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Statistics for Managers Using Microsoft Excel, 9th edition

Published by Pearson (January 2, 2020) © 2021

  • David M. Levine Baruch College, City University of New York
  • David F. Stephan Two Bridges Instructional Technology
  • Kathryn A. Szabat La Salle University

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For 1-semester courses in Introduction to Business Statistics.

The definitive source for Microsoft Excel in business statistics

Statistics for Managers Using Microsoft® Excel®, 9th Edition presents statistics in the context of specific business fields and includes a full chapter on business analytics. Guided by the authors' diverse teaching experiences and by ASA's Guidelines for Assessment and Instruction (GAISE), it continues to innovate and improve how the course is taught. Current data throughout gives students valuable practice analyzing the types of data they will see in their professions, and the authors' friendly writing style includes tips and learning aids throughout. 

Hallmark features of this title

  • Data analysis through interpretation of the results from Microsoft Excel is emphasized.
  • An integrated 5-step approach helps students follow the progression of applying statistics: Define, Collect, Organize, Visualize, Analyze.
  • First Things First sets the context for explaining what statistics is while ensuring students understand the importance of business statistics. It is designed for distribution before the first class begins and especially helpful for hybrid or online courses.
  • Student Tips reinforce concepts and offer quick study tips for mastering key details.
  • Learn More references reinforce important points and direct students to additional learning resources. 
  • End-of-chapter cases include a business case that continues through most chapters. 

New and updated features of this title

  • New or revised Using Statistics case scenarios in 7 chapters begin those chapters, and show how statistics is used in accounting, finance, information systems, management or marketing. Scenarios are then used throughout the chapter to provide an applied context for the concepts. 
  • New Tableau Guides in each chapter explain how to use the data visualization software Tableau Public to complement Microsoft Excel for visualizing data. The text offers Tableau Public results for selected methods in which Tableau can enhance or complement Excel results.  
  • A new Business Analytics chapter (Ch. 17) is a complete introduction to the field of business analytics. It defines terms and categories that introductory business statistics students may encounter in other courses or outside the classroom.
    • Includes a new Consider This feature, “What's My Major If I Want to Be a Data Miner?”
    • Exercises have been reviewed, updated and replaced in this edition.
  • Tabular summaries now guide readers to reaching conclusions and making decisions based on statistical information. This change not only adds clarity to the purpose of the statistical method being discussed but better illustrates the role of statistics in business decision-making processes.

Features of MyLab Statistics for the 9th Edition

  • Excel Grader Projects: Excel Projects in MyLab Statistics allow students to analyze data using actual Microsoft Excel spreadsheet software.
    • Each Excel project focuses on a key concept in the business statistics course and asks students to answer questions about a data set provided in Excel.
    • Excel project questions are auto-graded and provide immediate feedback so students can identify any conceptual or procedural mistakes made in the problem solving process.
    • 23 separate statistical topics are covered.
  • Using proven, field-tested technology, auto-graded Excel Projects let instructors seamlessly integrate Microsoft Excel content into the course without having to grade spreadsheets manually.

F. First Things First

  • FTF.1 Think Differently About Statistics
  • FTF.2 Business Analytics: The Changing Face of Statistics
  • FTF.3 Starting Point for Learning Statistics
  • FTF.4 Starting Point for Using Software
  • FTF.5 Starting Point for Using Microsoft Excel

1. Defining and Collecting Data

  • 1.1 Defining Variables
  • 1.2 Collecting Data
  • 1.3 Types of Sampling Methods
  • 1.4 Data Cleaning
  • 1.5 Other Data Preprocessing Tasks
  • 1.6 Types of Survey Errors

2. Organizing and Visualizing Variables

  • 2.1 Organizing Categorical Variables
  • 2.2 Organizing Numerical Variables
  • 2.3 Visualizing Categorical Variables
  • 2.4 Visualizing Numerical Variables
  • 2.5 Visualizing Two Numerical Variables
  • 2.6 Organizing a Mix of Variables
  • 2.7 Visualizing a Mix of Variables
  • 2.8 Filtering and Querying Data 
  • 2.9 Pitfalls in Organizing and Visualizing Variables

