Statistics S620 

Contact:  Andrew Womack ajwomack@indiana.edu 
Offered:  Spring, Every Year 
Class Days:  Tu, Th 
Capacity:  Combined enrollment for Spring 2012 was 20 students. 
Sequence:  Not part of a sequence. 
PreRequisites:  STAT S320/520 (Introduction to Statistics) and MATH M463 (Introduction of Probability Theory I). 
Algebra Required:  Not extensive. Used for basic matrix representations. Not much in homeworks or proofs. 
Calculus Required:  Some knowledge of integration and differentiation is needed. Understanding of probability distributions and some basic knowledge of expectations and variances is needed along with parametric representations of distributions. 
Contact Person for Authorization:  Instructor. 
Instructor:  Andrew Womack ajwomack@indiana.edu 
Days Per Week Offered:  Historically it has been taught TR in 75minute class meetings. It can be taught in the MWF format. No computer lab. 
Website:  To be constructed. 
Recommended followup classes:  This course allows students to take other classes that require some degree of knowledge in statistical inference. Examples are: S625 Nonparametric Statistics and S626 Bayesian Theory and Data Analysis. 
Syllabus:  No Syllabus Avaliable 
Keywords:  Mathematical statistics. Statistical Inference theory. Maximum likelihood estimation. Sufficiency. Bayesian ideas. Decision theory. 
Description:  Fundamental concepts and principles of data reduction and statistical inference, including the method of maximum likelihood, the method of least squares, and Bayesian inference. Theoretical justification of statistical procedures introduced in S320. 
Books:  DeGroot and Schervish, Probability and Statistics, 4th Edition (2011) 
Substantive Orientation:  Designed for students in graduate degree programs in the Department of Statistics. 
Statistical Orientation:  Statistical inference methodology. 
Applied/Theoretical:  This is mainly a theory course. Some data examples are considered. 
Software:  R 
How Software is Used:  Graphs of distributions. Calculations of probabilities and quantiles. Basic Monte Carlo random number simulations. 
Problem Sets:  Yes. 
Data Analysis:  Basic data examples. 
Presentation:  No. 
Exams:  Two midterms and a final exam. 
Comments:  S620 meets concurrently with S420, a required course for undergraduate statistics majors. 