10.1.2 Linear Regression

# 10.1.2 Linear Regression - HOMEW UlQ K’ Blhol(1)1 l5,lb,n...

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Unformatted text preview: HOMEW UlQ K’ Blhol (1)1 l5,lb,n orgl‘tll (nm 0) . 10.1 Linear regression Deterministic model Probabilistic model y = 2x y =2x + random error First order probabilistic model y = Bo + 81 x + E y = dependent or response variable x = independent or predictor variable Elvl=Bo+l31x E = random error component Bo = y intercept B; = slope ofthe line; change in E(y) for a one unit increase in x 9.1mm / 10.2 Scatterplot ‘J Least squares line . Regression model 50 and Bl are parameters (unknown values). We estimate them with BB and E by minimizing SSE — 555 = zpt — (B; + 511)]: 55,, E=—Z_ SSxx .2 " v'C— maimed regression line error devmnaw 5i'9‘t A A " \d T Bo ‘ Bl X X least squares prediction equation linear model first order model Eta—73:2? Ex: The sample data comes from a business statistics class. Test 2 Score I Final Exam Score ‘3 62 88 76 43 73 42 94 61 70 86 82 82 72 89 62 75 56 97 80 84 78 83 76 68 zx= B‘lz 2x1= (0017 K -' 10.25 mi rams)? '1 mt, 91m omlo ’) v (M stair 23436 23144609 §=lﬂ 213-61233 Excel/22(0Iixmns,data,dmra a m clemegreﬁlah/labelg, residuals, standardized ran ea 5 9: ‘ll'mtlhrgvcert,detriment) 1.523CUM-8é timela’lialiw emucmx b , 3/!3/14 ...
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