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Practical Business Statistics, Seventh Edition, provides a conceptual, realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize mathematical correctness. The book provides deep understanding of how to learn from data and how to deal with uncertainty while promoting the use of practical computer applications. This valuable, accessible approach teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results.
The text uses excellent examples with real world data relating to business sector functional areas such as finance, accounting, and marketing. Written in an engaging style, this timely revision is class-tested and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details.
- Provides users with a conceptual, realistic, and matter-of-fact approach to managerial statistics
- Offers an accessible approach to teach present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand concepts and to interpret results
- Features updated examples and graphics (200+ figures) to illustrate important applied uses and current business trends
- Includes robust ancillary instructional materials such as an instructor’s manual, lecture slides, and data files to save you time when preparing for class
Undergraduate students of business, finance, economics, and statistics
- Statistical Graphics
- Extensive Development: Reviews and Class Testing
- Writing Style
- PowerPoint Slides
- Companion Website
- Instructor’s Manual
- To the Student
About the Author
Part I: Introduction and Descriptive Statistics
- Chapter 1: Introduction: Defining the Role of Statistics in Business
- 1.1 Why Statistics?
- 1.2 What is Statistics?
- 1.3 The Five Basic Activities of Statistics
- 1.4 Data Mining and Big Data
- 1.5 What is Probability?
- 1.6 General Advice
- 1.7 End-of-Chapter Materials
- Chapter 2: Data Structures: Classifying the Various Types of Data Sets
- 2.1 How Many Variables?
- 2.2 Quantitative Data: Numbers
- 2.3 Qualitative Data: Categories
- 2.4 Time-Series and Cross-Sectional Data
- 2.5 Sources of Data, Including the Internet
- 2.6 End-of-Chapter Materials
- Chapter 3: Histograms: Looking at the Distribution of Data
- 3.1 A List of Data
- 3.2 Using a Histogram to Display the Frequencies
- 3.3 Normal Distributions
- 3.4 Skewed Distributions and Data Transformation
- 3.5 Bimodal Distributions With Two Groups
- 3.6 Outliers
- 3.7 Data Mining With Histograms
- 3.8 End-of-Chapter Materials
- Chapter 4: Landmark Summaries: Interpreting Typical Values and Percentiles
- 4.1 What is The Most Typical Value?
- 4.2 What Percentile is it?
- 4.3 End-of-Chapter Materials
- Chapter 5: Variability: Dealing with Diversity
- 5.1 The Standard Deviation: The Traditional Choice
- 5.2 The Range: Quick and Superficial
- 5.3 The Coefficient of Variation: A Relative Variability Measure
- 5.4 Effects of Adding to or Rescaling the Data
- 5.5 End-of-Chapter Materials
Part II: Probability
- Chapter 6: Probability: Understanding Random Situations
- 6.1 An Example: is it Behind Door Number 1, Door Number 2, or Door Number 3?
- 6.2 How Can You Analyze Uncertainty?
- 6.3 How Likely is An Event?
- 6.4 How Can You Combine Information About More Than One Event?
- 6.5 What is the Best Way to Solve Probability Problems?
- 6.6 End-of-Chapter Materials
- Chapter 7: Random Variables: Working with Uncertain Numbers
- 7.1 Discrete Random Variables
- 7.2 The Binomial Distribution
- 7.3 The Normal Distribution
- 7.4 The Normal Approximation to the Binomial
- 7.5 Two Other Distributions: The Poisson and The Exponential
- 7.6 End-of-Chapter Materials
Part III: Statistical Inference
- Chapter 8: Random Sampling: Planning Ahead for Data Gathering
- 8.1 Populations and Samples
- 8.2 The Random Sample
- 8.3 The Sampling Distribution and the Central Limit Theorem
- 8.4 A Standard Error is an Estimated Standard Deviation
- 8.5 Other Sampling Methods
- 8.6 End-of-Chapter Materials
- Chapter 9: Confidence Intervals: Admitting That Estimates Are Not Exact
- 9.1 The Confidence Interval for a Population Mean or a Population Percentage
- 9.2 Assumptions Needed for Validity
- 9.3 Interpreting a Confidence Interval
- 9.4 One-Sided Confidence Intervals
- 9.5 Prediction Intervals
- 9.6 End-of-Chapter Materials
- Chapter 10: Hypothesis Testing: Deciding Between Reality and Coincidence
- 10.1 Hypotheses Are Not Created Equal!
