STAT 330
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Statistical Modeling 2
Statistics
College of Computational, Mathematical & Physical Sciences
Course Description
Regression, transformations, residuals, indicator variables, variable selection, logistic regression, time series, observational studies, statistical software.
When Taught
Fall; Winter.
Min
3
Fixed
3
Fixed
3
Fixed
0
Recommended
STAT 250 and MATH 113 are strongly recommended as a prerequisites or concurrent enrollment.
Title
Regression Models
Learning Outcome
Fit a regression model with professional statistical software, express models using matrix notation, explain model assumptions, and interpret model results.
Title
Transformations
Learning Outcome
Apply appropriate transformations to variables to improve agreement with regression assumptions
Title
Residual Diagnostics
Learning Outcome
Use residuals and influence diagnostics to assess model fit, evaluate agreement with regression assumptions, and identify outliers and influential observations
Title
Indicator Variables
Learning Outcome
Create sets of indicator variables for categorical explanatory variables and interpret their effects.
Title
Variable Selection
Learning Outcome
Apply variable selection methods to identify a subset regression model that selects the most important explanatory variables from a large data set.
Title
Generalized Regression Models
Learning Outcome
Fit generalized regression models, such as logistic and Poisson; explain model assumptions; and interpret model results.
Title
Software Proficiency
Learning Outcome
Implement the basic steps of statistical modeling from this course in both R and Python
