The new edition of Business Statistics in Practice provides a modern, practical, and unique framework for teaching the first course in business statistics. This framework features case study and example-driven discussions of all basic business statistics topics. In addition, the authors have rewritten many of the discussions in this edition and have explained concepts more simply from first principles. The only prerequisite for this text is high school algebra.
1. An Introduction to Business Statistics
2. Descriptive Statistics: Tabular and Graphical Methods
3. Descriptive Statistics: Numerical Methods
5. Discrete Random Variables
6. Continuous Random Variables
7. Sampling Distributions
8. Confidence Intervals
9. Hypothesis Testing
10. Statistical Inferences Based on Two Samples
11. Experimental Design and Analysis of Variance
12. Chi-Square Tests
13. Simple Linear Regression Analysis
14. Multiple Regression
15. Model Building and Model Diagnostics
16. Time Series Forecasting
17. Process Improvement Using Control Charts
18. Nonparametric Methods
19. Decision Theory
Appendix A: Statistical Tables
Appendix B: Counting Rules
Appendix C: Hypergeometric Distribution
Appendix D: Properties of the Mean and the Variance of a Random Variable, and the Covariance
Appendix E: Derivations of the Mean and Variance of x(bar) and p(hat
New to this Edition
McGraw-Hill's Connect uses exercises pulled directly from the textbook to create static and algorithmic questions that can be used for practice, homework, quizzes, and tests.
Continuing case studies that tie together different statistical topics.
Business improvement conclusions that explicitly show how statistical results lead to practical business decisions.
New chapter introductions that list learning objectives and preview the case study analysis to be carried out in each chapter.
A shorter and more intuitive introduction to business statistics in Chapter 1.
A streamlined discussion of the graphical and numerical methods of descriptive statistics in Chapters 2 and 3.
An improved discussion of probability and probability distributions.
A simplified, unique, and more inferentially oriented approach to sampling distributions.
A simpler discussion of confidence intervals employing a more graphical approach.
A simpler and more streamlined discussion of hypothesis testing.
A new and better flowing discussion of simple and multiple regression analysis.
Increased emphasis on Excel (and to some extent, MINITAB) throughout the text.
Two shorter chapters on descriptive statistics with simplified and clarified explanations. Chapter 2 discusses the graphical and tabular methods of descriptive statistics. Chapter 3 discusses the numerical methods of descriptive statistics, with new material on covariance, correlation, and the least squares line. Both chapters utilize a substantial number of new examples, exercises, cases, and data sets, including The Jeep Case and The Household Income Case.
Use of the cumulative normal table in the discussion of the normal distribution in Chapter 6 and throughout the rest of the book. Because use of the cumulative normal table makes many normal curve calculations easier and is consistent with the way that most statistical software systems give normal curve probabilities, the 6th edition uses this table for all normal curve applications.
A clearer and more motivating discussion of sampling distributions in Chapter 7. Chapter 7 begins with a new case: The Risk Reduction Case: Game Shows and Stock Returns. This case motivates students to think about the properties of sampling distributions in a "fun? and familiar context.
A clearer and more motivating introduction to confidence intervals in Chapter 8. Chapter 8 begins with an intuitive example that illustrates a practical application of a confidence interval for a population mean. Then, using this example as a springboard, Chapter 8 develops the logic behind and a formula for a confidence interval for a population mean (as well as other confidence interval formulas). One new real world case exercise in Chapter 8 is The Air Traffic Control Case.
A simpler and streamlined discussion of hypothesis testing in Chapter 9. As in the previous edition, this basic hypothesis testing chapter uses a seven-step procedure that breaks hypothesis testing down into small, easy to understand steps and clearly shows how to use the book's hypothesis testing summary boxes. In addition, the material on hypothesis tests for a population mean has been simplified and streamlined for the 6th edition. A motivating new case " The Valentine's Day Chocolate Case - is used throughout the discussion of hypothesis testing.
Three chapters on regression analysis. Chapter 13 discusses simple linear regression analysis, including an introduction to residual analysis. Chapter 14 discusses multiple regression analysis, including the use of dummy variables and analyzing residuals from a multiple regression model. Chapter 15 discusses model building and model diagnostics, including the use of quadratic and interaction terms, logistic regression, identifying outlying and influential observations, data transformations, and the Durbin-Watson test. Chapters 13 and 14 contain significantly simplified presentations of the simple linear repression model, multiple regression models, the least square point estimates, and confidence and prediction intervals. Furthermore, the more advanced material in Chapter 15 has been designed for maximum teaching flexibility. For example, the discussions of quadratic and interaction terms in Chapter 15 can be studied before or after the material on dummy variables in Chapter 14. In addition, the presentations of data transformations and the Durbin-Watson test in Chapter 15 can be covered after studying residual analysis in simple linear regression (in Chapter 13) or, alternatively, after studying residual analysis in multiple regression (in Chapter 14).
Inclusion of Holt-Winters? double exponential smoothing and multiplicative Winters? method in Chapter 16, which discusses time series forecasting.
A `Business Improvement? theme, connecting statistical analysis and business decision making, is highlighted and called out with the BI icons in the book.
The z vs. t decision is governed by sigma known/unknown rather than by sample size. This is a major change reflecting a new direction in academia.
Hypothesis testing is approached using a stepped method, which makes the material easier to learn. This method received outstanding reviews.
Excel and Minitab appendices are found in most chapters. These appendices illuminate chapter content through the use of comprehensive, visual walkthroughs that feature complete screen captures and step-by-step examples.
Student-Friendly pedagogy: These step by step examples and summary boxes to aid student understanding of what often can be overwhelming content.
Continuing Case Study Examples are uniquely integrated across chapters.
The importance of variation is emphasized beginning with Chapter 1 and continuing throughout the text.
This text utilizes a flexible topic flow that facilitates different topic choices and encourages different teaching approaches. Instructors want flexibility, and through the use of optional sections, appendices, and self-learning exercises, this is facilitated.
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