Discovering Business Statistics, 2nd Edition

by Quinton Nottingham and James S. Hawkes

Discovering Business Statistics introduces modern statistical methodologies in the context of contemporary business applications and is designed to prepare students to analyze data and make informed decisions in a competitive global marketplace. In the newly expanded second edition, each chapter begins with a Discovering the Real World experience that introduces the content of the chapter using the lens of relevant business contexts and ends with a Discovery Project that ask students to use the content and analysis techniques discussed to make business decisions in a variety of practical situations. Each chapter also includes a Discovering Technology section that provides students with step-by-step instructions for using a variety of technologies including graphing calculators, Microsoft Excel, JMP, and Minitab to perform the statistical analyses explored in the chapter. Newly expanded exercise sets are designed to build fluency through practice. New examples have been added throughout the text that incorporate contemporary data sets to address the present-day concerns of students such as smartphone screen time usage, the COVID-19 pandemic, and credit card debt.

Notable content updates to the second edition include a new chapter on Time Series Analysis and Forecasting, as well as new sections addressing residual analysis, the assessment of normality, confidence interval estimation of the population standard deviation and variance, hypothesis testing of two population variances, and multiple comparison procedures for analysis of variance. Additionally, chapters addressing hypothesis testing have been updated to reflect the most modern methodology used by practitioners.

Formats: Software, Textbook, eBook

Product ISBN
Textbook 978-1-64277-511-2
Software + eBook + Textbook 978-1-64277-510-5

About the Authors:

Dr. Quinton J. Nottingham

Dr. Quinton J. Nottingham

Quinton J. Nottingham is Associate Professor of Business Information Technology (BIT) in the Pamplin College of Business at Virginia Tech. He earned his B.S. (1989), M.S. (1991), and Ph.D. (1995) degrees in Statistics from Virginia Tech. Professor Nottingham has served on the faculty at Virginia Tech since 1995. He primarily teaches the Quantitative Methods courses and the MBA-level Managerial Statistics. As a Master Online Instructor, he has also delivered the Quantitative Methods courses online with the use of the Hawkes Learning Systems: Discovering Business Statistics software.

Professor Nottingham has published numerous articles in the areas of applied statistics, regression, nonparametric regression, logistic regression, time series analysis, artificial intelligence, and security. He has served on the board of the Southeastern Decision Sciences Institute (SEDSI) as the VP of Student Liaison, VP Finance, Program Chair, as well as the President (2011-2012). He is a member of or has held membership in the Institute for Operations Research and Management Science (INFORMS), Decision Sciences Institute (DSI), American Statistical Association (ASA), Institute of Mathematical Sciences, and the Virginia Academy of Sciences.

Professor Nottingham enjoys spending time with his family and friends, vacationing at various beaches in the southeast, playing golf and basketball, and lifting weights. As a former Virginia Tech basketball player, he is an avid supporter of Virginia Tech Athletics and can be found in attendance at many Virginia Tech sporting events.

Dr. James S. Hawkes

Dr. James S. Hawkes

James S. Hawkes earned his Bachelor’s degree at the University of Richmond (1969), a Master’s of Business Administration at New York University (1972), and his Ph.D. in Management Science at Clemson University (1978). As a faculty member at the College of Charleston from 1977 to 1998, he primarily taught Business Statistics. In 1978, he created Hawkes Learning Systems (HLS) where he is currently CEO. In 1986, Dr. Hawkes designed a breakthrough teaching technology called Adventures in Statistics. This software was the first to use expert system methodology to teach problem solving in statistics. Successive generations of the software expanded the content and improved the expert system. The software was renamed Hawkes Learning Systems: Statistics and is the basis for the software that accompanies this textbook, Discovering Business Statistics.

Other accomplishments include the founding of Quant Systems India (QSI), a software development company located in Vishakaputnam, India in 1994. QSI focuses on software development and quality assurance of HLS software. Dr. Hawkes also co-founded Automated Trading Desk in 1988, a proprietary stock trading company that traded approximately six percent of the NYSE and NASDAQ market places before it was sold to Citi Group in 2008. Most recently, he created The Writer’s Muse which was an outgrowth of his hobby of collecting interesting phrases. This company has produced a phrase thesaurus which is available for smart phones, tablets and personal computers. Dr. Hawkes enjoys composing music, playing bridge, and collecting hats.

