Expected outcomes:

Students can collaborate effectively as statisticians on multidisciplinary teams. This includes understanding the needs of researchers, designing studies to investigate client needs, and communicating study results through graphs, writing, and oral presentations in a manner accessible to non-statisticians.
Students can design research ethically, respecting the rights of research subjects, analyzing data without manipulating results, and properly citing and crediting the work of others.
Students will be exposed to professional statisticians to understand careers in statistics.
Students can effectively communicate statistical information in both written and verbal forms appropriate for diverse audiences, including those without statistical training.
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 sampling theory and the concept of sufficiency in statistical inference.
Students have mastered estimation theory, including point and interval estimation methods.
Students are proficient in hypothesis testing, including an understanding of test size and power.
Students can analyze categorical data and contingency tables, and apply linear models to statistical problems.
Students understand decision theory and its application to statistical inference problems.

Statistical Science B.S.

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Are students ever admitted to a pre-major status before admission to this major?

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Minimum number of completed credits required for admission