3. Numerical Descriptive Measures

  • 3.1 Measures of Central Tendency
  • 3.2 Measures of Variation and Shape
  • 3.3 Exploring Numerical Variables
  • 3.4 Numerical Descriptive Measures for a Population
  • 3.5 The Covariance and the Coefficient of Correlation
  • 3.6 Descriptive Statistics: Pitfalls and Ethical Issues

4. Basic Probability

  • 4.1 Basic Probability Concepts
  • 4.2 Conditional Probability
  • 4.3 Ethical Issues and Probability
  • 4.4 Bayes' Theorem
  • 4.5 Counting Rules

5. Discrete Probability Distributions

  • 5.1 The Probability Distribution for a Discrete Variable
  • 5.2 Binomial Distribution
  • 5.3 Poisson Distribution
  • 5.4 Covariance of a Probability Distribution and Its Application in Finance
  • 5.5 Hypergeometric Distribution

6. The Normal Distribution and Other Continuous Distributions

  • 6.1 Continuous Probability Distributions
  • 6.2 The Normal Distribution
  • 6.3 Evaluating Normality
  • 6.4 The Uniform Distribution
  • 6.5 The Exponential Distribution
  • 6.6 The Normal Approximation to the Binomial Distribution

7. Sampling Distributions

  • 7.1 Sampling Distributions
  • 7.2 Sampling Distribution of the Mean
  • 7.3 Sampling Distribution of the Proportion
  • 7.4 Sampling from Finite Populations

8. Confidence Interval Estimation

  • 8.1 Confidence Interval Estimate for the Mean (σ Known)
  • 8.2 Confidence Interval Estimate for the Mean (σ Unknown)
  • 8.3 Confidence Interval Estimate for the Proportion
  • 8.4 Determining Sample Size
  • 8.5 Confidence Interval Estimation and Ethical Issues
  • 8.6 Application of Confidence Interval Estimation in Auditing
  • 8.7 Estimation and Sample Size Determination for Finite Populations
  • 8.8 Bootstrapping

9. Fundamentals of Hypothesis Testing: One-Sample Tests

  • 9.1 Fundamentals of Hypothesis Testing
  • 9.2 t Test of Hypothesis for the Mean (σ Unknown)
  • 9.3 One-Tail Tests
  • 9.4 Z Test of Hypothesis for the Proportion
  • 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
  • 9.6 Power of the Test

10. Two-Sample Tests

  • 10.1 Comparing the Means of Two Independent Populations
  • 10.2 Comparing the Means of Two Related Populations
  • 10.3 Comparing the Proportions of Two Independent Populations
  • 10.4 F Test for the Ratio of Two Variances
  • 10.5 Effect Size

11. Analysis of Variance

  • 11.1 One-Way ANOVA
  • 11.2 Two-Way ANOVA
  • 11.3 The Randomized Block Design
  • 11.4 Fixed Effects, Random Effects, and Mixed Effects Models

12. Chi-Square and Nonparametric Tests

  • 12.1 Chi-Square Test for the Difference Between Two Proportions
  • 12.2 Chi-Square Test for Differences Among More Than Two Proportions
  • 12.3 Chi-Square Test of Independence
  • 12.4 Wilcoxon Rank Sum Test for Two Independent Populations
  • 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA
  • 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
  • 12.7 Chi-Square Test for the Variance or Standard Deviation
  • 12.8 Wilcoxon Signed Ranks Test for Two Related Populations

13. Simple Linear Regression

  • 13.1 Simple Linear Regression Models
  • 13.2 Determining the Simple Linear Regression Equation
  • 13.3 Measures of Variation
  • 13.4 Assumptions of Regression
  • 13.5 Residual Analysis
  • 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic
  • 13.7 Inferences About the Slope and Correlation Coefficient
  • 13.8 Estimation of Mean Values and Prediction of Individual Values
  • 13.9 Potential Pitfalls in Regression

