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. David Levine has also 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 also 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.

Kathryn 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.

Preface 

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 73

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

15.1 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 

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