AMS 597: Statistical Computing

Introduction to statistical computing using R. This course introduces graduate students to some basic elements of statistical computing and computational statistics. Students are expected to know statistical concepts including ANOVA, regression analysis, etc before taking the course. This course is divided into two main parts. The first part covers R and SAS implementation of important statistical models. The second part covers computational statistics including numerical analysis, Monte Carlo methods, bootstrap, permutation, etc.

Required Textbooks:

1. Introductory Statistics with R by Peter Dalgaard, 2nd edition, 2008, Springer
2. Statistical Computing with R by Maria L. Rizzo, 2nd edition, 2019, Chapman & Hall/CRC

Supplementary Textbooks:

1. Modern Applied Statistics with S by W. N. Venables and B. D. Ripley, 4th edition, 2002, Springer
2. Computational Statistics by Geof H. Givens and Jennifer A. Hoeting, 2nd edition, 2012, Wiley
3. Elements of Computational Statistics by James E. Gentle, 2002, Springer
4. Applied Statistics and SAS Programming Language by Ronald P. Cody and Jeffrey K. Smith, 5th edition, 2005, Pearson

Learning Outcomes

1. Demonstrate skills of working with R in:
   • Engineering;
   • Biological sciences;
   • Finance.
2. Demonstrate skills with proficient usage of R for statistical analysis.
   • R basics: data types, data input/output, functional programming;
   • Descriptive statistics and graphics with R;
   • Advanced statistical modeling with R: one or two-sample tests, analysis of variance, linear models and generalized linear models.
3. Demonstrate understanding of computational statistics including numerical analysis, Monte Carlo methods, bootstrap and permutation; and usage of R to implement these methods.
4. Demonstrate skills of analyzing real-world problems with proper statistical tools (including methods and software packages), including introduction to Perl for high-throughput data.
5. Demonstrate understanding of the assumptions and interpretation of results from various statistical analysis.
   • Gain the ability to write suitable/sophisticated codes for analyzing real-world research problems;
   • Learn to write comprehensive analysis reports that is rigorous in statistics and yet understandable in layman’s terms.