Table of Contents

  1. Chapter 1: Decision Making Using Statistics
    1. 1.1 Statistics and Global Issues
    2. 1.2 Statistics and Quality
    3. 1.3 Descriptive Statistics versus Inferential Statistics
    4. 1.4 The Value of Statistical Literacy
    5. Chapter 1 Review
  2. Chapter 2: Data, Big Data, and Analytics
    1. 2.1 Data and Decision Making
    2. 2.2 Big Data and Business Analytics
    3. 2.3 Data Classifications
    4. 2.4 Time Series Data and Cross-Sectional Data
    5. Chapter 2 Review
  3. Chapter 3: Organizing, Displaying, and Interpreting Data
    1. 3.1 Frequency Distributions
    2. 3.2 Displaying Qualitative Data
    3. 3.3 Constructing Frequency Distributions for Quantitative Data
    4. 3.4 Graphical Displays of Quantitative Data
    5. 3.5 Analyzing Graphs
    6. Chapter 3 Review
  4. Chapter 4: Numerical Descriptive Statistics
    1. 4.1 Measures of Location
    2. 4.2 Measures of Dispersion
    3. 4.3 Measures of Relative Position
    4. 4.4 Data Subsetting
    5. 4.5 Analyzing Grouped Data
    6. 4.6 Proportions
    7. 4.7 Measures of Association Between Two Variables
    8. Chapter 4 Review
  5. Chapter 5: Probability, Randomness, and Uncertainty
    1. 5.1 Introduction to Probability
    2. 5.2 Laws of Probability
    3. 5.3 Conditional Probability
    4. 5.4 Independence
    5. 5.5 Bayes' Theorem
    6. 5.6 Counting Techniques
    7. Chapter 5 Review
  6. Chapter 6: Discrete Probability Distributions: About the Future
    1. 6.1 Types of Random Variables
    2. 6.2 Discrete Random Variables
    3. 6.3 The Discrete Uniform Distribution
    4. 6.4 The Binomial Distribution
    5. 6.5 The Poisson Distribution
    6. 6.6 The Hypergeometric Distribution
    7. Chapter 6 Review
  7. Chapter 7: Continuous Random Variables
    1. 7.1 The Uniform Distribution
    2. 7.2 The Normal Distribution
    3. 7.3 Assessing Normality Graphically
    4. 7.4 The Standard Normal Distribution
    5. 7.5 Approximations to Other Distributions
    6. Chapter 7 Review
  8. Chapter 8: Samples and Sampling Distributions
    1. 8.1 Random Samples and Sampling Distributions
    2. 8.2 The Distribution of the Sample Mean and the Central Limit Theorem
    3. 8.3 The Distribution of the Sample Proportion
    4. 8.4 Sampling Methods
    5. Chapter 8 Review
  9. Chapter 9: Estimation with Confidence Intervals: Single Sample
    1. 9.1 Estimating the Population Mean, Sigma Known
    2. 9.2 Estimating the Population Mean, Sigma Unknown
    3. 9.3 Estimating the Population Proportion
    4. 9.4 Estimating the Population Standard Deviation or Variance
    5. Chapter 9 Review
  10. Chapter 10: Hypothesis Testing: Single Sample
    1. 10.1 Introduction to Hypothesis Testing
    2. 10.2 Testing a Hypothesis about a Population Mean, Sigma Known
    3. 10.3 Testing a Hypothesis about a Population Mean, Sigma Unknown
    4. 10.4 The Relationship Between Confidence Interval Estimation and Hypothesis Testing
    5. 10.5 Testing a Hypothesis about a Population Proportion
    6. 10.6 Testing a Hypothesis about a Population Variance
    7. Chapter 10 Review
  11. Chapter 11: Inferences about Two Samples
    1. 11.1 Comparing Two Population Means, Sigma 1 and Sigma 2 Known
    2. 11.2 Comparing Two Population Means, Sigma 1 and Sigma 2 Unknown
    3. 11.3 Paired Difference Test
    4. 11.4 Comparing Two Population Proportions
    5. 11.5 Comparing Two Population Variances
    6. Chapter 11 Review
  12. Chapter 12: Analysis of Variance (ANOVA)
    1. 12.1 Introduction to Analysis of Variance (ANOVA)
    2. 12.2 Assumptions in an ANOVA Test
    3. 12.3 The F-Distribution and the F-Test
    4. 12.4 Multiple Comparison Procedures
    5. 12.5 Two-Way ANOVA: The Randomized Block Design
    6. 12.6 Two-Way ANOVA: The Factorial Design
    7. Chapter 12 Review
  13. Chapter 13: Regression, Inference, and Model Building
    1. 13.1 The Simple Linear Regression Model
    2. 13.2 Residual Analysis
    3. 13.3 Evaluating the Fit of the Linear Regression Model
    4. 13.4 Fitting a Linear Time Trend
    5. 13.5 Inference Concerning the Slope
    6. 13.6 Inference Concerning the Model's Prediction
    7. Chapter 13 Review
  14. Chapter 14: Multiple Regression
    1. 14.1 The Multiple Regression Model
    2. 14.2 The Coefficient of Determination and Adjusted R^2
    3. 14.3 Inference Concerning the Multiple Regression Model and Its Coefficients
    4. 14.4 Inference Concerning the Model's Prediction
    5. 14.5 Models with Qualitative Independent Variables
    6. 14.6 Additional Topics in Multiple Regression
    7. Chapter 14 Review
  15. Chapter 15: Time Series Analysis and Forecasting
    1. 15.1 Time Series Components
    2. 15.2 Moving Averages
    3. 15.3 Exponential Smoothing Techniques
    4. 15.4 Forecast Accuracy
    5. 15.5 Seasonality
    6. Chapter 15 Review
  16. Chapter 16: Looking for Relationships in Qualitative Data
    1. 16.1 The Chi-Square Distribution
    2. 16.2 The Chi-Square Test for Goodness of Fit
    3. 16.3 The Chi-Square Test for Association
    4. Chapter 16 Review
  17. Chapter 17: Nonparametric Statistics
    1. 17.1 The Sign Test
    2. 17.2 The Wilcoxon Signed-Rank Test
    3. 17.3 The Wilcoxon Rank-Sum Test
    4. 17.4 The Rank Correlation Test
    5. 17.5 The Runs Test for Randomness
    6. 17.6 The Kruskal-Wallis Test
    7. Chapter 17 Review
  18. Chapter 18: Statistical Process Control
    1. 18.1 Basic Charts and Diagrams Used in Quality Control
    2. 18.2 Basic Concepts
    3. 18.3 Monitoring with x-Bar and R Charts
    4. 18.4 Monitoring with a p Chart
    5. Chapter 18 Review
  19. Appendix
    1. A.1 Name that Distribution
    2. A.2 Direct Mail
    3. A.3 Type II Errors
    4. A.4 Games of Chance