About the Book
Table of Contents:
F. First Things First
Using Statistics: Is the Price Right?
FTF.1 Business Statistics
Sidebar: Crossing Over
FTF:.2 Talking About Data
Sidebar: Secondary Data and Data Privacy
FTF.3: Software Orientation
Using Statistics: Is the Price Right? Revisited
Summary
Key Terms
References
Cases
Excel Orientation
JMP Orientation
Minitab Orientation
Tableau Orientation
1. Defining and Collecting Data
Using Statistics: Collecting Some Defining Moments
1.1: Defining Data
Sidebar: Failing at Statistics I
1.2: Populations, Samples, and Sampling
1.3: Types of Survey Errors
Sidebar: Failing at Statistics II: What George Gallup Got Wrong
1.4: Data Cleaning
1.5: Data Wrangling
Using Statistics: Collecting Some Defining Moments, Revisited
Summary
Key Terms
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
Tableau Guide
2. Tabular and Visual Summarization of Variables
Using Statistics: “The Choice Is Yours”
2.1: Summarizing Categorical Variables as Tables
2.2: Summarizing Numerical Variables as Tables
Sidebar: Excelling with Bins
2.3: Visualizing Categorical Variables
2.4: Visualizing Numerical Variables
2.5: Visualizing Two Numerical Variables
2.6: Summarizing Multiple Variables as Tables
2.7: Visualizing Multiple Variables
2.8: Filtering Variables
2.9: Pitfalls in Summarizing and Visualizing Variables
Using Statistics: “The Choice Is Yours,” Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
Tableau Guide
3. Numerical Descriptive Measures
Using Statistics:More Descriptive Choices
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
Using Statistics: More Descriptive Choices, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
Tableau Guide
4. Basic Probability
Using Statistics: Probable Outcomes at Fredco Warehouse Club
4.1: Basic Probability Concepts
4.2: Conditional Probability
4.3: Bayes' Theorem
Sidebar: Divine Providence and Spam
4.4: Counting Rules
4.5: Ethical Issues and Probability
Using Statistics: Probable Outcomes at Fredco Warehouse Club, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
5. Discrete Probability Distributions
Using Statistics: Events of Interest at Ricknel Home Centers
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
Using Statistics: Probable Events of Interest at Ricknel Home Centers, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
6. The Normal Distribution and Other Continuous Distributions
Using Statistics: Normal Load Times for See+ Home Page
6.1: Continuous Probability Distributions
6.2: The Normal Distribution
Visual Explorations: Exploring the Normal Distribution
Sidebar: What is Normal?
6.3: Evaluating Normality
6.4: The Uniform Distribution
6.5: The Exponential Distribution
6.6: The Normal Approximation to the Binomial Distribution
Using Statistics: Normal Load Times ... , Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
7. Sampling Distributions
Using Statistics:Sampling Oxford Snacks
7.1: Sampling Distributions
7.2: Sampling Distribution of the Mean
Visual Explorations: Exploring Sampling Distributions
7.3: Sampling Distribution of the Proportion
7.4: Sampling from Finite Populations
Using Statistics: Sampling Oxford Snacks, Revisited
Summary
Sidebar: (Of) Chance Discoveries
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
MP Guide
Minitab Guide
8. Confidence Interval Estimation
Using Statistics: Getting Estimates at Ricknel Home Centers
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: Confidence Interval Estimation in Auditing
8.7: Estimation and Sample Size Determination for Finite Populations
8.8: Bootstrapping
Using Statistics: Getting Estimates at Ricknel Home Centers, Revisited
Summary
Sidebar: Errors About the “Margin of Error” in Polls
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
9. Fundamentals of Hypothesis Testing: One-Sample Tests
Using Statistics: Significant Testing at Oxford Snacks
Fundamentals of Hypothesis Testing: One-Sample Tests
9.1: Fundamentals of Hypothesis Testing
9.2: Hypothesis Test Approaches
9.3: t Test of Hypothesis for the Mean (σ Unknown)
9.4: One-Tail Tests
9.5: Z Test of Hypothesis for the Proportion
9.6: Potential Hypothesis-Testing Pitfalls and Ethical Issues
9.7: Power of the Test
Using Statistics: Significant Testing at Oxford Snacks, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
10. Two-Sample Tests
Using Statistics: Differing Means for Selling Smart TVs at Arlingtons?
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
Using Statistics: Differing Means ....? Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
11. Analysis of Variance
Using Statistics: The Means to Find Differences at Arlingtons
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
Using Statistics: The Means to Find Differences at Arlingtons, Revisited
Summary
Sidebar: “Why can't you combine the pre- and post-hoc tests into one?”
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
12. Chi-Square and Nonparametric Tests
Using Statistics: Making a Difference at T.C. Resorts
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
12.9: Friedman Rank Test
Using Statistics: Making a Difference at T.C. Resorts, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
13. Simple Linear Regression
Using Statistics: Finding the Best Pattern at Sunflowers
13.1: Simple Linear Regression Models
13.2: Determining the Simple Linear Regression Equation
Visual Explorations: Exploring Simple Linear Regression Coefficients
13.3: Measures of Variation
13.4: Evaluating Assumptions Using Residual Analysis
13.5: Measuring Autocorrelation: The Durbin-Watson Statistic
13.6: Inferences About the Slope and Correlation Coefficient
13.7: Estimation of Mean Values and Prediction of Individual Values
13.8: Potential Pitfalls in Regression
Using Statistics: Finding the Best Pattern at Sunflowers, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
MP Guide
Minitab Guide
Tableau Guide
14. Introduction to Multiple Regression
Using Statistics: Designing for Multiple Effects at Quick Value
14.1: Developing a Multiple Regression Model
14.2: Multiple Regression Residual Analysis
14.3: Evaluating Multiple Regression Models
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
Consider This: What Is Not Normal? (Using a categorical dependent variable)
14.7: The Quadratic Regression Model
14.8 Using Transformations in Regression Models
14.9: Influence Analysis
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
15. More Complex Multiple Regression Models
Using Statistics: Valuing Parsimony at Nickels Online
15.1 Multicollinearity
15.2 Variable Selection
15.3 Automated Model Building and Selection
15.4 Overfit Models
15.5 Logistic Regression
15.6 Pitfalls in Multiple Regression and Ethical Issues
Using Statistics: Valuing Parsimony …, Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
16. Time-Series Forecasting
Using Statistics: Are Your Investment Advisers Trending?
