 -53,49743261 0 -114,6464972 0 -155,2089972 0 -50,02149721 0 -196,3339972 0 -14,58399721 0 -82,20899721 0 17,91600279 0 -162,8964972 0 -132,2714972 0 -16,83399721 0 81,54100279 0 275,6808314 0 -32,46823322 0 17,96926678 0 27,15676678 0 -123,1557332 0 108,5942668 0 67,96926678 0 34,09426678 0 -13,71823322 0 -113,0932332 0 54,34426678 0 149,7192668 0 153,8590954 0 -28,28996923 0 238,1475308 0 50,33503077 0 8,022530771 0 -61,22746923 0 -140,8524692 0 -28,72746923 0 9,460030771 0 -121,9149692 0 41,52253077 0 115,8975308 0 27,03735936 0 -91,11170524 0 3,325794759 0 -29,48670524 0 -73,79920524 0 50,95079476 0 -86,67420524 0 -9,54920524 0 -66,36170524 0 73,26329476 0 -216,2992052 0 -128,9242052 0 -142,7843767 0 27,06655875 0 60,50405875 0 35,69155875 0 16,37905875 0 -64,87094125 0 115,5040587 0 -30,37094125 0 87,81655875 0 205,4415587 0 -64,12094125 0 -322,7459413 0 -139,6061127 0 35,24482274 0 -4,317677263 0 17,86982274 0 2,557322737 0 129,3073227 0 -16,31767726 0 164,8073227 0 21,99482274 0 138,6198227 0 87,05732274 0 51,43232274 0 -80,42784867 0 -105,1918797 1 5,245620328 1 68,43312033 1 -0,879379672 1 -105,1293797 1 -82,75437967 1 -132,6293797 1 102,5581203 1 23,18312033 1 -180,3793797 1 -267,0043797 1 30,13544892 1 23,98638432 1 90,42388432 1 31,61138432 1 81,29888432 1 25,04888432 1 -13,57611568 1 33,54888432 1 140,7363843 1 132,3613843 1 94,79888432 1 4,173884316 1

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 1.21654431132889e-08 + 6.88735776348077e-10x[t] -6.8334583240873e-09M1[t] -4.29661928209056e-09M2[t] + 1.47049105461567e-09M3[t] -9.1375158892331e-09M4[t] -5.57685126062429e-10M5[t] -7.72802527863611e-09M6[t] -7.64838770393621e-09M7[t] -1.13185688938199e-08M8[t] -5.48910731490598e-09M9[t] -1.17843471254250e-08M10[t] -1.07967748508455e-09M11[t] -7.9671781335216e-11t + 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) 1.21654431132889e-08 44.029939 0 1 0.5 x 6.88735776348077e-10 41.037226 0 1 0.5 M1 -6.8334583240873e-09 53.942919 0 1 0.5 M2 -4.29661928209056e-09 53.941479 0 1 0.5 M3 1.47049105461567e-09 53.931287 0 1 0.5 M4 -9.1375158892331e-09 53.922166 0 1 0.5 M5 -5.57685126062429e-10 53.914117 0 1 0.5 M6 -7.72802527863611e-09 53.907141 0 1 0.5 M7 -7.64838770393621e-09 53.901237 0 1 0.5 M8 -1.13185688938199e-08 53.896405 0 1 0.5 M9 -5.48910731490598e-09 53.892648 0 1 0.5 M10 -1.17843471254250e-08 53.889963 0 1 0.5 M11 -1.07967748508455e-09 53.888353 0 1 0.5 t -7.9671781335216e-11 0.240551 0 1 0.5 Multiple Linear Regression - Regression Statistics Multiple R 4.14282819785689e-11 R-squared 1.71630254769581e-21 Adjusted R-squared -0.0730337078651686 F-TEST (value) 2.35001425761427e-20 F-TEST (DF numerator) 13 F-TEST (DF denominator) 178 p-value 1 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 152.417759557116 Sum Squared Residuals 4135148.87025712 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -183.9235445 5.25224663761037e-09 -183.923544505252 2 -177.0726091 7.70918973103107e-09 -177.072609107709 3 -228.6351091 1.33968569571152e-08 -228.635109113397 4 -237.4476091 2.70995315077016e-09 -237.44760910271 5 -127.7601091 1.12092806148212e-08 -127.760109111209 6 -193.0101091 3.95957044929673e-09 -193.010109103960 7 -220.6351091 3.95951360587787e-09 -220.635109103960 8 -164.5101091 2.09183781407773e-10 -164.510109100209 9 -268.3226091 5.95935034652939e-09 -268.322609105959 10 -333.6976091 -4.15298018197063e-10 -333.697609099585 11 -34.26010911 1.02093764553501e-08 -34.2601091202094 12 -154.8851091 1.12094653559325e-08 -154.885109111209 13 -97.74528053 4.29619717579044e-09 -97.7452805342962 14 101.1056549 6.75311184750171e-09 101.105654893247 15 2.543154874 1.24408705559631e-08 