Statistics: The Art and Science of Learning from Data, Global Edition

Description
Table of Contents

Statistics: The Art and Science of Learning From Data, 5th Edition helps you understand what statistics is all about and learn the right questions to ask when analyzing data, instead of just memorizing procedures. It makes accessible the ideas that have turned statistics into a central science of modern life, without compromising essential material. Students often find this book enjoyable to read and stay engaged with the wide variety of real-world data in the examples and exercises. Based on the authors' belief that it's important for you to learn and analyze both quantitative and categorial data, this text pays greater attention to the analysis of proportions than many other introductory statistics texts.

Key features include:

Greater attention to the analysis of proportions compared to other introductory statistics texts.
Introduction to key concepts, presenting the categorical data first, and quantitative data after.
A wide variety of real-world data in the examples and exercises
New sections and updated content will enhance your learning and understanding.

Pearson MyLab^® Students, if Pearson Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

PART I: GATHERING AND EXPLORING DATA

Statistics: The Art and Science of Learning from Data
- Using Data to Answer Statistical Questions
- Sample Versus Population
- Organizing Data, Statistical Software, and the New Field of Data Science
- Chapter Summary
- Chapter Exercises
Exploring Data with Graphs and Numerical Summaries
- Different Types of Data
- Graphical Summaries of Data
- Measuring the Center of Quantitative Data
- Measuring the Variability of Quantitative Data
- Using Measures of Position to Describe Variability
- Linear Transformations and Standardizing
- Recognizing and Avoiding Misuses of Graphical Summaries
- Chapter Summary
- Chapter Exercises
Exploring Relationships Between Two Variables
- The Association Between Two Categorical Variables
- The Relationship Between Two Quantitative Variables
- Linear Regression: Predicting the Outcome of a Variable
- Cautions in Analyzing Associations
- Chapter Summary
- Chapter Exercises
Gathering Data
- Experimental and Observational Studies
- Good and Poor Ways to Sample
- Good and Poor Ways to Experiment
- Other Ways to Conduct Experimental and Nonexperimental Studies
- Chapter Summary
- Chapter Exercises

PART II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLINGDISTRIBUTIONS

Probability in Our Daily Lives
- How Probability Quantifies Randomness
- Finding Probabilities
- Conditional Probability
- Applying the Probability Rules
- Chapter Summary
- Chapter Exercises
Random Variables and Probability Distributions
- Summarizing Possible Outcomes and Their Probabilities
- Probabilities for Bell-Shaped Distributions
- Probabilities When Each Observation Has Two Possible Outcomes
- Chapter Summary
- Chapter Exercises
Sampling Distributions
- How Sample Proportions Vary Around the Population Proportion
- How Sample Means Vary Around the Population Mean
- Using the Bootstrap to Find Sampling Distributions
- Chapter Summary
- Chapter Exercises

PART III: INFERENTIAL STATISTICS

Statistical Inference: Confidence Intervals
- Point and Interval Estimates of Population Parameters
- Confidence Interval for a Population Proportion
- Confidence Interval for a Population Mean
- Bootstrap Confidence Intervals
- Chapter Summary
- Chapter Exercises
Statistical Inference: Significance Tests About Hypotheses
- Steps for Performing a Significance Test
- Significance Tests About Proportions
- Significance Tests About a Mean
- Decisions and Types of Errors in Significance Tests
- Limitations of Significance Tests
- The Likelihood of a Type II Error
- Chapter Summary
- Chapter Exercises
Comparing Two Groups
- Categorical Response: Comparing Two Proportions
- Quantitative Response: Comparing Two Means
- Comparing Two Groups with Bootstrap or Permutation Resampling
- Analyzing Dependent Samples
- Adjusting for the Effects of Other Variables
- Chapter Summary
- Chapter Exercises

PART IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICALMETHODS

Analyzing the Association Between Categorical Variables
- Independence and Dependence (Association)
- Testing Categorical Variables for Independence
- Determining the Strength of the Association
- Using Residuals to Reveal the Pattern of Association
- Fisher's Exact and Permutation Tests
- Chapter Summary
- Chapter Exercises
Analyzing the Association Between Quantitative Variables: Regression Analysis
- Modeling How Two Variables Are Related
- Inference About Model Parameters and the Association
- Describing the Strength of Association
- How the Data Vary Around the Regression Line
- Exponential Regression: A Model for Nonlinearity
- Chapter Summary
- Chapter Exercises
Multiple Regression
- Using Several Variables to Predict a Response
- Extending the Correlation and R2 for Multiple Regression
- Using Multiple Regression to Make Inferences
- Checking a Regression Model Using Residual Plots
- Regression and Categorical Predictors
- Modeling a Categorical Response
- Chapter Summary
- Chapter Exercises
Comparing Groups: Analysis of Variance Methods
- One-Way ANOVA: Comparing Several Means
- Estimating Differences in Groups for a Single Factor
- Two-Way ANOVA
- Chapter Summary
- Chapter Exercises
Nonparametric Statistics
- Compare Two Groups by Ranking
- Nonparametric Methods for Several Groups and for Matched Pairs
- Chapter Summary
- Chapter Exercises

Appendix
Answers
Index
Index of Applications
Credits