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AMS 597, Statistical Computing

Introduction to statistical computing using R. 

3 credits, ABCF grading 

Text  Introductory Statistics with R (2nd ed.), by Peter, Dalgaard, Springer. ISBN  9780387790534 (recommended/optional text)

Modern Applied Statistics with S, by Venables, W.N. and Ripley, B.D, Springer. ISBN 9780387954578 (recommended/optional text)

Computational Statistics (2nd ed.), by Geof H. Givens and Jennifer A. Hoeting, Wiley. ISBN 9780470533314 (recommended/optional text)

Statistical Computing with R, by Maria L. Rizzo, CRC Press. ISBN 9781584885450 (recommended/optional text)

Spring Semester

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.

 

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