Statistical Computing and Programming

Lecture, three hours; discussion, one hour. Limited to Master of Applied Statistics students. Fundamentals of statistical programming using R, C, and C++. R is currently state-of-art for statistical computing, simulation, statistical graphics, and analysis of data. C and C++ 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++, 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.

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Course

Instructor
Michael Tsiang
Previously taught
23W

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