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Statistics for Business and Economics,
5th Edition

James Freeman, Thomas Williams, Thomas Williams, James Freeman

ISBN-13: 9781473768451 | ISBN-10: 1473768454

Copyright 2020

| Published 2020

| 640 pages

List Price USD $78.25

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Meet the Authors

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Jim Freeman was formerly Senior Lecturer in Statistics and Operational Research at Alliance Manchester Business School (AMBS), United Kingdom. He was born in Tewkesbury, Gloucestershire. After taking a first degree in Pure Mathematics at UCW Aberystwyth, he went on to receive MSc and PhD degrees in Applied Statistics from Bath and Salford universities respectively. In 1992/3 he was Visiting Professor at the University of Alberta. Before joining AMBS, he was Statistician at the Distributive Industries Training Board – and prior to that – the Universities Central Council on Admissions. He has taught undergraduate and postgraduate courses in business statistics and operational research courses to students from a wide range of management and engineering backgrounds. Until 2017 he taught the statistical core course on AMBS's Business Analytics master's programme – since rated top in Europe and sixth in the world. For many years he was also responsible for providing introductory statistics courses to staff and research students at the University of Manchester’s Staff Teaching Workshop. Through his gaming and simulation interests he has been involved in a significant number of external consultancy and grant-aided projects. More recently he received significant government (‘KTP’) funding for research in the area of risk management. Between July 2008 and December 2014 he was Editor of the Operational Research Society’s OR Insight journal and is currently Editor of the Tewkesbury Historical Society Bulletin. In November 2012 he received the Outstanding Achievement Award at the Decision Sciences Institutes 43rd Annual Meeting in San Francisco. In 2018 he was awarded an Honorary Fellowship by the University of Manchester.

Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate programme in Information Systems. At RIT he was the first chair of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Professor Williams is the co-author of 11 textbooks in the areas of management science, statistics, production and operations management and mathematics. He has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of elementary data analysis to the development of large-scale regression models.

Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate programme in Information Systems. At RIT he was the first chair of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Professor Williams is the co-author of 11 textbooks in the areas of management science, statistics, production and operations management and mathematics. He has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of elementary data analysis to the development of large-scale regression models.

Jim Freeman was formerly Senior Lecturer in Statistics and Operational Research at Alliance Manchester Business School (AMBS), United Kingdom. He was born in Tewkesbury, Gloucestershire. After taking a first degree in Pure Mathematics at UCW Aberystwyth, he went on to receive MSc and PhD degrees in Applied Statistics from Bath and Salford universities respectively. In 1992/3 he was Visiting Professor at the University of Alberta. Before joining AMBS, he was Statistician at the Distributive Industries Training Board – and prior to that – the Universities Central Council on Admissions. He has taught undergraduate and postgraduate courses in business statistics and operational research courses to students from a wide range of management and engineering backgrounds. Until 2017 he taught the statistical core course on AMBS's Business Analytics master's programme – since rated top in Europe and sixth in the world. For many years he was also responsible for providing introductory statistics courses to staff and research students at the University of Manchester’s Staff Teaching Workshop. Through his gaming and simulation interests he has been involved in a significant number of external consultancy and grant-aided projects. More recently he received significant government (‘KTP’) funding for research in the area of risk management. Between July 2008 and December 2014 he was Editor of the Operational Research Society’s OR Insight journal and is currently Editor of the Tewkesbury Historical Society Bulletin. In November 2012 he received the Outstanding Achievement Award at the Decision Sciences Institutes 43rd Annual Meeting in San Francisco. In 2018 he was awarded an Honorary Fellowship by the University of Manchester.

  • Chapter 19 on index numbers (on the online platform) has been updated with current index numbers.
  • Case Problems, exercises and Statistics in Practice features have been updated throughout to relate statistics to current issues and bring concepts alive.
  • The section on data mining in Chapter 1 has been expanded to include a discussion on big data and a new section on analytics has been added. The distinction between observed and experimental data has been highlighted.
  • Examples have been added to Chapter 12 in particular to point out an alternative computational method for calculating the chi-squared statistic in goodness-of-fit tests.
  • WebAssign is available with this title, a powerful digital solution designed by educators to enrich the teaching and learning experience. WebAssign provides extensive content, instant assessment and superior support.
  • Chapter 5 has been expanded to allow for new material on bivariate distributions, covariance and financial applications.
  • Self-test problems in each chapter help to guide and consolidate students' learning.
  • Data files that can be used with the most common statistics packages accompany exercises and examples. WebAssign is available with this title, a powerful digital solution designed by educators to enrich the teaching and learning experience. WebAssign provides extensive content, instant assessment and superior support.
  • A fully updated companion website is available with exercises and solutions, PowerPoint slides, appendices, data sets, online chapters and sample papers as well as software sections, covering basic functions in EXCEL, Minitab, SPSS and R.
  • Examples are sourced from a wide geographical base to ensure students get insight into statistics at work in different cultures and contexts.
  • Real-life end-of-chapter Case Problems bring theory to life and give students the opportunity to analyse larger data sets and prepare managerial reports.

Table of Contents

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Book contents
Preface
Acknowledgements
About the authors
1 Data and statistics
2 Descriptive statistics: tabular and graphical presentations
3 Descriptive statistics: numerical measures
4 Introduction to probability
5 Discrete probability distributions
6 Continuous probability distributions
7 Sampling and sampling distributions
8 Interval estimation
9 Hypothesis tests
10 Statistical inference about means and proportions with two populations
11 Inferences about population variances
12 Tests of goodness of fit and independence
13 Experimental design and analysis of variance
14 Simple linear regression
15 Multiple regression
16 Regression analysis: model building
17 Time series analysis and forecasting
18 Non-parametric methods

Online contents
19 Index numbers
20 Statistical methods for quality control
21 Decision analysis
22 Sample surveys

• Chapter Software Sections for EXCEL, MINITAB, SPSS and R
• Appendix A: References and bibliography
• Appendix B: Tables
• Appendix C: Summation Notation
• Appendix D: Answers to even-numbered exercises and fully worked solutions to exercises flagged with the SOLUTIONS icon.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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Statistics for Business and Economics

  • ISBN-10: 1473768454
  • ISBN-13: 9781473768451

Price USD$ 78.25

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