2.54315486155913 16 -43.26934513 1.75317182993240e-09 -43.2693451317532 17 -163.5818451 1.02532737855654e-08 -163.581845110253 18 -162.8318451 3.00346414405794e-09 -162.831845103003 19 46.54315487 3.00328650837400e-09 46.5431548669967 20 26.66815487 -7.46641859450392e-10 26.6681548707466 21 -107.1443451 5.00320140872645e-09 -107.144345105003 22 42.48065487 -1.37166722424809e-09 42.4806548713717 23 76.91815487 9.25328436096606e-09 76.9181548607467 24 196.2931549 1.02533306289843e-08 196.293154889747 25 201.4329835 3.34020455738937e-09 201.43298349666 26 12.28391886 5.79717074344899e-09 12.2839188542028 27 -0.278581137 1.14848128784928e-08 -0.278581148484813 28 42.90891886 7.9708684097568e-10 42.9089188592029 29 87.59641886 9.2972527454549e-09 87.5964188507027 30 84.34641886 2.04735783881915e-09 84.3464188579526 31 57.72141886 2.04725125740879e-09 57.7214188579527 32 173.8464189 -1.70268776855664e-09 173.846418901703 33 -185.9660811 4.04719457947067e-09 -185.966081104047 34 47.65891886 -2.32775931863216e-09 47.6589188623278 35 89.09641886 8.29723489914613e-09 89.0964188517028 36 -68.52858114 9.29729537801904e-09 -68.5285811492973 37 272.6112475 2.38418351727887e-09 272.611247497616 38 146.4621829 4.84106976728071e-09 146.462182895159 39 162.8996829 1.05287654150743e-08 162.899682889471 40 10.08718285 -1.58959068130571e-10 10.0871828501590 41 279.7746829 8.34120328363497e-09 279.774682891659 42 212.5246829 1.09130837699922e-09 212.524682898909 43 248.8996829 1.09136522041808e-09 248.899682898909 44 -41.97531715 -2.65875854665865e-09 -41.9753171473412 45 -5.787817149 3.0910891624103e-09 -5.78781715209109 46 52.83718285 -3.28381588587945e-09 52.8371828532838 47 274.2746829 7.34121385903563e-09 274.274682892659 48 414.6496829 8.34120328363497e-09 414.649682891659 49 310.7895114 1.42807721204008e-09 310.789511398572 50 362.6404468 3.88507714887965e-09 362.640446796115 51 26.07794684 9.57269108425862e-09 26.0779468304273 52 403.2654468 -1.11504050437361e-09 403.265446801115 53 327.9529468 7.38515382181504e-09 327.952946792615 54 193.7029468 1.35287336888723e-10 193.702946799865 55 317.0779468 1.35230493469862e-10 317.077946799865 56 202.2029468 -3.61475827048707e-09 202.202946803615 57 321.3904468 2.13503881241195e-09 321.390446797865 58 178.0154468 -4.23980850428052e-09 178.015446804240 59 16.45294684 6.3851324227926e-09 16.4529468336149 60 -68.17205316 7.38516803266975e-09 -68.1720531673852 61 -157.0322246 4.7202775022015e-10 -157.032224600472 62 -76.18128917 2.92894242193142e-09 -76.181289172929 63 -81.74378917 8.61660964801558e-09 -81.7437891786166 64 -134.5562892 -2.07108996619354e-09 -134.556289197929 65 77.13121083 6.42906172743096e-09 77.131210823571 66 199.8812108 -8.20818968350068e-10 199.881210800821 67 105.2562108 -8.2093265518779e-10 105.256210800821 68 198.3812108 -4.57089299743529e-09 198.381210804571 69 262.5687108 1.17898935059202e-09 262.568710798821 70 196.1937108 -5.19594323122874e-09 196.193710805196 71 11.63121083 5.42904565747904e-09 11.6312108245710 72 -145.9937892 6.42907593828568e-09 -145.993789206429 73 -166.8539606 -4.8402171159978e-10 -166.853960599516 74 -202.0030252 1.97292138182092e-09 -202.003025201973 75 43.43447482 7.6605530807683e-09 43.4344748123394 76 -113.3780252 -3.0271678497229e-09 -113.378025196973 77 -113.6905252 5.47302647646575e-09 -113.690525205473 78 -155.9405252 -1.77689685187943e-09 -155.940525198223 79 -210.5655252 -1.77701053871715e-09 -210.565525198223 80 -124.4405252 -5.52694245925522e-09 -124.440525194473 81 -64.25302518 2.22939888772089e-10 -64.253025180223 82 -298.6280252 -6.15193584962981e-09 -298.628025193848 83 -154.1905252 4.47300863015698e-09 -154.190525204473 84 