This course explores advanced techniques in linear statistical modeling and analysis, covering multiple linear regression, nonlinear regression, and logistic regression. The focus is on model development, statistical inference, diagnostic evaluation, and practical application using real-world datasets. Students will build a robust methodological toolkit through a series of structured mini-projects, leading up to final projects that tackle complex, consulting-level data challenges such as stratification, covariate adjustment, and handling imperfect or unstructured data. Statistical methods will be implemented using R; however, the course is not intended to provide comprehensive instruction in R programming or syntax. Prerequisite: DATA 3441, MATH 4313.