Predictive Modeling Applications in Actuarial Science
 Volume 1
 Introduction
 Predictive Modeling Foundations
 Predictive Modeling Methods
 Bayesian and Mixed Modeling
 Longitudinal Modeling
 Volume 2
 Generalized Linear Model
 Extensions of the Generalized Linear Model
 Unsupervised Predictive Modeling Methods

Applications on Current Problems in Actuarial Science
 Chapter 8  The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
 Chapter 9  Finite Mixture Model and Workersâ€™ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 2  Overview of Linear Models
Authors
Margie Rosenberg  University of Wisconsin  Madison
mrosenberg@bus.wisc.edu
Jim Gusczca  Deloitte
jguszcza@deloitte.com
Chapter Preview
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 opensource 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 