University of Waterloo (UW ) • STAT
Latest uploads for STAT at University of Waterloo (UW ). Looking for STAT notes at University of Waterloo (UW )? We have lots of notes, study guides and study notes available for STAT at University of Waterloo (UW ).
-
2
- 0
- 0
Courses STAT at University of Waterloo (UW )
Notes available for the following courses of STAT at University of Waterloo (UW )
-
STAT 431 2
Latest notes & summaries University of Waterloo (UW ) • STAT
Stat 431 ASSIGNMENT 3 SOLUTIONS 1. (a) Given the tolerance distribution, the probability of response the dose x is π(x) = Zx −∞ exp((u − µ)/δ) δ(1 + exp((u − µ)/δ) 2 du = exp((x − µ)/δ) 1 + exp((x − µ)/δ) ⇒ log π(x) 1 − π(x) = − µ δ + 1 δ x This implies that it is most appropriate to choose a logistic link function. (b) The binary logistic regression model is log π(x) 1 − π(x) = β0 + β1x where β0 = − µ δ and β1 = 1 δ . ...
- Exam (elaborations)
- • 7 pages's •
-
University of Waterloo•STAT 431
Preview 2 out of 7 pages
Stat 431 ASSIGNMENT 3 SOLUTIONS 1. (a) Given the tolerance distribution, the probability of response the dose x is π(x) = Zx −∞ exp((u − µ)/δ) δ(1 + exp((u − µ)/δ) 2 du = exp((x − µ)/δ) 1 + exp((x − µ)/δ) ⇒ log π(x) 1 − π(x) = − µ δ + 1 δ x This implies that it is most appropriate to choose a logistic link function. (b) The binary logistic regression model is log π(x) 1 − π(x) = β0 + β1x where β0 = − µ δ and β1 = 1 δ . ...
Stat 431 Assignment 3 solutions 1. [ 12 marks] (a) We start by fitting the interaction model logit(πi) = β0 + β1xi1 + β2xi2 + β3xi1xi2 The Wald-test for H0 : β3 (coefficient of interaction term) gives a p−value << 0.05, hence we can not drop the interaction term. Since we can not further simplify this model, it is the best logit model we can fit. [2] cells<-("", header=T) cells$resp<-cbind(cells$Cell, 200-cells$Cell) fit1<-glm(resp~tnf+ifn+tnf*ifn, family=binomi...
- Exam (elaborations)
- • 12 pages's •
-
University of Waterloo•STAT 431
Preview 2 out of 12 pages
Stat 431 Assignment 3 solutions 1. [ 12 marks] (a) We start by fitting the interaction model logit(πi) = β0 + β1xi1 + β2xi2 + β3xi1xi2 The Wald-test for H0 : β3 (coefficient of interaction term) gives a p−value << 0.05, hence we can not drop the interaction term. Since we can not further simplify this model, it is the best logit model we can fit. [2] cells<-("", header=T) cells$resp<-cbind(cells$Cell, 200-cells$Cell) fit1<-glm(resp~tnf+ifn+tnf*ifn, family=binomi...