HW Solutions Stat 40

HW Solutions Stat 40 - Chapter 14 LOGISTIC REGRESSION,...

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Chapter 14 LOGISTIC REGRESSION, POISSON REGRESSION,AND GENERALIZED LINEAR MODELS 14.5. a. E { Y } = [1 + exp( 20 + . 2 X )] 1 b. 100 c. X = 125 : π = . 006692851, π/ (1 π )= . 006737947 X = 126 : π = . 005486299, (1 π . 005516565 005516565 /. 006737947 = . 81873 = exp( . 2) 14.7. a. b 0 = 4 . 80751, b 1 = . 12508, ˆ π = [1 + exp(4 . 80751 . 12508 X )] 1 c. 1.133 d. .5487 e. 47.22 14.11. a. j : 123456 p j : . 144 . 206 . 340 . 592 . 812 . 898 b. b 0 = 2 . 07656, b 1 = . 13585 ˆ π = [1 + exp(2 . 07656 . 13585 X )] 1 d. 1.1455 e. .4903 f. 23.3726 14.14. a.
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