This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Chapter 13: Introduction to Multiple Regression CHAPTER 13 13.1 (a) Holding constant the effect of X 2 , for each additional unit of X 1 the response variable Y is expected to increase on average by 5 units. Holding constant the effect of X 1 , for each additional unit of X 2 the response variable Y is expected to increase on average by 3 units. (b) The Y intercept 10 estimates the expected value of Y if X 1 and X 2 are both 0. (c) 60% of the variation in Y can be explained or accounted for by the variation in X 1 and the variation in X 2 . 13.2 (a) Holding constant the effect of X 2 , for each additional unit of X 1 the response variable Y is expected to decrease on average by 2 units. Holding constant the effect of X 1 , for each additional unit of X 2 the response variable Y is expected to increase on average by 7 units. (b) The Y intercept 50 estimates the value of Y if X 1 and X 2 are both 0. (c) 40% of the variation in Y can be explained or accounted for by the variation in X 1 and the variation in X 2 . 13.3 (a) 2 1 60484 . 79116 . 02686 . ˆ X X Y + + = (b) For a given measurement of the change in impact properties over time, each increase of one unit in forefoot impact absorbing capability is estimated to result in an average increase in the longterm ability to absorb shock of 0.79116 units. For a given forefoot impact absorbing capability, each increase of one unit in measurement of the change in impact properties over time is estimated to result in an average increase in the longterm ability to absorb shock of 0.60484 units. (c) 9421 . 38473 . 13 / 6102 . 12 / 2 12 . = = = SST SSR r Y . So, 94.21% of the variation in the longterm ability to absorb shock can be explained by variation in forefoot absorbing capability and variation in midsole impact. (d) 93245 . 1 2 15 1 15 ) 9421 . 1 ( 1 1 1 ) 1 ( 1 2 12 . 2 =  =  = p n n r r Y adj 13.4 (a) 68% of the total variability in team performance can be explained by team skills after adjusting for the number of predictors and sample size. 78% of the total variability in team performance can be explained by clarity in expectation after adjusting for the number of predictors and sample size. 97% of the total variability in team performance can be explained by both team skills and clarity in expectations after adjusting for the number of predictors and sample size. (b) Model 3 is the best predictor of team performance since it has the highest adjusted r 2 . 64 Chapter 13: Introduction to Multiple Regression 13.5 (a) 1 2 ˆ 2.72825 0.047114 0.011947 Y X X =  + + (b) For a given number of orders, each increase of $1000 in sales is expected to result in an estimated average increase in distribution cost of $47.114. For a given amount of sales, each increase of one order is expected to result in an estimated average increase in distribution cost of $11.95....
View
Full
Document
This homework help was uploaded on 04/09/2008 for the course ENGR, STAT 320, 262, taught by Professor Harris during the Spring '08 term at Purdue.
 Spring '08
 Harris

Click to edit the document details