This robustness can be a deciding factor in choosing the appropriate statistical method. For linear regression models, graphical methods such as residual plots can help identify issues like ...

Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for ...

Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience in linear model building on real data sets. Prerequisite: recommended: Linear Algebra.

Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics Prior knowledge needed: Basic calculus ...

Stat 350 - Linear Models in Applied Stats Lectures: Mondays, Wednesdays and Fridays 10:30 in AQ3003 Tutorials: ... Applied Linear Statistical Models by Neter/Wasserman/Kutner, Applied Linear ...

The course contains five topics, including (1) principles of statistical analysis, including data preparation, statistical models, regression and classification, inference, prediction, and ...