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Business Statistics: A First Course Plus NEW MyLab Statistics with Pearson eText -- Access Card Package, 2nd Edition

By Norean D. Sharpe, Richard D. De Veaux, Paul F. Velleman

Published by Pearson

Published Date: Apr 23, 2013

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In Business Statistics: A First Course, the authors leverage their unique blend of teaching, consulting, and entrepreneurial experiences to bring a modern business edge and dynamic approach to teaching statistics to business students. Focusing on statistics in the context of real business issues, the text emphasizes analysis and understanding over computation. This approach helps students be analytical, preparing them to make better business decisions and effectively communicate results. The authors have an accessible and compelling writing style and use short, targeted chapters to build understanding of new topics. They integrate current business applications to capture students’ attention and teach statistical concepts needed in the modern business world.


The Second Edition provides a wealth of examples and exercises so that the story is always tied to the way statistics is used to make better business decisions. New to this edition are For Examples (illustrative examples), Section Exercises (single-concept exercises), and part-ending Case Studies (which are more in-depth than the Brief Cases located at the end of chapters). To help students become proficient with technology, the Second Edition includes instructions for JMP®, Minitab®, and SPSS®, as well as new and expanded coverage of Excel® 2010 and the add-in XLSTAT for Pearson. Screenshots of output are included throughout the chapters.


0321890256 / 9780321890252 Business Statistics: A First Course Plus MyStatLab -- Access Card Package

Package consists of:   

0321838696 / 9780321838698 Business Statistics: A First Course

0321847997 / 9780321847997 My StatLab Glue-in Access Card

032184839X / 9780321848390 MyStatLab Inside Sticker for Glue-In Packages


Table of Contents

Part I Exploring and Collecting Data


Chapter 1 Statistics and Variation

1.1  So, What is Statistics?

1.2   How Will This Book Help?


Chapter 2 Data

2.1 What Are Data?

2.2 Variable Types

2.3 Data Sources: Where, How and When


Chapter 3 Surveys and Sampling

3.1 Three Ideas of Sampling

3.2 Population Parameters

3.3 Common Sampling Designs

3.4 The Valid Survey

3.5 How to Sample Badly


Chapter 4 Displaying and Describing Categorical Data

4.1 Summarizing a Categorical Variable

4.2 Displaying a Categorical Variable

4.3 Exploring Two Categorical Variables: Contingency Tables


Chapter 5 Displaying and Describing Quantitative Data

5.1 Displaying Quantative Variables

5.2 Shape

5.3 Center

5.4 Spread of the Distribution

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


Chapter 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 Caustation

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?


Part II Understanding Data and Distributions


Chapter 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’ Rule


Chapter 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


Chapter 9 Sampling Distributions and Confidence Intervals for Proportions

9.1 The Distribution of Sample Proportions

9.2 Sampling Distributions for Proportions

9.3 The Central Limit Theorem

9.4 A Confidence Interval

9.5 Margin of Error: Certainty vs. Precision

9.6 Assumptions and Conditions

9.7 Choosing the Sample Size

A Confidence Interval for Small Samples – on CD


Chapter 10 Testing Hypotheses about Proportions

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 Testing

10.9 Two Types of Errors

*10.10 Power


Chapter 11 Confidence Intervals and Hypothesis Tests for Means

11.1 The Sampling Distribution of the Mean

11.2 How Sampling Distribution Models Work

11.3 Gossett and the t-Distribution

11.4 Confidence Interval for Means

11.5 Assumptions and Conditions

11.6 Testing Hypothesis about Means – the One-Sample t-Test


Chapter 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


Chapter 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 Tests of Homogeneity

13.5 Comparing Two Proportions

13.6 Chi-Square Test of Independence


Part III Building Models for Decision Making


Chapter 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

Transforming (Re-Expressing) Data – on CD

The Ladder of Powers – on CD


Chapter 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

Indicator (or Dummy) Variables – on CD

Adjusting for Different Slopes

Interaction Terms – on CD

Collinearity – on CD


Chapter 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

16.7 Data Mining Algorithms

16.8 The Data Mining Process

16.9 Summary


*Indicates an optional topic



A Answers A

B Photo Acknowledgments

C Tables and Selected Formulas

D Index

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