chapter 1

Chapter 12: Simple Linear Regression and Correlation19. N = 14, = 3300x , y = 5010 , x 2 = 913, 750 , Σ 2 i= 2,207, 100Σ iΣ iΣiΣ x y i i= 1,413,5003,256,000a. βˆ1= = 1. 71143233 , βˆ0 = 551905431,902,500−45.y = −45 .5519 + 1. 7114x .ˆ225=b. µ = −45.5519+ 1.7114( 225) 339. 51Y⋅c. Estimated expected change = −50β1= −85.57ˆy ,, so we use the equationd. No, the value 500 is outside the range of x values for which observations were available(the danger of extrapolation).20.a. βˆ0= . 3651, βˆ1= . 9668b. .8485c. σ ˆ = . 1932d. SST = 1.4533, 71.7% of this variation can be explained by the model. Note:SSR 1.0427= = .717SST 1.4533which matches R-squared on output.21.a. The summary statistics can easily be verified using Minitab or Excel, etc.491.4b. βˆ1= = . 66034186 , βˆ0 = 18247148744.16−2.ˆc. predicted y = β + β ( 15) 7. 72ˆˆ0 1=µ ˆ Y ⋅15 0 1=d. = β + β ( 15) 7. 72ˆ363