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Business Statistics: For Contemporary Decision Making, 5th Edition
Description: Black makes the concepts of statistics much easier to understand by presenting them in the setting
of business decision-making. This includes the use of real world examples, real business data and
applications. The fifth edition has also been substantially revised and reorganized in order to group
similar approaches, topics, and techniques more closely together. Each chapter offers a brief
discussion about how the material fits into the larger scheme.
Authors Bio:
Ken Black is currently Professor of Decision Sciences in the School of Business and Public
Administration at the University of Houston-Clear Lake. Born in Cambridge, Massachusetts and
raised in Missouri, he earned a Bachelor's degree in mathematics from Graceland College, a
Master's degree in math education from the University of Texas at El Paso, a Ph.D. in business
administration in management science, and a Ph.D. in educational research from the University of
North Texas. Since joining the faculty in 1979, Professor Black has taught all levels of statistics
courses, forecasting, management science, market research, and production/operations
management. He published fifteen journal articles and over twenty professional papers, as well as
two textbooks: Business Statistics: An Introductory Course and Business Statistics: Contemporary
Decision Making. Black has consulted for many different companies, including Aetna, City of
Houston, NYLCare, At&T, Johnson Space Center, Southwest Information resource, Connect
Corporation, and Eagle Engineering.
Contents: Unit I: Introduction.
Chapter 1 Introduction to Statistics.
Chapter 2 Charts and Graphs.
Chapter 3 Descriptive Statistics.
Chapter 4 Probability.
Unit II: Distributions and Sampling.
Chapter 5 Discrete Distributions.
Chapter 6 Continuous Distributions.
Chapter 7 Sampling and Sampling Distributions.
Unit III: Making Inferences about Population Parameters.
Chapter 8 Statistical Inference: Estimation for Single Populations.
Chapter 9 Statistical Inference: Hypothesis Testing for Single Populations.
Chapter 10 Statistical Inferences about Two Populations.
Chapter 11 Analysis of Variance and Design of Experiments.
Chapter 12 Analysis of Categorical Data.
Chapter 13 Nonparametric Statistics.
Unit IV: Regression, Forecasting, and Quality: Relationships, Prediction, and Process Improvement.
Chapter 14 Simple Regression Analysis.
Chapter 15 Multiple Regression Analysis.
Chapter 16 Building Multiple Regression Models.
Chapter 17 Time-Series Forecasting and Index Numbers.
Chapter 18 Statistical Quality Control.
Appendix A Tables.
Appendix B Answers to Selected Odd-Numbered Quantitative Problems.
Glossary.
Index.
Chapter 19 Decision Analysis.
Supplement 1 Summation Notation.
Supplement 2 Derivation of Simple Regression Formulas for Slope and y Intercept.
Supplement 3 Advanced Exponential Smoothing.
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