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Applied Statistics in Business and Economics https://www.mheducation.com/cover-images/Jpeg_400-high/1260716287.jpeg 7 2022 9781260716283 Applied Statistics in Business and Economics, 7th edition, provides real meaning to the use of statistics in the real world by using real business situations and real data while appealing to students who want to know the why rather than just the how. The text emphasizes thinking about data, choosing appropriate analytic tools, using computers effectively, and recognizing the limitations of statistics. It motivates student learning through applied current exercises and cases that provide real-world relevance and includes analytics in action, careers, and applications of big data, Artificial Intelligence, and machine learning (including ethical issues). The Doane and Seward authors work as a team, integrating the digital and eBook assets seamlessly. In recognition of a growing interest in analytics training beyond Excel, the textbook now provides an optional introduction to R with illustrations of topics in each chapter. Support for R is further enhanced with Learning Stats modules, tables of R functions, and R-compatible Excel data sets.
09781260716283
Applied Statistics in Business and Economics
Applied Statistics in Business and Economics

Applied Statistics in Business and Economics, 7th Edition

ISBN10: 1260716287 | ISBN13: 9781260716283
By David Doane and Lori Seward
© 2022

Purchase Options:

* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

Additional Product Information:

Applied Statistics in Business and Economics, 7th edition, provides real meaning to the use of statistics in the real world by using real business situations and real data while appealing to students who want to know the why rather than just the how. The text emphasizes thinking about data, choosing appropriate analytic tools, using computers effectively, and recognizing the limitations of statistics. It motivates student learning through applied current exercises and cases that provide real-world relevance and includes analytics in action, careers, and applications of big data, Artificial Intelligence, and machine learning (including ethical issues). The Doane and Seward authors work as a team, integrating the digital and eBook assets seamlessly. In recognition of a growing interest in analytics training beyond Excel, the textbook now provides an optional introduction to R with illustrations of topics in each chapter. Support for R is further enhanced with Learning Stats modules, tables of R functions, and R-compatible Excel data sets.

Chapter 1: Overview of Statistics 

Chapter 2: Data Collection 

Chapter 3: Describing Data Visually 

Chapter 4: Descriptive Statistics 

Chapter 5: Probability 

Chapter 6: Discrete Probability Distributions 

Chapter 7: Continuous Probability Distributions 

Chapter 8: Sampling Distributions and Estimation 

Chapter 9: One-Sample Hypothesis Tests 

Chapter 10: Two-Sample Hypothesis Tests 

Chapter 11: Analysis of Variance 

Chapter 12: Simple Regression 

Chapter 13: Multiple Regression 

Chapter 14: Time-Series Analysis 

Chapter 15: Chi-Square Tests 

Chapter 16: Nonparametric Tests 

Chapter 17: Quality Management 

Chapter 18: Simulation 1


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About the Author

David Doane

David P. Doane is Professor of Quantitative Methods in Oakland University's Department of Decision and Information Sciences. He earned his Bachelor of Arts degree in mathematics and economics at the University of Kansas and his PhD from Purdue University's Krannert Graduate School.

Lori Seward

Lori E. Seward is an Instructor in the Decisions Sciences Department in the College of Business at The University of Colorado at Denver and Health Sciences Center. She earned her Bachelor of Science and Master of Science degrees in Industrial Engineering at Virginia Tech. After several years working as a reliability and quality engineer in the paper and automotive industries, she earned her PhD from Virginia Tech.

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