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.

This course will provide a set of foundational statistical modeling tools for data science. You will be introduced to: Methods, theory, and applications of linear statistical models; The topics of ...

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

Examples of such models include the normal distribution model and the linear regression model. Nonparametric statistics sometimes use data that is ordinal, meaning it relies not on numbers but on ...

The authors present a set of statistical decision rules based on linear regression models that can be implemented in an automated trend system to assist stability studies. coronado/shutterstock.com ...

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

Get ahead of big data. The Master of Science (M.S.) in Applied Statistics and Psychometrics meets the need for quantitative specialists to conduct statistical analyses, design quantitative research ...