Statistics for Business: Decision Making and Analysis Plus MathXL Student Access Card: International Edition

Description

Features

• The ‘Four M’ approach to problem solving (Motivation, Method, Mechanics, Message) is the consistent methodology used for worked-out examples. This approach gives students a consistent structure for solving problems and presenting their findings in the appropriate context.

• By focusing on answering real questions with data, students learn how to use statistical models and draw conclusions.

• Exercises are divided into five types. Each type focuses on a particular skill to build a deeper understanding of business statistics.
  • Mix and Match and True/False problems test whether students recognize symbols and important steps of calculations.

  • Think About It questions encourage students to pull together concepts and ideas from the chapter; no technology is required.

  • You Do It problems are more computational in nature (statistical software usage is recommended), providing practice with working through the mechanics of solving a problem. These exercises apply the statistical concepts students have learned in the chapter to data related to a business application. Data are available on the included CD-ROM.

  • Four M Questions are richer, more substantive problems that mimic real applications of statistics in business. Data are available on the included CD-ROM.

• Helpful reading features keep students focused and help them avoid errors.
  • Caution icons in the margin indicate a concept that can be particularly troublesome. This icon reminds readers to make sure they understand the concept and helps them avoid making mistakes.

  • Tip icons highlight important ideas or hints within the exposition that readers might otherwise overlook.

  • What Do You Think? boxes throughout each chapter check students’ comprehension of the important ideas in the preceding section, ensuring that they understand the concepts before moving on in the chapter.

• Best Practices and Pitfalls listed at the end of every chapter offer reminders to help students avoid mistakes such as using the wrong method for the situation, or misinterpreting results.

• Deeper coverage points students in the right direction for further exploration, should they want to dive deeper into concepts.
  • Statistics in Action case studies follow each of the four parts of the book. These applications expand on the statistical methods presented within the preceding part and use them to delve into substantive aspects of real-world business cases.

  • Software Hints at the end of each chapter provide relevant commands for popular statistics packages: Excel®, Minitab®, and JMP®.

  • Extensive graphics, including Excel screenshots, provide important pictures of the data for better understanding.

  • Behind the Math sections at the end of most chapters discuss technical details and mathematical justifications of statistical procedures.

• About the Data sections at the end of each chapter provide the sources of the data in the chapter and areas for further research.

