Expected outcomes:

Students have mastered programming in R, including the development of statistical graphics.
Students can conduct Monte Carlo simulations and simulation studies to investigate statistical properties and validate analytical methods.
Students possess competency in optimization techniques for fitting statistical models and solving numerical problems.
Students can use computational tools to implement, validate, and visualize statistical methods for data analysis.
Students can identify types of problems and select appropriate statistical methods for data analysis, correctly interpret results, and gain hands-on experience with real-life data analysis.
Students have developed proficiency in classification methods, including both classical linear classification rules and modern computer-intensive methods such as classification trees, and the estimation of classification errors using training and validation data sets.
Students have mastered advanced regression techniques, including non-linear parametric regression and nonparametric regression using kernel estimates.
Students can analyze categorical data using logistic and Poisson regression methods and appropriately handle missing data in statistical analyses.
Students are proficient in analyzing and interpreting numerous real-world datasets using statistical software R and RStudio.
Students understand estimation theory and can apply point and interval estimation techniques to statistical problems.
Students have mastered hypothesis testing procedures and understand test size and power.
Students can analyze categorical data and contingency tables using appropriate statistical methods.
Students are proficient in applying linear models and regression techniques to statistical problems.
Students possess a strong theoretical foundation that enables them to select appropriate inferential methods and interpret statistical results with rigor.

Statistics Minor

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Learning outcomes