STAT 330
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Statistical Modeling 2
StatisticsCollege of Computational, Mathematical, & Physical Sciences
Course Description
Intermediate regression and inference with R. Topics include matrix notation, diagnostics, interactions, nonlinear predictors, logistic regression, model selection, and validation. Focus on balancing interpretability and predictive performance in applied data analysis.
When Taught
Fall and Winter
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Prerequisite
Complete ALL of the following Courses:
- 11839-001
AND 03615-008
Title
Regression Modeling
Learning Outcome
Apply multiple regression models using matrix notation to describe and analyze relationships among variables, reinforcing both predictive and inferential reasoning.
Title
Model Evaluation
Learning Outcome
Assess model adequacy through diagnostics, residual analysis, and validation techniques, demonstrating critical thinking in evaluating statistical evidence.
Title
Complex Predictors
Learning Outcome
Incorporate interactions and nonlinear predictors into regression models and interpret their effects in context.
Title
Model Selection
Learning Outcome
Compare and select models using criteria such as AIC, BIC, adjusted R², and cross-validation, with attention to the tradeoff between interpretability and predictive performance.
Title
Logistic Regression
Learning Outcome
Apply logistic regression for binary outcomes, interpreting coefficients, odds ratios, and classification metrics, and connecting results to real-world questions.
Title
Communication
Learning Outcome
Communicate statistical findings clearly to both technical and non-technical audiences, reflecting honesty, clarity, and responsibility in the use of statistical evidence.