16.1 Time-Series Component Factors
16.2 Smoothing an Annual Time Series is
16.3 Least-Squares Trend Fitting and Forecasting
16.4 Autoregressive Modeling for Trend Fitting and Forecasting
16.5 Choosing an Appropriate Forecasting Model to make
16.6 Time-Series Forecasting of Seasonal Data from
16.7 Index Numbers
Sidebar: Let the Model User Beware
Using Statistics: Are Your Investment Advisers Trending? Revisited
Summary
Key Terms
Key Equations
Checking Your Understanding
Chapter Review Problems
References
Cases
Excel Guide
JMP Guide
Minitab Guide
17. Business Analytics
Using Statistics: Future Thinking at Stores of Value, Inc.
17.1 Business Analytics Overview
Sidebar: What's My Major If I Want to Be a Data Miner?
17.2 Descriptive Analytics
17.3 Decision Trees
17.4 Clustering
17.5 Association Analysis
17.6 Text Analytics
17.7 Prescriptive Analytics
Using Statistics: Future Thinking …, Revisited
Summary
Key Terms
Checking Your Understanding
References
Software Guide for Chapter 17
18. Getting Ready to Analyze Data in the Future
Using Statistics: Mounting Future Analyses
18.1 Analyzing Numerical Variables
18.2 Analyzing Categorical Variables
Using Statistics: The Future to Be Visited
Chapter Review Problems
19. Statistical Applications in Quality Management (online)
Using Statistics: Finding Quality at the Beachcomber
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
Using Statistics: Finding Quality at the Beachcomber, Revisited
Summary
Key Terms
Key Equations
Chapter Review Problems
References
Cases
Excel Guide
20. Decision Making (online)
Using Statistics: Reliable 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
Sidebar: Risky Business
Using Statistics: Reliable Decision Making, Revisited
Summary
Key Terms
Key Equations
Chapter Review Problems
References
Cases
Excel Guide
APPENDICES
A. Basic Math Concepts and Symbols
B. Important Software Skills and Concepts
C. Online Resources
D. Configuring Software
E. Table
F. Useful Knowledge
G. Software FAQs
H. All About PHStat
Self-Test Solutions and Answers to Selected Even-Numbered Problems Index
About the Author :
About our authors Mark L. Berenson is Professor of Information Management and Business Analytics at Montclair State University and Professor Emeritus of Information Systems and Statistics at Baruch College. He currently teaches graduate and undergraduate courses in statistics and 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.
Berenson received a BA in economic statistics and an MBA in business statistics from City College of New York and a PhD in business from the City University of New York. Berenson's research has been published in Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, Psychometrika, Educational and Psychological Measurement, Journal of Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The New York Statistician, Journal of Health Administration Education, Journal of Behavioral Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The Encyclopedia of Measurement & Statistics and the Encyclopedia of Statistical Sciences. He has coauthored numerous statistics texts published by Pearson. Over the years, Berenson has received several 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 and in 2012, he was the recipient of the Khubani/Telebrands Faculty Research Fellowship in the School of Business.
David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY, has been a nationally recognized innovator in statistics education for more than 3 decades. Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience. Levine has presented or chaired numerous sessions about business education at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Data, Analytics, and Statistics Instruction (DASI) mini-conference.
During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College's highest distinguished teaching honor. He earned BBA and MBA degrees from CCNY, and a PhD in industrial engineering and operations research from New York University.
Kathryn Szabat, Associate Professor of Business Systems and Analytics at La Salle University, has transformed several business school majors into 1 interdisciplinary major that better supports careers in new and emerging disciplines of data analysis, including analytics. Szabat strives to inspire, stimulate, challenge and motivate students through innovation and curricular enhancements, and shares her coauthors' commitment to teaching excellence and the continual improvement of statistics presentations.
Beyond the classroom, she has provided statistical advice to numerous business, non-business and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building. Her research activities have led to journal publications, chapters in scholarly books, and conference presentations. Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI DASI. She received a BS from SUNY-Albany, an MS in statistics from the Wharton School of the University of Pennsylvania, and a PhD degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.
David Stephan's professional life has always been shaped by advances in computing. As an undergraduate, he helped professors use statistics software that was considered advanced, even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades). An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson's MathXL, and served as special assistant for computing to the Dean and Provost at Baruch College.
In his many years teaching at Baruch, Stephan implemented the first computer-based classroom; helped redevelop the CIS curriculum; and as part of a FIPSE project team, designed and implemented a multimedia learning environment. He was also nominated for teaching honors. Stephan has presented at SEDSI and DSI DASI (formerly MSMESB) mini-conferences, sometimes with his coauthors. Stephan earned a BA from Franklin & Marshall College and an MS from Baruch College, CUNY, and completed the instructional technology graduate program at Teachers College, Columbia University.
