Statistics Minor

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.