### Solution Manual For Statistics for Business Decision Making and Analysis 2nd Edition Robert Stine

#### Solution Manual For Statistics for Business Decision Making and Analysis 2nd Edition Robert Stine

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 and the XLSTAT^{™} add-in.

## Table of Contents

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

13.4 Questions to Ask

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

Answers

Photo Acknowledgments

Index

**Supplementary Material (online-only)**

Alternative Approaches to Inference

More Regression

##### 2-Way ANOVA

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#### Solution Manual For Statistics for Business Decision Making and Analysis 2nd Edition Robert Stine