LinearRegression - linear regresSion allOWS you to analyze...

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. .. The line is described by y - mx + b ,where m is the slope of the line .and b is the y-intercept. EXAMPLE: Suppose the Ghostbusters, INC.have recorded the following data regarding th number of ghostswhichhave been captured at various staffinglevels. L1Jf Ghostl captured 81 98 99 112 151 152 , of GW;wbusters 3 5 7 '. 8 11 12 linear regresSion allOWSyou to analyze one vanaDl~S n:li:lUUllMlll-'W1UldllUUlC1 The method is to peIform a least-squares linear regression which mjnjmizes th, sum of the squares of the deviationsof the actual data points from the straight lint of best fit. In practice, a plot of the data points is made and a line is constructed tha uniformly divides the data points. The least squares line is calculatedas : Supposeyou wanted to predict how many Ghostbusters \Wuld be needed in order to capture 200 ghosts. iuppose you wanted to predict how many ghosts might be captured by 15 ghostbusters. .. . REVENUES OF INTERNATIONAL PASSENGER AIR CARRIERSIBILLIONS)
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Unformatted text preview: flO VAt.:cIS 71.478 PRoa~. 0.0001 .. ,. .. . .-', .~ '"" . 0.9608 o .9,t, 7.t, .-...•. ". .. '.-: .' ...~<; . OF VARIANCE ..StiW~. 3.00214~l9 0.9'~O~~~ R-SQUARE AD. . J~-::lq ANALYSIS SUII"OF SQUA",ES 3&.025716&7 1.'l700 45.8 37. 49'5'7 l.as 0.2049407 4.335625 4.726902 SOURCt! OF MODEL 12 ERROR. 3 4 5 7 . CTOl'AL ROOT liSE g~t"EAN VARIABLE INTERCEP T. .. . 111 112 1'\3 114 115 11& 117 118 119 1110 OF 1. 1.. 1 1 1 1 1 1 t. 1 1 PARAMEtER ESTIMATE 't.0910'J167 .••. 0•. 01028472-0.0631319t,-0.8028t,722-0.2200625S1 0.070.2222, 0.5405069t, 1.12329167 .1d.~UUn 0.8816'1583 0.6469100;" PAR~METER ESTIMATES .S1'AN'DUb . ERROR .0 ~oU~B~U:. :. o • 146930t,4 0.14658264 ...8zuntm 0.14573452 a.1455175t, . O.~1.U3.3369 . O.l!tft'll3mi: 0.14506585 n.16IoQA,ii" .. T FtOltJ-fOI PARAIIETER"O P ROB ;> iT I 0.0001 'O~llJ)J)l 0.6701 O.'~2.~l g~ 0.Og07 0.0 01 BiBB:81 0.0001 ~ •..•. ........
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