Statistics 200: Statistical Analysis

Instructor: Ellen Fireman
Section: E58 (TR 11:00am - 12:20pm, 150 Animal Sciences Laboratory)
Section: F58 (TR 02:00pm - 03:20pm, 150 Animal Sciences Laboratory)

This course is a follow up to Stat 100 with 3 Main Goals.

1. Retaining the conceptual, intuitive approach of Stat 100 while learning a set of much more complex statistical methods. - That's a huge challenge. We try to tie them together with a few unifying themes rather than get bogged down in the tedious calculations and tiny differences between some of them. We build a unifying framework for general predictive models.
2. How can we determine whether the predictors are also causes? - Often the question we really care about is not just whether X predicts Y, but also whether X causes Y. Will intervening and making changes in X (without changing anything else) change Y? As we saw in Stat 100, trying to answer that question in the absence of a randomized controlled experiment involves sorting out a tangle of possible confounders and causal links. As we examine real data sets, we investigate how to evaluate causal claims.
3. Learning to use statistical software both to help us understand what the statistical methods are doing and to do the calculations for us. -
   
   a.We have a new updated, simple, point and click data program to let you compare the same data sets with a variety of methods to see how the results they give differ and why. We can get feel for which of the methods, which are usually all approximations to the real world, work better in which situations by analyzing the data using a variety of them. We focus on understanding the underlying concepts behind the methods so you can choose your methods wisely.
   
   b.Once you do understand the needed statistics, performing useful applied calculations usually requires more flexible software than our data program. This semester, we are now adding a component on using the R statistical language. We have bonus exercises in using R to acquire basic familiarity. For students who wish to become fluent in R programming, we now offer a two-hour R independent study.

JV Cunningham