APPLIED REGRESSION ANALYSIS

Prerequisites & course information

Requisites: ( STAT 240 , 301 , 302, 312 , 324 , 371 , ECON 310 , B M E 325 , GEN BUS 306 , I SY E 210 , or POLI SCI 270 ) and (STAT 327 or 303 , or concurrent enrollment) Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Correctly choose and apply common regression methods that are used in practice to analyze data, including simple and multiple linear regressions, ANOVAs/ANCOVAs, generalized linear models (e.g. logistic and Poisson) and fixed/random/mixed effect models Audience: Undergraduate 2. Identify the underlying assumptions behind common regression methods and utilize diagnostic tools to detect violations of said assumptions Audience: Undergraduate 3. Correctly interpret and explain results from regression methods, including interpretation of the coefficients, the p-values, R-squared, and other statistical summaries from regression Audience: Undergraduate 4. Apply these methods to real data using the free statistical software R Audience: Undergraduate Learning Outcomes: 1. Correctly choose and apply common regression methods that are used in practice to analyze data, including simple and multiple linear regressions, ANOVAs/ANCOVAs, generalized linear models (e.g. logistic and Poisson) and fixed/random/mixed effect models Audience: Undergraduate 2. Identify the underlying assumptions behind common regression methods and utilize diagnostic tools to detect violations of said assumptions Audience: Undergraduate 3. Correctly interpret and explain results from regression methods, including interpretation of the coefficients, the p-values, R-squared, and other statistical summaries from regression Audience: Undergraduate 4. Apply these methods to real data using the free statistical software R Audience: Undergraduate