 Statistics: Informed Decisions Using Data, Global Edition (5e) : 9781292157115

# Statistics: Informed Decisions Using Data, Global Edition (5e)

Sullivan

Edition

5
ISBN

9781292157115
ISBN 10

1292157119
Published

03/05/2017

Pearson Higher Ed USA
Pages

976
Format

Available on demand Book
\$136.99

#### Related digital items

\$60.00

##### Description

For courses in introductory statistics.

Statistics: Informed Decisions Using Data gives students the tools to see a bigger picture and make informed choices. As a current introductory statistics instructor, Mike Sullivan III presents a text that is filled with ideas and strategies that work in today’s classroom. His practical emphasis resonates with students and helps them see that statistics is connected, not only to individual concepts, but also with the world at large.

• PART 1: GETTING THE INFORMATION YOU NEED
•  1. Data Collection
• 1.1 Introduction to the Practice of Statistics
• 1.2 Observational Studies versus Designed Experiments
• 1.3 Simple Random Sampling
• 1.4 Other Effective Sampling Methods
• 1.5 Bias in Sampling
• 1.6 The Design of Experiments
• PART 2: DESCRIPTIVE STATISTICS
• 2. Organizing and Summarizing Data
• 2.1 Organizing Qualitative Data
• 2.2 Organizing Quantitative Data: The Popular Displays
• 2.3 Additional Displays of Quantitative Data
• 2.4 Graphical Misrepresentations of Data
• 3. Numerically Summarizing Data
• 3.1 Measures of Central Tendency
• 3.2 Measures of Dispersion
• 3.3 Measures of Central Tendency and Dispersion from Grouped Data
• 3.4 Measures of Position and Outliers
• 3.5 The Five-Number Summary and Boxplots
• 4. Describing the Relation between Two Variables
• 4.1 Scatter Diagrams and Correlation
• 4.2 Least-Squares Regression
• 4.3 Diagnostics on the Least-Squares Regression Line
• 4.4 Contingency Tables and Association
• 4.5 Nonlinear Regression: Transformations (online) 4-1
• PART 3: PROBABILITY AND PROBABILITY DISTRIBUTIONS
• 5. Probability
• 5.1 Probability Rules
• 5.2 The Addition Rule and Complements
• 5.3 Independence and the Multiplication Rule
• 5.4 Conditional Probability and the General Multiplication Rule
• 5.5 Counting Techniques
• 5.6 Putting It Together: Which Method Do I Use?
• 5.7 Bayes’s Rule (online) 5-1
• 6. Discrete Probability Distributions
• 6.1 Discrete Random Variables
• 6.2 The Binomial Probability Distribution
• 6.3 The Poisson Probability Distribution
• 6.4 The Hypergeometric Probability Distribution (online) 6-1
• 7. The Normal Probability Distribution
• 7.1 Properties of the Normal Distribution
• 7.2 Applications of the Normal Distribution
• 7.3 Assessing Normality
• 7.4 The Normal Approximation to the Binomial Probability Distribution
• PART 4: INFERENCE: FROM SAMPLES TO POPULATION
• 8. Sampling Distributions
• 8.1 Distribution of the Sample Mean
• 8.2 Distribution of the Sample Proportion
• 9. Estimating the Value of a Parameter
• 9.1 Estimating a Population Proportion
• 9.2 Estimating a Population Mean
• 9.3 Estimating a Population Standard Deviation
• 9.4 Putting It Together: Which Procedure Do I Use?
• 9.5 Estimating with Bootstrapping
• 10. Hypothesis Tests Regarding a Parameter
• 10.1 The Language of Hypothesis Testing
• 10.2 Hypothesis Tests for a Population Proportion
• 10.3 Hypothesis Tests for a Population Mean
• 10.4 Hypothesis Tests for a Population Standard Deviation
• 10.5 Putting It Together: Which Method Do I Use?
• 10.6 The Probability of a Type II Error and the Power of the Test
• 11. Inferences on Two Samples
• 11.1 Inference about Two Population Proportions
• 11.2 Inference about Two Means: Dependent Samples
• 11.3 Inference about Two Means: Independent Samples
• 11.4 Inference about Two Population Standard Deviations
• 11.5 Putting It Together: Which Method Do I Use?
• 12. Inference on Categorical Data
• 12.1 Goodness-of-Fit Test
• 12.2 Tests for Independence and the Homogeneity of Proportions
• 12.3 Inference about Two Population Proportions: Dependent Samples
• 13. Comparing Three or More Means
• 13.1 Comparing Three or More Means (One-Way Analysis of Variance)
• 13.2 Post Hoc Tests on One-Way Analysis of Variance
• 13.3 The Randomized Complete Block Design
• 13.4 Two-Way Analysis of Variance
• 14. Inference on the Least-Squares Regression Model and Multiple Regression
• 14.1 Testing the Significance of the Least-Squares Regression Model
• 14.2 Confidence and Prediction Intervals
• 14.3 Introduction to Multiple Regression
• 14.4 Interaction and Dummy Variables
• 14.5 Polynomial Regression
• 14.6 Building a Regression Model
• 15. Nonparametric Statistics
• 15.1 An Overview of Nonparametric Statistics
• 15.2 Runs Test for Randomness
• 15.3 Inferences about Measures of Central Tendency
• 15.4 Inferences about the Difference between Two Medians: Dependent Samples
• 15.5 Inferences about the Difference between Two Medians: Independent Samples
• 15.6 Spearman’s Rank-Correlation Test
• 15.7 Kruskal—Wallis Test
##### New to this edition

