<p>Lecture, three hours; discussion, one hour. Limited to Master of Applied Statistics students. Fundamentals of statistical programming using R, C, and C&amp;plus;&amp;plus;. R is currently state-of-art for statistical computing, simulation, statistical graphics, and analysis of data. C and C&amp;plus;&amp;plus; perform computations much faster, and added speed is necessary for analysis of large datasets and for high-level computations, particularly those involving loops and object-oriented programming. Performance of simulations and analysis of real datasets using C, C&amp;plus;&amp;plus;, and R. Fundamental principles and techniques for programming in these languages. How to use and interpret results of important functions in R packages. Statistical applications involve linear and nonlinear regression, shrinkage methods, density estimation, numerical optimization, maximum likelihood estimation, classification, and resampling. Graphics and real examples used to illustrate techniques. Analyses of both real and simulated data. Letter grading.</p>

Statistics

Combining rigorous, inclusive pedagogy with opportunities for co-curricular activities—including research, internships, and community engagement—the College provides students with diverse learning experiences that helps them thrive, become critical thinkers, and transform society.

College of Letters and Science

The department introduces students to the science of data through three sequences: theoretical statistics, data analysis, and statistical computing.

General Catalog

Statistical Computing and Programming

STATS404

4 units