Predictive Modeling Applications in Actuarial Science

Chapter 2 - Overview of Linear Models


Margie Rosenberg | University of Wisconsin - Madison

Jim Gusczca | Deloitte

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Linear modeling, also known as regression analysis, is a core tool in statistical practice for data analysis, prediction and decision support. Applied data analysis requires judgment, domain knowledge, and the ability to analyze data.

This chapter provides a summary of the linear model, discusses model assumptions, parameter estimation, variable selection and model validation around a series of examples. These examples are grounded in data to help relate the theory to practice. All of these practical examples and exercises are completed using the open-source R statistical computing package. Particular attention is paid to the role of Exploratory Data Analysis in the iterative process of criticizing, improving, and validating models in a detailed case study. Linear models provide a foundation for many of the more advanced statistical and machine learning techniques that are explored in the later chapters of this volume.

Data R Code
MEPS Diabetes Data Examples and Case Study Code
Exercise Code