18:14:56 From Ronald Neath to Everyone : The first is and end-of-chapter
18:15:03 From Ronald Neath to Everyone : problem from Gelman et al
18:15:31 From Ronald Neath to Everyone : It’s chapter 9
18:15:32 From Ronald Neath to Everyone : and no,
18:15:36 From Ronald Neath to Everyone : we didn’t study chapter 9
18:15:42 From Ronald Neath to Everyone : However,
18:15:49 From Ronald Neath to Everyone : this is a straight-up
18:15:52 From Ronald Neath to Everyone : probability exercise
18:15:58 From Ronald Neath to Everyone : It could’ve been a problem
18:16:05 From Ronald Neath to Everyone : in a 4203/5203
18:16:07 From Ronald Neath to Everyone : type course
18:16:33 From Ronald Neath to Everyone : It’s just application of
18:16:37 From Ronald Neath to Everyone : Bayes’ rule
18:16:40 From Ronald Neath to Everyone : and expected values
18:17:04 From Ronald Neath to Everyone : Problem 2
18:17:09 From Ronald Neath to Everyone : is also and end-of-chapter
18:17:14 From Ronald Neath to Everyone : problem from Gelman et al
18:17:19 From Ronald Neath to Everyone : Chapter 14 on regression
18:17:27 From Ronald Neath to Everyone : but BE CAREFUL!
18:17:39 From Ronald Neath to Everyone : You have to key in the data yourself
18:18:38 From Ronald Neath to Everyone : The values entered
18:18:44 From Ronald Neath to Everyone : the responses
18:18:49 From Ronald Neath to Everyone : the model we fit
18:18:52 From Ronald Neath to Everyone : assumes their logs are
18:18:57 From Ronald Neath to Everyone : Normally distributed
18:19:30 From Ronald Neath to Everyone : This is a regression problem
18:19:41 From Ronald Neath to Everyone : with two categorical predictor variables
18:19:46 From Ronald Neath to Everyone : one of them has three levels
18:19:49 From Ronald Neath to Everyone : (that’s county)
18:19:56 From Ronald Neath to Everyone : so create two indicator variables
18:19:59 From Ronald Neath to Everyone : for county
18:20:21 From Ronald Neath to Everyone : and then one indicator variable
18:20:25 From Ronald Neath to Everyone : for the other categorical predictor
18:20:33 From Ronald Neath to Everyone : which is “basement versus first floor”
18:20:44 From Ronald Neath to Everyone : create an indicator variable
18:20:48 From Ronald Neath to Everyone : for “first-floor”
18:20:57 From Ronald Neath to Everyone : that’s 1 for the starred cases
18:20:59 From Ronald Neath to Everyone : and 0 for the others
18:21:09 From Ronald Neath to Everyone : The regression model you’re to fit
18:21:11 From Ronald Neath to Everyone : has the
18:21:14 From Ronald Neath to Everyone : county effect
18:21:15 From Ronald Neath to Everyone : and
18:21:23 From Ronald Neath to Everyone : first-floor vs basement effect
18:21:26 From Ronald Neath to Everyone : but not an interaction
18:21:47 From Ronald Neath to Everyone : There are 4 “beta” parameters
18:22:29 From Ronald Neath to Everyone : The third problem
18:22:33 From Ronald Neath to Everyone : is a
18:22:37 From Ronald Neath to Everyone : Bayesian logistic regression
18:23:52 From Ronald Neath to Everyone : You’re going to fit
18:23:59 From Ronald Neath to Everyone : this Bayesian logistic regression model
18:24:05 From Ronald Neath to Everyone : Using Stan
18:24:07 From Ronald Neath to Everyone : (?)
18:24:25 From Ronald Neath to Everyone : Do model checks
18:24:32 From Ronald Neath to Everyone : based on posterior predictive distributions
18:24:38 From Ronald Neath to Everyone : Part (c):
18:24:46 From Ronald Neath to Everyone : more stuff related to the posterior
18:24:50 From Ronald Neath to Everyone : predictive distribution
18:26:54 From Ronald Neath to Everyone : I will not hold office hours
18:26:59 From Ronald Neath to Everyone : You may email me
18:27:08 From Ronald Neath to Everyone : I will respond within 24 hours
18:27:12 From Ronald Neath to Everyone : Note to everybody:
18:27:29 From Ronald Neath to Everyone : Check the Courseworks announcements
18:27:35 From Ronald Neath to Everyone : regularly over the next 7 days