Statistics : The Art and Science of Learning from Data

by Agresti, Alan; Franklin, Christine

• ISBN13: 9780135131992
• ISBN10: 0135131995
• Edition: 2nd
• Format: Hardcover
• Copyright: 2013-01-17
• Publisher: Pearson

Summary

KEY MESSAGE: Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text.Statistics: The Art and Science of Learning from Data, Second Editionhelps readers become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to readers without compromising necessary rigor. The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current, and successfully illustrate the relevance of statistics. The authors believe that it is important for readers to be comfortable with analyzing both quantitative and categorical data. Every day in the media, percentages and rates are used to summarize opinion polls, outcomes of medical studies, and economic reports. As a result, greater attention is paid to the analysis of proportions than is typical of many introductory statistics texts. The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report. KEY TOPICS: Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypothesis; Comparing Two Groups; Analyzing the Associations Between Categorical Variables; Analyzing Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics MARKET: For all readers interested in statistics.

Author Biography

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 35 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed articles and five texts including "Statistical Methods for the Social Sciences" (with Barbara Finlay, Prentice Hall, 4th edition 2009) and "Categorical Data Analysis" (Wiley, 2nd edition 2002). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003 Alan was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in 2004 he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 30 countries worldwide. Alan has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons.

 

Christine Franklin is a Senior Lecturer and Honors Professor in the Department of Statistics at the University of Georgia. She has been a member of college faculty in statistics for almost 30 years. Chris has been actively involved at the national level with promoting statistical education at the K-12 level and college undergraduate level since the 1980's. She is currently the Chief Reader for AP Statistics and has developed three masters level courses at UGA in data analysis for elementary, middle school, and secondary teachers. Chris was the lead writer for the ASA endorsed Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K-12 Curriculum Framework.

 

Chris has been honored by her selection as a Fellow of the American Statistical Association, the 2006 Mu Sigma Rho National Statistical Education Award recipient for her teaching and lifetime devotion to statistics education, and numerous teaching and advising awards at UGA. Chris has written more than 30 journal articles and resource materials for textbooks.

Table of Contents

Gathering and Exploring Data

Statistics: The Art and Science of Learning from Data

How Can You Investigate Using Data?

We Learn about Population Using Samples

What Role do Computers Play in Statistics?

Chapter Summary

Chapter Exercises

Exploring Data with Graphs and Numerical Summaries

What Are the Types of Data?

How Can We Describe Data using Graphical Summaries?

How Can We Describe the Center of Quantitative Data?

How Can We Describe the Spread of Quantitative Data?

How Can Measures of Position Describe Spread?

How Can Graphical Summaries Be Misused?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Association: Contingency, Correlation, and Regression

How Can We Explore the Association between Two Categorical Variables?

How Can We Explore the Association between Two Quantitative Variables?

How Can We Predict the Outcome of a Variable?

What are Some Cautions in Analyzing Associations?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Gathering Data

Should We Experiment or Should We Merely Observe?

What Are Good Ways and Poor Ways to Sample?

What Are Good Ways and Poor Ways to Experiment?

What Are Other Ways to Perform Experimental and Nonexperimental Studies?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Part 1 Review

Part 1 Summary

Part 1 Exercises

Probability and Probability Distributions

Probability in our Daily Lives

How Can Probability Quantify Randomness?

How Can We Find Probabilities?

Conditional Probability: What's the Probability of A, Given B?

Applying the Probability Rules

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Probability Distributions

How Can We Summarize Possible Outcomes and Their Probabilities?

How Can We Find Probabilities for Bell-Shaped Distributions?

How Can We Find Probabilities when Each Observation has Two Possible Outcomes?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Sampling Distributions

How Likely Are the Possible Values of a Statistics?

The Sampling Distribution

How Close Are Sample Means to Population Means?

How Can We Make Inferences about a Population?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Part 2 Review

Part 2 Summary

Part 2 Exercises

Inference Statistics

Statistical Inference: Confidence Intervals

What Are Point and Interval Estimates of Population Parameters?

How Can We Construct a Confidence Interval to Estimate a Population Proportion?

How Can We Construct a Confidence Interval to Estimate a Population Mean?

How Do We Choose the Sample Size for a Study?

How Do Computers Make New Estimation Methods Possible?

Answers to Chapter Figure Questions

Chapter Summary

Chapter Exercises

Statistical Inference: Significance Tests about Hypotheses

What Are the Steps for Performing a Significance Test?

Significance Tests about Proportions

Significance Tests about Means

Decisions and Types of Errors in Significance Tests

Limitations of Significance Tests

How Likely is a Type II Error (Not Rejecting H0, Even though it's False)?

Answers to Chapter Figure Questions

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