14. Introduction to Multiple Regression

  • 14.1 Developing a Multiple Regression Model
  • 14.2 Evaluating Multiple Regression Models
  • 14.3 Multiple Regression Residual Analysis
  • 14.4 Inferences About the Population Regression Coefficients
  • 14.5 Testing Portions of the Multiple Regression Model
  • 14.6 Using Dummy Variables and Interaction Terms
  • 14.7 Logistic Regression
  • 14.8 Cross-Validation

15. Multiple Regression Model Building

  • 15.1 The Quadratic Regression Model
  • 15.2 Using Transformations in Regression Models
  • 15.3 Collinearity
  • 15.4 Model Building
  • 15.5 Pitfalls in Multiple Regression and Ethical Issues

16. Time-Series Forecasting

  • 16.1 Time-Series Component Factors
  • 16.2 Smoothing an Annual Time Series
  • 16.3 Least-Squares Trend Fitting and Forecasting
  • 16.4 Autoregressive Modeling for Trend Fitting and Forecasting
  • 16.5 Choosing an Appropriate Forecasting Model
  • 16.6 Time-Series Forecasting of Seasonal Data
  • 16.7 Index Numbers

17. Business Analytics

  • 17.1 Business Analytics Overview
  • 17.2 Descriptive Analytics
  • 17.3 Decision Trees
  • 17.4 Clustering
  • 17.5 Association Analysis
  • 17.6 Text Analytics
  • 17.7 Prescriptive Analytics

18. Getting Ready to Analyze Data in the Future

  • 18.1 Analyzing Numerical Variables
  • 18.2 Analyzing Categorical Variables

19. Statistical Applications in Quality Management (online)

  • 19.1 The Theory of Control Charts
  • 19.2 Control Chart for the Proportion: The p Chart
  • 19.3 The Red Bead Experiment: Understanding Process Variability
  • 19.4 Control Chart for an Area of Opportunity: The c Chart
  • 19.5 Control Charts for the Range and the Mean
  • 19.6 Process Capability
  • 19.7 Total Quality Management
  • 19.8 Six Sigma

20. Decision Making

  • 20.1 Payoff Tables and Decision Trees
  • 20.2 Criteria for Decision Making
  • 20.3 Decision Making with Sample Information
  • 20.4 Utility

Appendices

About our authors

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 2nd 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 Pearson imprint FT Press; and Quality Management, 3rd Edition with 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 also has received several awards for outstanding teaching and curriculum development from Baruch College.

David F. Stephan is an independent instructional technologist. He was an Instructor/Lecturer of Computer Information Systems at Baruch College (City University of New York) for over 20 years, and served as an Assistant to the Provost and to the Dean of the School of Business & Public Administration for computing. He pioneered the use of computer classrooms for business teaching, devised interdisciplinary multimedia tools, and created techniques for teaching computer applications in a business context. He also conducted the first large-scale controlled experiment to show the benefit of teaching Microsoft Excel in a business case context to undergraduate students.

An avid developer, he created multimedia courseware while serving as the Assistant Director of a Fund for the Improvement of Postsecondary Education (FIPSE) project at Baruch College. Stephan is also the originator of PHStat, the Pearson Education statistical add-in for Microsoft Excel and a co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, and Practical Statistics by Example Using Microsoft Excel and Minitab. He is currently developing ways to extend the instructional materials that he and his co-authors develop to mobile and cloud computing platforms as well as develop social-media facilitated means to support learning in introductory business statistics courses.

David Stephan received a B.A. in geology from Franklin and Marshall College and a M.S. in computer methodology from Baruch College (City University of New York).

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. She also teaches as Visiting Professor at the Ecole Superieure de Commerce et de Management (ESCEM) in France.

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. She 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.

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