23.18447482 5.47305489817518e-09 23.1844748145269 85 -249.6756966 -1.44015643854800e-09 -249.67569659856 86 118.1752388 1.01680086572742e-09 118.175238798983 87 -180.3872612 6.70453914608515e-09 -180.387261206705 88 -79.19976119 -3.98320310068812e-09 -79.1997611860168 89 -81.51226119 4.51697701464582e-09 -81.512261194517 90 -246.7622612 -2.73291789198993e-09 -246.762261197267 91 -105.3872612 -2.73310263310123e-09 -105.387261197267 92 -319.2622612 -6.48299192107515e-09 -319.262261193517 93 -72.07476119 -7.33109573047841e-10 -72.0747611892669 94 -90.44976119 -7.1080421548686e-09 -90.449761182892 95 -80.01226119 3.51695916833705e-09 -80.012261193517 96 119.3627388 4.5169628037911e-09 119.362738795483 97 -53.49743261 -2.39613484609436e-09 -53.4974326076039 98 -114.6464972 6.08224581810646e-11 -114.646497200061 99 -155.2089972 5.74846126255579e-09 -155.208997205748 100 -50.02149721 -4.9392596679354e-09 -50.0214972050607 101 -196.3339972 3.56087070940703e-09 -196.333997203561 102 -14.58399721 -3.6890277499424e-09 -14.5839972063110 103 -82.20899721 -3.68912367321172e-09 -82.2089972063109 104 17.91600279 -7.4390555937498e-09 17.9160027974391 105 -162.8964972 -1.6891874565772e-09 -162.896497198311 106 -132.2714972 -8.06412003839796e-09 -132.271497191936 107 -16.83399721 2.56090260108977e-09 -16.8339972125609 108 81.54100279 3.56092755282589e-09 81.541002786439 109 275.6808314 -3.35222694047843e-09 275.680831403352 110 -32.46823322 -8.95269636203011e-10 -32.4682332191047 111 17.96926678 4.79240114259483e-09 17.9692667752076 112 27.15676678 -5.8953482096058e-09 27.1567667858953 113 -123.1557332 2.60477861502295e-09 -123.155733202605 114 108.5942668 -4.64507365904865e-09 108.594266804645 115 67.96926678 -4.6452157675958e-09 67.9692667846452 116 34.09426678 -8.39513347727916e-09 34.0942667883951 117 -13.71823322 -2.64524757653817e-09 -13.7182332173548 118 -113.0932332 -9.0201837110726e-09 -113.093233190980 119 54.34426678 1.60481050670569e-09 54.3442667783952 120 149.7192668 2.60487809100596e-09 149.719266797395 121 153.8590954 -4.30827640229836e-09 153.859095404308 122 -28.28996923 -1.85130843988190e-09 -28.2899692281487 123 238.1475308 3.83636233891593e-09 238.147530796164 124 50.33503077 -6.85140122413941e-09 50.3350307768514 125 8.022530771 1.64876645669665e-09 8.02253076935123 126 -61.22746923 -5.60113022629594e-09 -61.2274692243989 127 -140.8524692 -5.6012368077063e-09 -140.852469194399 128 -28.72746923 -9.3511651755307e-09 -28.7274692206488 129 9.460030771 -3.60132013099701e-09 9.46003077460132 130 -121.9149692 -9.97621896203782e-09 -121.914969190024 131 41.52253077 6.48739728603687e-10 41.5225307693513 132 115.8975308 1.64880020747660e-09 115.897530798351 133 27.03735936 -5.26435073311404e-09 27.0373593652644 134 -91.11170524 -2.80742540326173e-09 -91.1117052371926 135 3.325794759 2.8802675799966e-09 3.32579475611973 136 -29.48670524 -7.80743647510462e-09 -29.4867052321926 137 -73.79920524 6.92722323947237e-10 -73.7992052406927 138 50.95079476 -6.55720100439794e-09 50.9507947665572 139 -86.67420524 -6.55731469123566e-09 -86.6742052334427 140 -9.54920524 -1.03072270718485e-08 -9.54920522969277 141 -66.36170524 -4.55736426374642e-09 -66.3617052354426 142 73.26329476 -1.09323678998408e-08 73.2632947709324 143 -216.2992052 -3.07267100652098e-10 -216.299205199693 144 -128.9242052 6.92722323947237e-10 -128.924205200693 145 -142.7843767 -6.22037532593822e-09 -142.784376693780 146 27.06655875 -3.76343223251752e-09 27.0665587537634 147 60.50405875 1.92419946642985e-09 60.5040587480758 148 35.69155875 -8.76353567491606e-09 35.6915587587635 149 16.37905875 -2.63348454154766e-10 16.3790587502633 150 -64.87094125 -7.51326467707258e-09 -64.8709412424867 151 115.5040587 -7.51336415305559e-09 115.504058707513 152 -30.37094125 -1.12632818627389e-08 -30.3709412387367 153 87.81655875 -5.51339951471164e-09 87.8165587555134 154 205.4415587 -1.18883463073871e-08 205.441558711888 155 -64.12094125 -1.26334498418146e-09 -64.1209412487367 156 -322.7459413 -2.63298716163263e-10 -322.745941299737 157 -139.6061127 -7.17642478775815e-09 -139.606112692824 158 35.24482274 -4.7194887997648e-09 35.2448227447195 159 -4.317677263 9.68151780966764e-10 -4.31767726396815 160 17.86982274 -9.71957447859495e-09 17.8698227497196 161 2.557322737 -1.21941035047257e-09 2.55732273821941 162 129.3073227 -8.4692999280378e-09 129.307322708469 163 -16.31767726 -8.46943137844391e-09 -16.3176772515306 164 164.8073227 -1.22193455354136e-08 164.807322712219 165 21.99482274 -6.46948805638203e-09 21.9948227464695 166 138.6198227 -1.28443673474976e-08 138.619822712844 167 87.05732274 -2.21940865685610e-09 87.0573227422194 168 51.43232274 -1.21939081054734e-09 51.4323227412194 169 -80.42784867 -8.1324884604328e-09 -80.4278486618675 170 -105.1918797 -4.98685892580397e-09 -105.191879695013 171 5.245620328 7.00855373736431e-10 5.24562032729914 172 68.43312033 -9.98689131392894e-09 68.4331203399869 173 -0.879379672 -1.48672829602958e-09 -0.879379670513272 174 -105.1293797 -8.73670558121376e-09 -105.129379691263 175 -82.75437967 -8.73676242463262e-09 -82.7543796612632 176 -132.6293797 -1.24866801343160e-08 -132.629379687513 177 102.5581203 -6.7368404188528e-09 102.558120306737 178 23.18312033 -1.31117090518273e-08 23.1831203431117 179 -180.3793797 -2.48667220148491e-09 -180.379379697513 180 -267.0043797 -1.48662593346671e-09 -267.004379698513 181 30.13544892 -8.39983016476253e-09 30.1354489283998 182 23.98638432 -5.94286575505976e-09 23.9863843259429 183 90.42388432 -2.55241161539743e-10 90.4238843202552 184 31.61138432 -1.09429336703215e-08 31.6113843309429 185 81.29888432 -2.44281750383379e-09 81.2988843224428 186 25.04888432 -9.69269819961482e-09 25.0488843296927 187 -13.57611568 -9.69279057017047e-09 -13.5761156703072 188 33.54888432 -1.34427153852812e-08 33.5488843334427 189 140.7363843 -7.69284724810859e-09 140.736384307693 190 132.3613843 -1.40677229865105e-08 132.361384314068 191 94.79888432 -3.44282113928784e-09 94.7988843234428 192 4.173884316 -2.44274023231128e-09 4.17388431844274

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/12ur11195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/12ur11195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/28c451195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/28c451195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/3g0ko1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/3g0ko1195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/4lqpw1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/4lqpw1195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/531881195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/531881195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/6q8di1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/6q8di1195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/7pxrn1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/7pxrn1195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/8di5d1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/8di5d1195301574.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/9ysmb1195301574.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301227acg4kz4zjkjpevm/9ysmb1195301574.ps (open in new window)

Parameters:

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')

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by