- 10.2 Testing the Population Mean Against a Known Reference Value: The t-Test
- 10.3 Interpreting a Hypothesis Test
- 10.4 One-Sided Testing
- 10.5 Testing Whether or not a New Observation Comes From the Same Population
- 10.6 Testing Two Samples
- 10.7 End-of-Chapter Materials
Part IV: Regression and Time Series
- Chapter 11: Correlation and Regression: Measuring and Predicting Relationships
- 11.1 Exploring Relationships Using Scatterplots and Correlations
- 11.2 Regression: Prediction of One Variable From Another
- 11.3 End-of-Chapter Materials
- Chapter 12: Multiple Regression: Predicting One Variable From Several Others
- 12.1 Interpreting the Results of a Multiple Regression
- 12.2 Pitfalls and Problems in Multiple Regression
- 12.3 Dealing With Nonlinear Relationships and Unequal Variability
- 12.4 Indicator Variables: Predicting From Categories
- 12.5 End-of-Chapter Materials
- Chapter 13: Report Writing: Communicating the Results of a Multiple Regression
- 13.1 How to Organize Your Report
- 13.2 Hints and Tips
- 13.3 Example: A Quick Pricing Formula for Customer Inquiries
- 13.4 End-of-Chapter Materials
- Chapter 14: Time Series: Understanding Changes Over Time
- 14.1 An Overview of Time-Series Analysis
- 14.2 Trend-Seasonal Analysis
- 14.3 Modeling Cyclic Behavior Using Box-Jenkins ARIMA Processes
- 14.4 End-of-Chapter Materials
Part V: Methods and Applications
- Chapter 15: ANOVA: Testing for Differences Among Many Samples and Much More
- 15.1 Using Box Plots to Look at Many Samples at Once
- 15.2 The F Test Tells You If the Averages are Significantly Different
- 15.3 The Least-Significant-Difference Test: Which Pairs are Different?
- 15.4 More Advanced ANOVA Designs
- 15.5 End-of-Chapter Materials
- Chapter 16: Nonparametrics: Testing with Ordinal Data or Nonnormal Distributions
- 16.1 Testing the Median Against a Known Reference Value
- 16.2 Testing for Differences in Paired Data
- 16.3 Testing to See if Two Unpaired Samples are Significantly Different
- 16.4 End-of-Chapter Materials
- Chapter 17: Chi-Squared Analysis: Testing for Patterns in Qualitative Data
- 17.1 Summarizing Qualitative Data by Using Counts and Percentages
- 17.2 Testing if Population Percentages are Equal to Known Reference Values
- 17.3 Testing for Association Between Two Qualitative Variables
- 17.4 End-of-Chapter Materials
- Chapter 18: Quality Control: Recognizing and Managing Variation
- 18.1 Processes and Causes of Variation
- 18.2 Control Charts and How to Read Them
- 18.3 Charting a Quantitative Measurement with X¯ and R Charts
- 18.4 Charting the Percent Defective
- 18.5 End-of-Chapter Materials
Appendix A: Employee Database
Appendix B: Donations Database
Appendix C: Self-Test: Solutions to Selected Problems and Database Exercises
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Chapter 10
- Chapter 11
- Chapter 12
- Chapter 13
- Chapter 14
- Chapter 15
- Chapter 16
- Chapter 17
- Chapter 18
Appendix D: Statistical Tables
- Table D.5 R2 Table: Level 5% Critical Values (Significant)
- No. of pages:
- © Academic Press 2016
- 16th August 2016
- Academic Press
- eBook ISBN:
- Paperback ISBN:
Andrew F. Siegel holds the Grant I. Butterbaugh Professorship in Quantitative Methods and Finance at the Michael G. Foster School of Business, University of Washington, Seattle, and is also Adjunct Professor in the Department of Statistics. His Ph.D. is in statistics from Stanford University (1977). Before settling in Seattle, he held teaching and/ or research positions at Harvard University, the University of Wisconsin, the RAND Corporation, the Smithsonian Institution, and Princeton University. He has taught statistics at both undergraduate and graduate levels, and earned seven teaching awards in 2015 and 2016. The interest-rate model he developed with Charles Nelson (the Nelson-Siegel Model) is in use at central banks around the world. His work has been translated into Chinese and Russian. His articles have appeared in many publications, including the Journal of the American Statistical Association, the Encyclopedia of Statistical Sciences, the American Statistician, Proceedings of the National Academy of Sciences, Nature, the American Mathematical Monthly, the Journal of the Royal Statistical Society, the Annals of Statistics, the Annals of Probability, the Society for Industrial and Applied Mathematics Journal on Scientific and Statistical Computing, Statistics in Medicine, Biometrika, Biometrics, Statistical Applications in Genetics and Molecular Biology, Mathematical Finance, Contemporary Accounting Research, the Journal of Finance, and the Journal of Applied Probability.
Professor of Information Systems and Operations Management, Professor of Finance and Business Economics, and Adjunct Professor of Statistics, Foster School of Business, University of Washington, Seattle, WA, USA
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