Home » date » 2007 » Nov » 17 » attachments

WS6Q2season

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 05:11:04 -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/t1195301089doj7y0qt1c1n8eh.htm/, Retrieved Sat, 17 Nov 2007 13:05:00 +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] = + 4.99498279311949e-09 -6.95975523183266e-09x[t] -6.43509941741123e-09M1[t] -3.49990146873840e-09M2[t] + 2.18753708663261e-09M3[t] -8.50014163855136e-09M4[t] + 1.73432840810075e-14M5[t] -7.24999459062482e-09M6[t] -7.25002879726013e-09M7[t] -1.09998817684790e-08M8[t] -5.25009197090033e-09M9[t] -1.16250035627545e-08M10[t] -1.00000570374933e-09M11[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) 4.99498279311949e-09 38.232411 0 1 0.5 x -6.95975523183266e-09 33.828114 0 1 0.5 M1 -6.43509941741123e-09 53.778656 0 1 0.5 M2 -3.49990146873840e-09 53.73708 0 1 0.5 M3 2.18753708663261e-09 53.73708 0 1 0.5 M4 -8.50014163855136e-09 53.73708 0 1 0.5 M5 1.73432840810075e-14 53.73708 0 1 0.5 M6 -7.24999459062482e-09 53.73708 0 1 0.5 M7 -7.25002879726013e-09 53.73708 0 1 0.5 M8 -1.09998817684790e-08 53.73708 0 1 0.5 M9 -5.25009197090033e-09 53.73708 0 1 0.5 M10 -1.16250035627545e-08 53.73708 0 1 0.5 M11 -1.00000570374933e-09 53.73708 0 1 0.5 Multiple Linear Regression - Regression Statistics Multiple R 3.31667024428794e-11 R-squared 1.1000301509345e-21 Adjusted R-squared -0.0670391061452513 F-TEST (value) 1.64087830847729e-20 F-TEST (DF numerator) 12 F-TEST (DF denominator) 179 p-value 1 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 151.991415338604 Sum Squared Residuals 4135148.87025712 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -183.9235445 -1.44032696880458e-09 -183.923544498560 2 -177.0726091 1.4948398074921e-09 -177.072609101495 3 -228.6351091 7.1824501901574e-09 -228.635109107182 4 -237.4476091 -3.50439677276881e-09 -237.447609096496 5 -127.7601091 4.9948312152992e-09 -127.760109104995 6 -193.0101091 -2.25483631766110e-09 -193.010109097745 7 -220.6351091 -2.25480789595167e-09 -220.635109097745 8 -164.5101091 -6.00527982896892e-09 -164.510109093995 9 -268.3226091 -2.55170107266167e-10 -268.322609099745 10 -333.6976091 -6.6297047851549e-09 -333.697609093370 11 -34.26010911 3.99499811010173e-09 -34.260109113995 12 -154.8851091 4.99500174555578e-09 -154.885109104995 13 -97.74528053 -1.44015643854800e-09 -97.7452805285598 14 101.1056549 1.4948398074921e-09 101.105654898505 15 2.543154874 7.1825452252483e-09 2.54315486681745 16 -43.26934513 -3.50517126435079e-09 -43.2693451264948 17 -163.5818451 4.99488805871806e-09 -163.581845104995 18 -162.8318451 -2.25495000449882e-09 -162.831845097745 19 46.54315487 -2.25503526962711e-09 46.543154872255 20 26.66815487 -6.00500627001566e-09 26.668154876005 21 -107.1443451 -2.55113263847306e-10 -107.144345099745 22 42.48065487 -6.63001742395863e-09 42.48065487663 23 76.91815487 3.99499811010173e-09 76.918154866005 24 196.2931549 4.99494490213692e-09 196.293154895005 25 201.4329835 -1.44012801683857e-09 201.43298350144 26 12.28391886 1.49488776912676e-09 12.2839188585051 27 -0.278581137 7.18254411502528e-09 -0.278581144182544 28 42.90891886 -3.50518547520551e-09 42.9089188635052 29 87.59641886 4.99495911299164e-09 87.596418855005 30 84.34641886 -2.25493579364411e-09 84.346418862255 31 57.72141886 -2.25501395334504e-09 57.721418862255 32 173.8464189 -6.00505245529348e-09 