2024-2025 University Catalog
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MATH 354 - Data Analysis I - Applied Linear Models
An applied course on linear models, an umbrella term that covers a wide range of statistical models widely used today across disciplines in academic research and industry. Building on simple linear regression, students explore multiple linear regression with both quantitative and qualitative explanatory variables. Students investigate model building, assessment of assumptions with model diagnostics and residual analyses, and the interpretation of results via statistical inference, untangling interaction terms, and post hoc analyses. This discussion is then extended to analysis of variance (ANOVA), generalized linear models, and mixed effects models. While applied, coursework aims to combine theory and application to emphasize the need for understanding each method’s theoretical foundation. Through iterative practice with scientific writing, students also learn to effectively communicate quantitative information. The statistical software R is used for all analyses.
Credits: 1.00
Corequisite: None
Prerequisites: or or or or ( and ) or ( and ) or ( and or ( and ) or ( and ) or ( and )
Major/Minor Restrictions: None
Class Restriction: None
Area of Inquiry: Natural Sciences & Mathematics
Liberal Arts Practices: Quantitative and Algorithmic Reasoning and The Process of Writing
Core Component: None
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