[3 Credits] Three hours lecture per week. Broad coverage of methods of applied statistics, designed for students who want to take advantage of their good math backgrounds for better understanding. Data description; elementary probability, random variables, distributions; principles of statistical inference; methods for one-, two-, and multi-sample settings, including ANOVA and multiple regression; methods for categorical responses. Use of SAS and other software for analysis, simulations, graphics, and report writing. Some cases will use large national databases, such as NHANES and CPS. Prerequisites: multi-variable calculus and linear algebra.
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