173.846418906005 33 -185.9660811 -2.55141685556737e-10 -185.966081099745 34 47.65891886 -6.63001742395863e-09 47.65891886663 35 89.09641886 3.99499811010173e-09 89.096418856005 36 -68.52858114 4.99500174555578e-09 -68.528581144995 37 272.6112475 -1.44012801683857e-09 272.61124750144 38 146.4621829 1.4948398074921e-09 146.462182898505 39 162.8996829 7.18253545528569e-09 162.899682892817 40 10.08718285 -3.50517126435079e-09 10.0871828535052 41 279.7746829 4.99494490213692e-09 279.774682895005 42 212.5246829 -2.25497842620825e-09 212.524682902255 43 248.8996829 -2.25500684791768e-09 248.899682902255 44 -41.97531715 -6.00499561187462e-09 -41.975317143995 45 -5.787817149 -2.55100829349431e-10 -5.7878171487449 46 52.83718285 -6.63000321310392e-09 52.83718285663 47 274.2746829 3.99489863411873e-09 274.274682896005 48 414.6496829 4.99494490213692e-09 414.649682895005 49 310.7895114 -1.43995748658199e-09 310.78951140144 50 362.6404468 1.49492507262039e-09 362.640446798505 51 26.07794684 7.18253900799937e-09 26.0779468328175 52 403.2654468 -3.50519258063287e-09 403.265446803505 53 327.9529468 4.99494490213692e-09 327.952946795005 54 193.7029468 -2.25495000449882e-09 193.702946802255 55 317.0779468 -2.25497842620825e-09 317.077946802255 56 202.2029468 -6.00499561187462e-09 202.202946806005 57 321.3904468 -2.55170107266167e-10 321.390446800255 58 178.0154468 -6.63001742395863e-09 178.01544680663 59 16.45294684 3.99500876824277e-09 16.452946836005 60 -68.17205316 4.99500174555578e-09 -68.172053164995 61 -157.0322246 -1.44012801683857e-09 -157.03222459856 62 -76.18128917 1.4948398074921e-09 -76.1812891714948 63 -81.74378917 7.18252124443097e-09 -81.7437891771825 64 -134.5562892 -3.50516415892344e-09 -134.556289196495 65 77.13121083 4.99494490213692e-09 77.131210825005 66 199.8812108 -2.25495000449882e-09 199.881210802255 67 105.2562108 -2.2550210587724e-09 105.256210802255 68 198.3812108 -6.00502403358405e-09 198.381210806005 69 262.5687108 -2.55170107266167e-10 262.568710800255 70 196.1937108 -6.6300742673775e-09 196.19371080663 71 11.63121083 3.99499633374489e-09 11.631210826005 72 -145.9937892 4.99497332384635e-09 -145.993789204995 73 -166.8539606 -1.44012801683857e-09 -166.85396059856 74 -202.0030252 1.49492507262039e-09 -202.003025201495 75 43.43447482 7.18253545528569e-09 43.4344748128175 76 -113.3780252 -3.50519258063287e-09 -113.378025196495 77 -113.6905252 4.99495911299164e-09 -113.690525204995 78 -155.9405252 -2.25495000449882e-09 -155.940525197745 79 -210.5655252 -2.25509211304598e-09 -210.565525197745 80 -124.4405252 -6.0049814010199e-09 -124.440525193995 81 -64.25302518 -2.55099052992591e-10 -64.2530251797449 82 -298.6280252 -6.63004584566806e-09 -298.62802519337 83 -154.1905252 3.99495547753759e-09 -154.190525203995 84 23.18447482 4.99499464012843e-09 23.184474815005 85 -249.6756966 -1.44001433000085e-09 -249.67569659856 86 118.1752388 1.49482559663738e-09 118.175238798505 87 -180.3872612 7.18253545528569e-09 -180.387261207183 88 -79.19976119 -3.50519258063287e-09 -79.1997611864948 89 -81.51226119 4.99497332384635e-09 -81.512261194995 90 -246.7622612 -2.25495000449882e-09 -246.762261197745 91 -105.3872612 -2.25503526962711e-09 -105.387261197745 92 -319.2622612 -6.00499561187462e-09 -319.262261193995 93 -72.07476119 -2.55084842137876e-10 -72.0747611897449 94 -90.44976119 -6.6299890022492e-09 -90.44976118337 95 -80.01226119 3.99498389924702e-09 -80.012261193995 96 119.3627388 4.99495911299164e-09 119.362738795005 97 -53.49743261 -1.44011380598386e-09 -53.4974326085599 98 -114.6464972 1.49495349432982e-09 -114.646497201495 99 -155.2089972 7.18253545528569e-09 -155.208997207183 100 -50.02149721 -3.50518547520551e-09 -50.0214972064948 101 -196.3339972 4.99491648042749e-09 -196.333997204995 102 -14.58399721 -2.25494467542831e-09 -14.5839972077451 103 -82.20899721 -2.25500684791768e-09 -82.208997207745 104 17.91600279 -6.0049814010199e-09 17.9160027960050 105 -162.8964972 -2.55141685556737e-10 -162.896497199745 106 -132.2714972 -6.6299890022492e-09 -132.27149719337 107 -16.83399721 3.99499811010173e-09 -16.833997213995 108 81.54100279 4.99504437811993e-09 81.541002785005 109 275.6808314 -1.44012801683857e-09 275.68083140144 110 -32.46823322 1.49486822920153e-09 -32.4682332214949 111 17.96926678 7.18255321885408e-09 17.9692667728174 112 27.15676678 -3.5052032387739e-09 27.1567667835052 113 -123.1557332 4.99491648042749e-09 -123.155733204995 114 108.5942668 -2.25496421535354e-09 108.594266802255 115 67.96926678 -2.2550210587724e-09 67.969266782255 116 34.09426678 -6.00500982272933e-09 34.094266786005 117 -13.71823322 -2.55091947565234e-10 -13.7182332197449 118 -113.0932332 -6.63001742395863e-09 -113.09323319337 119 54.34426678 3.99500521552909e-09 54.344266776005 120 149.7192668 4.99500174555578e-09 149.719266795005 121 153.8590954 -1.44012801683857e-09 153.85909540144 122 -28.28996923 1.49487178191521e-09 -28.2899692314949 123 238.1475308 7.18259229870455e-09 238.147530792817 124 50.33503077 -3.50517836977815e-09 50.3350307735052 125 8.022530771 4.9949573366348e-09 8.02253076600504 126 -61.22746923 -2.25494289907147e-09 -61.227469227745 127 -140.8524692 -2.25503526962711e-09 -140.852469197745 128 -28.72746923 -6.00498495373358e-09 -28.727469223995 129 9.460030771 -2.55079513067358e-10 9.46003077125508 130 -121.9149692 -6.63000321310392e-09 -121.91496919337 131 41.52253077 3.99497679381966e-09 41.522530766005 132 115.8975308 4.99497332384635e-09 115.897530795005 133 27.03735936 -1.44011025327018e-09 27.0373593614401 134 -91.11170524 1.4948398074921e-09 -91.1117052414948 135 3.325794759 7.18254344889147e-09 3.32579475181746 136 -29.48670524 -3.50517836977815e-09 -29.4867052364948 137 -73.79920524 4.99497332384635e-09 -73.799205244995 138 50.95079476 -2.25492868821675e-09 50.9507947622549 139 -86.67420524 -2.2550210587724e-09 -86.674205237745 140 -9.54920524 -6.00498317737674e-09 -9.54920523399502 141 -66.36170524 -2.55099052992591e-10 -66.3617052397449 142 73.26329476 -6.63004584566806e-09 73.26329476663 143 -216.2992052 3.99495547753759e-09 -216.299205203995 144 -128.9242052 4.99500174555578e-09 -128.924205204995 145 -142.7843767 -1.44009959512914e-09 -142.78437669856 146 27.06655875 1.49488244005624e-09 27.0665587485051 147 60.50405875 7.18252834985833e-09 60.5040587428175 148 35.69155875 -3.50520679148758e-09 35.6915587535052 149 16.37905875 4.99495200756428e-09 16.3790587450050 150 -64.87094125 -2.25495000449882e-09 -64.870941247745 151 115.5040587 -2.25506369133655e-09 115.504058702255 152 -30.37094125 -6.0049814010199e-09 -30.370941243995 153 87.81655875 -2.55056420428446e-10 87.816558750255 154 205.4415587 -6.63015953250579e-09 205.44155870663 155 -64.12094125 3.99498389924702e-09 -64.120941253995 156 -322.7459413 4.99494490213692e-09 -322.745941304995 157 -139.6061127 -1.44009959512914e-09 -139.60611269856 158 35.24482274 1.49488244005624e-09 35.2448227385051 159 -4.317677263 7.1825496661404e-09 -4.31767727018255 160 17.86982274 -3.50518902791919e-09 17.8698227435052 161 2.557322737 4.9949573366348e-09 2.55732273200504 162 129.3073227 -2.25495000449882e-09 129.307322702255 163 -16.31767726 -2.25502816419976e-09 -16.3176772577450 164 164.8073227 -6.00505245529348e-09 164.807322706005 165 21.99482274 -2.55088394851555e-10 21.9948227402551 166 138.6198227 -6.63001742395863e-09 138.61982270663 167 87.05732274 3.99501232095645e-09 87.057322736005 168 51.43232274 4.99500885098314e-09 51.432322735005 169 -80.42784867 -1.44009959512914e-09 -80.4278486685599 170 -105.1918797 -5.46495471098751e-09 -105.191879694535 171 5.245620328 2.22809326544393e-10 5.24562032777719 172 68.43312033 -1.04649302556936e-08 68.4331203404649 173 -0.879379672 -1.96479210679001e-09 -0.879379670035208 174 -105.1293797 -9.21474452297844e-09 -105.129379690785 175 -82.75437967 -9.21482978810673e-09 -82.7543796607852 176 -132.6293797 -1.29647048652259e-08 -132.629379687035 177 102.5581203 -7.21487936061749e-09 102.558120307215 178 23.18312033 -1.35897550990194e-08 23.1831203435898 179 -180.3793797 -2.96472535410430e-09 -180.379379697035 180 -267.0043797 -1.96467908608611e-09 -267.004379698035 181 30.13544892 -8.39985858647196e-09 30.1354489283999 182 23.98638432 -5.46487655128658e-09 23.9863843254649 183 90.42388432 2.22783569370222e-10 90.4238843197772 184 31.61138432 -1.04649373611210e-08 31.6113843304649 185 81.29888432 -1.96482119463326e-09 81.2988843219648 186 25.04888432 -9.21468057413222e-09 25.0488843292147 187 -13.57611568 -9.21476939197419e-09 -13.5761156707852 188 33.54888432 -1.2964726181508e-08 33.5488843329647 189 140.7363843 -7.21482251719863e-09 140.736384307215 190 132.3613843 -1.35897266773100e-08 132.361384313590 191 94.79888432 -2.96481061923259e-09 94.7988843229648 192 4.173884316 -1.96472260682867e-09 4.17388431796472

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/112lg1195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/112lg1195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/2dzs21195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/2dzs21195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/31mxs1195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/31mxs1195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/4fzh91195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/4fzh91195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/56jls1195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/56jls1195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/617411195301458.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/617411195301458.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/72gok1195301459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/72gok1195301459.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/8ps6g1195301459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/8ps6g1195301459.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/9nyxr1195301459.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195301089doj7y0qt1c1n8eh/9nyxr1195301459.ps (open in new window)

Parameters:

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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