# Statistics for Business: Decision Making and Analysis / Edition 2

ISBN-10: 0321836510

ISBN-13: 9780321836519

Pub. Date: 01/16/2013

Publisher: Pearson

In Statistics for Business: Decision Making and Analysis, authors Robert Stine and Dean Foster of the University of Pennsylvania’s Wharton School, take a sophisticated approach to teaching statistics in the context of making good business decisions. The authors show students how to recognize and understand each business question, use

## Overview

In Statistics for Business: Decision Making and Analysis, authors Robert Stine and Dean Foster of the University of Pennsylvania’s Wharton School, take a sophisticated approach to teaching statistics in the context of making good business decisions. The authors show students how to recognize and understand each business question, use statistical tools to do the analysis, and how to communicate their results clearly and concisely. In addition to providing cases and real data to demonstrate real business situations, this text provides resources to support understanding and engagement. A successful problem-solving framework in the 4-M Examples (Motivation, Method, Mechanics, Message) model a clear outline for solving problems, new What Do You Think questions give students an opportunity to stop and check their understanding as they read, and new learning objectives guide students through each chapter and help them to review major goals. Software Hints provide instructions for using the most up-to-date technology packages. The Second Edition also includes expanded coverage and instruction of Excel® 2010.

## Product Details

ISBN-13:
9780321836519
Publisher:
Pearson
Publication date:
01/16/2013
Edition description:
New Edition
Pages:
864
Sales rank:
575,348
Product dimensions:
8.40(w) x 11.00(h) x 1.40(d)

Preface

Index of Application

PART ONE: VARIATION

1. Introduction

1.1 What Is Statistics?

1.2 Previews

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

Chapter Summary

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

Chapter Summary

4. Describing Numerical Data

4.1 Summaries of Numerical Variables

4.2 Histograms

4.3 Boxplot

4.4 Shape of a Distribution

4.5 Epilog

Chapter Summary

5. Association between Categorical Variables

5.1 Contingency Tables

5.2 Lurking Variables and Simpson's Paradox

5.3 Strength of Association

Chapter Summary

6. Association between Quantitative Variables

6.1 Scatterplots

6.2 Association in Scatterplots

6.3 Measuring Association

6.4 Summarizing Association with a Line

6.5 Spurious Correlation

Chapter Summary

Statistics in Action: Financial Time Series

Statistics in Action: Executive Compensation

PART TWO: PROBABILITY

7. Probability

7.1 From Data to Probability

7.2 Rules for Probability

7.3 Independent Events

Chapter Summary

8. Conditional Probability

8.1 From Tables to Probabilities

8.2 Dependent Events

8.3 O rganizing Probabilities

8.4 O rder in Conditional Probabilities

Chapter Summary

9. Random Variables

9.1 Random Variables

9.2 Properties of Random Variables

9.3 Properties of Expected Values

9.4 Comparing Random Variables

Chapter Summary

10. Association between Random Variables

10.1 Portfolios and Random Variables

10.2 Joint Probability Distribution

10.3 Sums of Random Variables

10.4 Dependence between Random Variables

10.5 IID Random Variables

10.6 Weighted Sums

Chapter Summary

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

Chapter Summary

12. The Normal Probability Model

12.1 Normal Random Variable

12.2 The Normal Model

12.3 Percentiles

12.4 Departures from Normality

Chapter Summary

Statistics in Action: Managing Financial Risk

Statistics in Action: Modeling Sampling Variation

PART THREE: INFERENCE

13. Samples and Surveys

13.1 Two Surprising Properties of Samples

13.2 Variation

13.3 Alternative Sampling Methods

Chapter Summary

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

Chapter Summary

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

Chapter Summary

16. Statistical Tests

16.1 Concepts of Statistical Tests

16.2 Testing the Proportion

16.3 Testing the Mean

16.4 Significance versus Importance

16.5 Confidence Interval or Test?

Chapter Summary

17. Comparison

17.1 Data for Comparisons

17.2 Two-Sample z-test for Proportions

17.3 Two-Sample Confidence Interval for Proportions

17.4 Two-Sample T-test

17.5 Confidence Interval for the Difference between Means

17.6 Paired Comparisons

Chapter Summary

18. Inference for Counts

18.1 Chi-Squared Tests

18.2 Test of Independence

18.3 General versus Specific Hypotheses

18.4 Tests of Goodness of Fit

Chapter Summary

Statistics in Action: Rare Events

Statistics in Action: Data Mining Using Chi-Squared

PART FOUR: 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 Simple Regression

Chapter Summary

20. Curved Patterns

20.1 Detecting Nonlinear Patterns

20.2 Transformations

20.3 Reciprocal Transformation

20.4 Logarithm Transformation

Chapter Summary

21. The Simple Regression Model

21.1 The Simple Regression Model

21.2 Conditions for the SRM

21.3 Inference in Regression

21.4 Prediction Intervals

Chapter Summary

22. Regression Diagnostics

22.1 Changing Variation

22.2 Outliers

22.3 Dependent Errors and Time Series

Chapter Summary

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 Fitting a Multiple Regression

Chapter Summary

24. Building Regression Models

24.1 Identifying Explanatory Variables

24.2 Collinearity

24.3 Removing Explanatory Variables

Chapter Summary

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

Chapter Summary

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

Chapter Summary

27. Time Series

27.1 Decomposing a Time Series

27.2 Regression Models

27.3 Checking the Model

Chapter Summary

Statistics in Action: Analyzing Experiments

Statistics in Action: Automated Modeling

Appendix: Tables

Photo Acknowledgments

Index

Supplementary Material (online-only)

Alternative Approaches to Inference

More Regression

2-Way ANOVA

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