New and updated features

• Over 350 new and updated Exercises include new emphasis on explaining the results of statistical analysis in students' own words. Answers in the back of the text provide recommended explanations of the statistical results. In addition, exercises have been written to require students to understand pitfalls in faulty statistical analysis.
• Retain Your Knowledge problems help students in recalling skills learned earlier in the course, so that the material is fresh for the final exam. These appear periodically at the end of section exercises.
• Big Data Problems let students analyze data sets with more than 50 observations, which cover tens of thousands of observations with thousands of variables, giving them a taste of professional data analysis.. These problems are marked with an icon and the data is available at www.pearsonglobaleditions.com/sullivan.
• Over 100 new and updated examples keep this text fresh and relevant for today's students.

MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

MyStatLab from Pearson is the world’s leading online resource for teaching and learning statistics; it integrates interactive homework, assessment, and media in a flexible, easy-to-use format. MyStatLab is a course management system that helps individual students succeed. It provides engaging experiences that personalize, stimulate, and measure learning for each student. Tools are embedded to make it easy to integrate statistical software into the course. And, it comes from an experienced partner with educational expertise and an eye on the future.

• Videos develop statistical concepts and provide examples. Every objective in the text is accompanied by both by-hand solutions and technology solutions, where applicable. Most Chapter Test problems also have video solutions available.
• Technology Help in MyStatLab provides step-by-step instructions on how to obtain results using StatCrunch, TI-84 Plus/TI-84 Plus C, and Excel. These are marked with an icon.
• The Instructor Resource Guide provides an overview of each chapter, with detailed points to emphasize within each section, plus suggestions for presenting the material. The guide also provides examples that may be used in the classroom.
• Learning Catalytics Integration: MyStatLab now includes Learning Catalytics, an interactive student response tool that uses students’ smartphones, tablets, or laptops to engage them in more sophisticated tasks and thinking. Now included with MyLab & Mastering with eText, Learning Catalytics enables you to generate classroom discussion, guide your lecture, and promote peer-to-peer learning with real-time analytics. Instructors, you can:
• Monitor responses to find out where students are struggling.
• Use real-time data to adjust your instructional strategy and try other ways of engaging your students during class.
• Manage student interactions by automatically grouping students for discussion, teamwork, and peer-to-peer learning.

##### Features & benefits

Making Informed Decisions: Mike Sullivan helps students connect statistical concepts with their everyday lives, teaching them to think critically and make informed decisions.

• Putting It Together—found in chapter openers, sections, and exercises—connects concepts from different chapters to show statistics as a whole, rather than a series of disconnected procedures. These are indexed at the beginning of the book for easy reference.
• Making an Informed Decision chapter openers pose a question, and then present the statistical concept necessary for prudent decision-making. This feature engages the reader in the statistical-thinking process and highlights the practicality of statistics.

Checking Understanding opportunities appear throughout each section and at the end of every chapter for students to test their knowledge.

• Explaining the Concepts exercises follow Vocabulary & Skill Building and Applying the Concepts exercises at the end of each section. These new exercises ask the student to go beyond applying the concepts and explain their results in written form.
• Preparing for this Section quizzes verify that students have the prerequisite knowledge for the next section, and include page numbers for quick reference.

Practice, Practice, Practice

• NEW! Over 350 new and updated Exercises include new emphasis on explaining the results of statistical analysis in students' own words. Answers in the back of the text provide recommended explanations of the statistical results. In addition, exercises have been written to require students to understand pitfalls in faulty statistical analysis.
• NEW! Retain Your Knowledge problems help students in recalling skills learned earlier in the course, so that the material is fresh for the final exam. These appear periodically at the end of section exercises.
• NEW! Big Data Problems let students analyse data sets with more than 50 observations, which cover tens of thousands of observations with thousands of variables, giving them a taste of professional data analysis.
• Step-by-Step Annotated Examples guide students from problem to solution in three easy-to-follow steps. In this edition, the solutions demonstrate using both by-hand and technology methods, where applicable.
• Problem lays out the scenario of the example.
• Approach provides insight into the thought process behind the methodology used to solve the problem.
• Solution goes through the solution utilising the methodology suggested in the approach.
• “Now Work” problems follow most examples so that students can practice the concepts shown.
• NEW! Over 100 new and updated examples keep this text fresh and relevant for today's students.
• Case Studies conclude each chapter, promoting active learning and helping students apply their knowledge.
• Chapter Review sections include a Chapter Summary, a list of key chapter Vocabulary, and Chapter Objectives with corresponding Review Exercises.
• Chapter Tests help students prepare for exams.