# Business Statistics: A First Course, A La Carte with MML/MSL Student Access Kit (adhoc for valuepacks)

Published Date: Jul 1, 2010

## Description

Books à la Carte are unbound, three-hole-punch versions of the textbook. This lower cost option is easy to transport and comes with same access code or media that would be packaged with the bound book.

Professors Norean Sharpe (Georgetown University), Dick De Veaux (Williams College), and Paul Velleman (Cornell University) have taught at the finest business schools and draw on their consulting experience at leading companies to show readers how statistical thinking is vital to modern decision making. Managers make better business decisions when they understand statistics, and Business Statistics gives readers the statistical tools and understanding to take them from the classroom to the boardroom. Hundreds of examples are based on current events and timely business topics. Short, accessible chapters allow for flexible coverage of important topics, and the conversational writing style maintains readers’ interest and improves understanding.

This Package Contains:

Business Statistics: A First Course, (à la Carte edition) with MyMathLab/MyStatLab Student Access Kit

PART I EXPLORING AND COLLECTING DATA

1. Statistics and Variation

1.1 So, What Is Statistics?

1.2 How Will This Book Help?

2. Data

2.1 What Are Data?

2.2 Variable Types

2.3 Data Sources—Where, How, and When

3. Surveys and Sampling

3.1 Three Ideas of Sampling

3.2 A Census—Does It Make Sense?

3.3 Populations and Parameters

3.4 Simple Random Sample (SRS)

3.5 Other Sample Designs

3.6 Defining the Population

3.7 The Valid Survey

4. Displaying and Describing Categorical Data

4.1 The Three Rules of Data Analysis

4.2 Frequency Tables

4.3 Charts

4.4 Contingency Tables

5. Displaying and Describing Quantitative Data

5.1 Displaying Distributions

5.2 Shape

5.3 Center

5.5 Shape, Center, and Spread—A Summary

5.6 Five-Number Summary and Boxplots

5.7 Comparing Groups

5.8 Identifying Outliers

5.9 Standardizing

*5.10 Time Series Plots

Transforming Skewed Data—On CD-ROM

6. Correlation and Linear Regression

6.1 Looking at Scatterplots

6.2 Assigning Roles to Variables in Scatterplots

6.3 Understanding Correlation

6.4 Lurking Variables and Causation

6.5 The Linear Model

6.6 Correlation and the Line

6.7 Regression to the Mean

6.8 Checking the Model

6.9 Variation in the Model and R2

6.10 Reality Check: Is the Regression Reasonable?

Straightening Scatterplots—On CD-ROM

PART II UNDERSTANDING DATA AND DISTRIBUTIONS

7. Randomness and Probability

7.1 Random Phenomena and Probability

7.2 The Nonexistent Law of Averages

7.3 Different Types of Probability

7.4 Probability Rules

7.5 Joint Probability and Contingency Tables

7.6 Conditional Probability

7.7 Constructing Contingency Tables

7.8 Probability Trees

*7.9 Reversing the Conditioning: Bayes’s Rule

8. Random Variables and Probability Models

8.1 Expected Value of a Random Variable

8.2 Standard Deviation of a Random Variable

8.3 Properties of Expected Values and Variances

8.4 Discrete Probability Models

8.5 Continuous Random Variables

9. Sampling Distributions and Confidence Intervals for Proportions

9.1 Simulations

9.2 The Sampling Distribution for Proportions

9.3 Assumptions and Conditions

9.4 The Central Limit Theorem—The Fundamental Theorem of Statistics

9.5 A Confidence Interval

9.6 Margin of Error: Certainty vs. Precision

9.7 Critical Values

9.8 Assumptions and Conditions

9.9 Choosing the Sample Size

A Confidence Interval for Small Samples—On CD-ROM

10.1 Hypotheses

10.2 A Trial as a Hypothesis Test

10.3 P-Values

10.4 The Reasoning of Hypothesis Testing

10.5 Alternative Hypotheses

10.6 Alpha Levels and Significance

10.7 Critical Values

10.8 Confidence Intervals and Hypothesis Tests

10.9 Two Types of Errors

*10.10 Power

11. Confidence Intervals and Hypothesis Tests for Means

11.1 The Sampling Distribution for Means

11.2 How Sampling Distribution Models Work

11.3 Gossett and the t-Distribution

11.4 A Confidence Interval for Means

11.5 Assumptions and Conditions

11.6 Cautions About Interpreting Confidence Intervals

11.7 One-Sample t-Test

11.8 Sample Size

*11.9 Degrees of Freedom—Why n - 1

12. Comparing Two Groups

12.1 Comparing Two Means

12.2 The Two-Sample t-Test

12.3 Assumptions and Conditions

12.4 A Confidence Interval for the Difference Between Two Means

*12.5 The Pooled t-Test

*12.6 Tukey’s Quick Test

12.7 Paired Data

12.8 The Paired t-Test

13. Inference for Counts: Chi-Square Tests

13.1 Goodness-of-Fit Tests

13.2 Interpreting Chi-Square Values

13.3 Examining the Residuals

13.4 The Chi-Square Test of Homogeneity

13.5 Comparing Two Proportions

13.6 Chi-Square Test of Independence

PART III  BUILDING MODELS FOR DECISION MAKING

14. Inference for Regression

14.1 The Population and the Sample

14.2 Assumptions and Conditions

14.3 Regression Inference

14.4 Standard Errors for Predicted Values

14.5 Using Confidence and Prediction Intervals

14.6 Extrapolation and Prediction

14.7 Unusual and Extraordinary Observations

*14.8 Working with Summary Values

*14.9 Linearity

Re-expressing data—On CD-ROM

15. Multiple Regression

15.1 The Multiple Regression Model

15.2 Interpreting Multiple Regression Coefficients

15.3 Assumptions and Conditions for the Multiple Regression Model

15.4 Testing the Multiple Regression Model

15.5 Adjusted R2 and the F-statistic

The Logistic Regression Model—On CD-ROM

Indicator Variables—On CD-ROM

Adjusting for Different Slopes— Interaction Terms—On CD-ROM

Collinearity—On CD-ROM

16. Introduction to Data Mining

16.1 Direct Marketing

16.2 The Data

16.3 The Goals of Data Mining

16.4 Data Mining Myths

16.5 Successful Data Mining

16.6 Data Mining Problems

16.7 Data Mining Algorithms

16.8 The Data Mining Process

16.9 Summary

*Indicates an optional topic