Visualizing Linear Models

This book provides a visual and intuitive coverage of the core theory of linear models. Designed to develop fluency with the underlying mathematics and to build a deep understanding of the principles, it's an excellent basis for a one-semester course on statistical theory and linear modeling for intermediate undergraduates or graduate students.

Three chapters gradually develop the essentials of linear model theory. They are each preceded by a review chapter that covers a foundational prerequisite topic. This classroom-tested work explores two distinct and complementary types of visualization: the observations picture and the variables picture. To improve retention of material, this book is supplemented by a bank of ready-made practice exercises for students. These are available for digital or print use.

Features ready-made exercises using a flash card app, available for print or digital use, to improve retention of material Offers a targeted review of relevant, key background topics Presents the fundamentals of visualizing linear models in a compact volume, ideal for a one-semester course Includes supplementary material: sn.pub/extras

Autorentext

Dr. W. D. Brinda is a researcher and lecturer for the department of statistics and data science at Yale University. His research interests include simulated annealing, third moment tensor methods, adaptive estimation, and developing visual ways of understanding statistical concepts. His coauthored work has been published in Statistics and Probability Letters (2019) and IEEE Transactions on Information Theory (2019), among others. **

Inhalt
Preface.- Review: Linear Algebra.- Least-Squares Regression.- Review: Random Vectors.- The Linear Model.- Review: Normality.- Normal Errors.