Statistics 525 - Regression&Analysis/Variance

Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.

Junior and Senior undergraduate Math and Statistics majors only. STATISTC 516 This class has been approved to have remote components for the instructor. The class will be meeting in person. Please contact the instructor for details.

Must have prior knowledge of hypothesis tests incl. t-tests, z-tests, Ftests, confidence intervals and p-values.

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