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Student Learning Outcomes for the Statistics Major
Understand the rigorous mathematical underpinnings of statistics including; calculus based probability theory, proofs of statistical theorems, asymptotic behavior of estimators (expectation, variance, and bias)
Critically examine different statistics paradigms (Frequentist and Bayesian) as well as applications of their respective methodologies
Apply modern statistical methods/machine learning to real data analysis problems using R, the most popular statistical programming software.
Evaluate whether the theoretical assumptions behind statistical methods hold and what problems can arise when they don’t.
Model data in a variety of contexts and understand the strengths and weaknesses of the models.
Use statistical inference to solve problems in estimation and hypothesis testing.
Complete a research project through data collection, wrangling/cleaning, exploratory data analysis and visualization, analysis/modeling/inference, interpretation of results, and communication of those results to different audiences.
Assess ethical considerations behind the use of statistical methodology and the effects of bias on humans.