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Introduction to Regression Modeling

ISBN-10: 0534420753
ISBN-13: 9780534420758
Edition: 2006
List price: $316.95 Buy it from $74.99
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Description: Using a data-driven approach, this book is an exciting blend of theory and interesting regression applications. Students learn the theory behind regression while actively applying it. Working with many case studies, projects, and exercises from  More...

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Book details

List price: $316.95
Copyright year: 2006
Publisher: Brooks/Cole
Publication date: 1/20/2005
Binding: Mixed Media
Pages: 448
Size: 8.75" wide x 9.75" long x 1.00" tall
Weight: 2.2
Language: English

Using a data-driven approach, this book is an exciting blend of theory and interesting regression applications. Students learn the theory behind regression while actively applying it. Working with many case studies, projects, and exercises from areas such as engineering, business, social sciences, and the physical sciences, students discover the purpose of regression and learn how, when, and where regression models work. The book covers the analysis of observational data as well as of data that arise from designed experiments. Special emphasis is given to the difficulties when working with observational data, such as problems arising from multicollinearity and "messy" data situations that violate some of the usual regression assumptions. Throughout the text, students learn regression modeling by solving exercises that emphasize theoretical concepts, by analyzing real data sets, and by working on projects that require them to identify a problem of interest and collect data that are relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.

Introduction to Regression Models
Simple Linear Regression
A Review of Matrix Algebra and Important Results of Random Vectors
Multiple Linear Regression Model
Specification Issues in Regression Models
Model Checking
Model Selection
Case Studies in Linear Regression
Nonlinear Regression Models
Regression Models for Time Series Situations
Logistic Regression
Generalized Linear Models and Poisson Regression
Brief Answers to Selected Exercises
Statistical Tables
References

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