Note: You are purchasing a standalone product; MyStatLab does not come packaged with this content. If you would like to purchase both the physical text and MyStatLab, search for ISBN-10: 0133869466 / ISBN-13: 9780133869460. That package includes ISBN-10: 0321870026/ISBN-13: 9780321870025,  ISBN-10: 032192147X/ISBN-13: 9780321921475 and ISBN-10: 0321929713/ISBN-13: 9780321929716. 

 

MyStatLab is not a self-paced technology and should only be purchased when required by an instructor.

Statistics is essential for all business majors and this text helps students see the role statistics will play in their own careers by providing examples drawn from all functional areas of business. Guided by principles set by major statistical and business science associations (ASA and DSI), plus the authors’ diverse teaching experiences, the Thirteenth Edition of Berenson/Levine/Szabat’s Basic Business Statistics continues to innovate and improve the way this course is taught to all students.

Mark L. Berenson is Professor of Management and Information Systems at Montclair State University and also Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (City University of New York). He received his B.A. in economic statistics and an M.B.A. in business statistics from City College of New York and a Ph.D. in business from the City University of New York. Berenson currently teaches graduate and undergraduate courses in statistics and in operations management in the School of Business and an undergraduate course in international justice and human rights that he co-developed in the College of Humanities and Social Sciences. Over the years, he has received serval awards for teaching and for innovative contributions to statistics education. In 2005, he was the first recipient of The Catherine A. Becker Service for Educational Excellence Award at Montclair State University. Berenson’s research has been published in numerous journals, including Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, and Journal of Management Sciences and Applied Cybernetics, and Journal of Health Administration Education. He is co-author of 11 statistics texts, including Statistics for Managers Using Microsoft Excel and Business Statistics: A First Course.

 

David M. Levine is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York). He received B.B.A. and M.B.A. degrees in statistics from City College of New York and a Ph.D. from New York University in industrial engineering and operations research. He is nationally recognized as a leading innovator in statistics education and is the co-author of 14 books, including such best- selling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab.

He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, currently in its second edition, Six Sigma for Green Belts and Champions and Design for Six Sigma for Green Belts and Champions, and the author of Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and Quality Management, third edition, McGraw-Hill/Irwin. He is also the author of Video Review of Statistics and Video Review of Probability, both published by Video Aided Instruction, and the statistics module of the MBA primer published by Cengage Learning. He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Decision Sciences Journal of Innovative Education, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and he has given numerous talks at the Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences. Levine has also received several awards for outstanding teaching and curriculum development from Baruch College.

 

Kathryn A, Szabat is Associate Professor and Chair of Business Systems and Analytics at LaSalle University. She teaches undergraduate and graduate courses in business statistics and operations management. Szabat’s research has been published in International Journal of Applied Decision Sciences Accounting Education, Journal of Applied Business and Economics, Journal of Healthcare Management, and Journal of Management Studies. Scholarly chapters have appeared in Managing Adaptability, Intervention, and People in Enterprise Information Systems; Managing, Trade, Economies and International Business; Encyclopedia of Statistics in Behavioral Science; and Statistical Methods in Longitudinal Research. Szabat has provided statistical advice to numerous business, non-business, and academic communities. Her more recent involvement has been in the areas of education, medicine, and nonprofit capacity building. Szabat received a B.S. in mathematics from State University of New York at Albany and M.S. and Ph.D. degrees in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.

Getting Started: Important Things to Learn First

1. Defining and Collecting Data
2. Organizing and Visualizing Data

3. Numerical Descriptive Measures

4. Basic Probability 

5. Discrete Probability Distributions 

6. The Normal Distribution and Other Continuous Distributions 
7. Sampling Distributions 
8. Confidence Interval Estimation 
9. Fundamentals of Hypothesis Testing: One Sample Tests 
10. Two-Sample Tests 
11. Analysis of Variance

12. Chi-Square and Nonparametric Tests 

13. Simple Linear Regression 

14. Introduction to Multiple Regression 
15. Multiple Regression Model Building 
16. Time-Series Forecasting 
17. Business Analytics

18. A Roadmap for Analyzing Data

19. Statistical Applications in Quality Management (online)

18. Statistical Applications in Quality Management (online) 

19. Decision Making