Designed especially for business and social science students who are familiar with the fundamentals of statistics, this text explores both the theory and practice of regression ysis. Describes the interaction between data ysis and regression models used to represent the data - to help students learn how to yze regression data, understand regression models, and how to specify an appropriate model to represent a data set. The main narrative in each chapter stresses application and interpretation of results in applied statistical methods from a user's point of w. Principles are introduced as needed. Features and Benefits Uses a student-friendly style throughout. Provides numbered lists of procedures, chapter overws and summaries, marginal notes, chapter glossaries Arranges material in modular fashion to allow instructors flexibility in designing courses to fit individual class needs- chapters may be presented in many sequences Technical supplements reinforce and extend the material in the main body of the text by developing theory in a step-by-step fashion. Presents the use of matrix algebra in regression ysis on four different student levels - from students with no knowledge of matrix algebra to those with knowledge of advanced concepts. Interweaves examples and case studies with the basic principles. Introduces models with an example and discusses the data ysis at an intuitive level includes examples from each of the major areas of business, including accounting, finance, management, marketing, and risk management Provides examples of the most popular computer packages. Contains varied exercise sets featuring original data sets that are based on real-world situations. Offers several different types of exercises designed to meet the needs of different types of students.

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