This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

Regression Analysis: Unified Concepts, Practical Applications, Computer Implementation is a concise and innovative book that gives a complete presentation of applied regression analysis in approximately one-half the space of competing books. With only the modest prerequisite of a basic (non-calculus) statistics course this text is appropriate for the widest possible audience including college juniors, seniors and first-year graduate students in business and statistics, as well as professionals in business and industry. The book is able to accommodate this wide audience because of the unique, integrative approach that is taken to the teaching of regression analysis. Whereas other regression books cover regression in four chapters, beginning with a statistical review, followed by chapters on simple linear regression, matrix algebra and multiple regression, this book introduces regression and covers both simple linear regression and multiple regression in single cohesive chapter. This is made possible through an efficient, integrative discussion of the two techniques. Additionally, in the same chapter (Chapter Two) basic statistical and matrix algebra concepts are introduced as needed In order to facilitate instruction. This approach avoids the needless repetition that is often found in longer treatments of the subject, while serving to bring a collective focus to students of widely varying mathematical backgrounds.

This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

This book is intended to be primarily a supplemental text that can be used to integrate the use of computer packages into introductory business statistics and quantitative methods courses, demonstrating how computer packages can be used to solve statistical and operational research problems.

Regression Analysis: Unified Concepts, Practical Applications, Computer Implementation is a concise and innovative book that gives a complete presentation of applied regression analysis in approximately one-half the space of competing books. With only the modest prerequisite of a basic (non-calculus) statistics course this text is appropriate for the widest possible audience including college juniors, seniors and first-year graduate students in business and statistics, as well as professionals in business and industry. The book is able to accommodate this wide audience because of the unique, integrative approach that is taken to the teaching of regression analysis. Whereas other regression books cover regression in four chapters, beginning with a statistical review, followed by chapters on simple linear regression, matrix algebra and multiple regression, this book introduces regression and covers both simple linear regression and multiple regression in single cohesive chapter. This is made possible through an efficient, integrative discussion of the two techniques. Additionally, in the same chapter (Chapter Two) basic statistical and matrix algebra concepts are introduced as needed In order to facilitate instruction. This approach avoids the needless repetition that is often found in longer treatments of the subject, while serving to bring a collective focus to students of widely varying mathematical backgrounds.