Table of Contents

PART 1: VARIATION IN DATA

1. Introduction

1.1 What is Statistics?

1.2 Previews

1.3 How to Use This Book

2. Data

2.1 Data Tables

2.2 Categorical and Numerical Data

2.3 Recoding and Aggregation

2.4 Time Series

2.5 Further Attributes of Data

3. Describing categorical data

3.1 Looking at Data

3.2 Charts of Categorical Data

3.3 The Area Principle

3.4 Mode and Median

4. Describing numerical data

4.1 Summaries of Numerical Variables

4.2 Histograms and the Distribution of Numerical Data

4.3 Boxplot

4.4 Shape of a Distribution

5. Association in categorical data

5.1 Contingency Tables

5.2 Lurking Variables and Simpson's Paradox

5.3 Strength of Association

6. Association in numerical data

6.1 Scatterplots

6.2 Association in Scatterplots

6.3 Measuring Association

6.4 Summarizing Association with a Line

6.5 Spurious Correlation

Statistics in Action: Financial time series

Statistics in Action: Executive compensation

PART 2: PROBABILITY

7. Probability

7.1 From Data to Probability

7.2 Rules for Probability

7.3 Independent Events

7.4 Boole's Inequality

8. Conditional Probability

8.1 From Tables to Probabilities

8.2 Dependent Events

8.3 Organizing Probabilities

8.4 Order in Conditional Probabilities

9. Random Variables

9.1 Properties of Random Variables

9.2 Expected Values

9.3 Comparing Random Variables

10. Association between Random Variables

10.1 Portfolios and Random Variables

10.2 Probability Distribution

10.3 Sums of Random Variables

10.4 Measure Dependence between Random Variables

10.5 IID Random Variables

11. Probability models for Counts

11.1 Random Variables for Counts

11.2 Binomial Model

11.3 Properties of Binomial Random Variables

11.4 Poisson Model

12. Normality

12.1 Normal Random Variable

12.2 The Normal Model

12.3 Percentiles of the Normal Distribution

12.4 Departures from Normality

Statistics in Action: Managing Financial Risk

Statistics in Action: Modeling Sampling Variation

PART 3: INFERENCE

13. Samples and Surveys

13.1 Two Surprises in Sampling

13.2 Variation

13.3 Alternative Sampling Methods

13.4 Checklist for Surveys

14. Sampling Variation and Quality

14.1 Sampling Distribution of the Mean

14.2 Control Limits

14.3 Using a Control Chart

14.4 Control Charts for Variation

15. Confidence Intervals

15.1 Ranges for Parameters

15.2 Confidence Interval for the Mean

15.3 Interpreting Confidence Intervals

15.4 Manipulating Confidence Intervals

15.5 Margin of Error

16. Statistical Tests

16.1 Concepts of Statistical Tests

16.2 Testing the Proportion

16.3 Testing the Mean

16.4 Other Properties of Tests

17. Alternative Approaches to Inference

17.1 A Confidence Interval for the Median

17.2 Transformations and Intervals

17.3 Prediction Intervals

17.4 Proportions Based on Small Samples

18. Comparison

18.1 Data for Comparisons

18.2 Two-sample T-test

18.3 Confidence Interval for the Difference

18.4 Other Comparisons

Statistics in Action: Rare Events

Statistics in Action: Testing Association

PART 4: REGRESSION MODELS

19. Linear Patterns

19.1 Fitting a Line to Data

19.2 Interpreting the Fitted Line

19.3 Properties of Residuals

19.4 Explaining Variation

19.5 Conditions for a Simple Regression

20. Curved Patterns

20.1 Detecting Nonlinear Patterns

20.2 Reciprocal Transformation

20.3 Comparing a Linear and Nonlinear Equation

20.4 Logarithm Transformation

20.5 Comparing Equations

21. Simple Regression

21.1 The Simple Regression Model

21.2 Conditions for the Simple Regression Model

21.3 Inference in Regression

21.4 Prediction Intervals

22. Regression Diagnostics

22.1 Changing Variation

22.2 Leveraged Outliers

22.3 Dependent Errors and Time Series

23. Multiple Regression

23.1 The Multiple Regression Model

23.2 Interpreting Multiple Regression

23.3 Checking Conditions

23.4 Inference in Multiple Regression

23.5 Steps in Building a Multiple Regression

24. Building Regression Models

24.1 Identifying Explanatory Variables

24.2 Collinearity

24.3 Removing Explanatory Variables

25. Categorical Explanatory Variables

25.1 Two-sample Comparisons

25.2 Analysis of Covariance

25.3 Checking Conditions

25.4 Interactions and Inference

25.5 Regression with Several Groups

26. Analysis of Variance

26.1 Comparing Several Groups

26.2 Inference in Anova Regression Models

26.3 Multiple Comparisons

26.4 Groups of Different Size

27. Time Series

27.1 Decomposing a Time Series

27.2 Regression Models

27.3 Checking Conditions

Statistics in Action: Analyzing Experiments

Statistics in Action: Automated Regression Modeling

Author

Robert Stine holds a PhD from Princeton University. He has taught at the Wharton School since 1983, during which time he has regularly taught business statistics. During his tenure, Bob has received a variety of teaching awards. Bob also actively consults for industry. His clients include the pharmaceutical firms Merck and Pfizer, and he regularly works with the Federal Reserve Bank of Philadelphia on models for retail credit risk. This collaboration has produced three well-received conferences held at Wharton. His areas of research include computer software, time series analysis and forecasting, and general problems related to model identification and selection. Bob has published numerous articles in research journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, and The Annals of Statistics.

Dean Foster holds a PhD from the University of Maryland. He has taught at the Wharton School since 1992 and previously taught at the University of Chicago. Dean teaches courses in introductory business statistics, probability and Markov chains, statistical computing, and advanced statistics for managers. Dean’s research areas are statistical inference for stochastic processes, game theory, machine learning, and variable selection. He is published in a wide variety of journals, including The Annals of Statistics, Operations Research, Games and Economic Behaviour, Journal of Theoretical Population Biology, andEconometrica.

Bob Stine and Dean Foster have co-authored two casebooks: Basic Business Statistics (Springer-Verlag) and Business Analysis Using Regression (Springer-Verlag). These casebooks offer a collection of data analysis examples that motivate and illustrate key ideas of statistics, ranging from standard error to regression diagnostics and time series analysis. They also have collaborated on a number of research articles.

The textbook’s greatest feature is its focus on the question: “What is important to business today?” All of the book’s features have been carefully developed to build skills that will help students stand out from the crowd when entering the business world.