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Statistics: The Art and Science of Learning from Data: International Edition de Alan Agresti

Statistics: The Art and Science of Learning from Data: International Edition

Autor Alan Agresti, Christine Franklin
en Limba Engleză Mixed media product – 31 dec 2007
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 Edition helps students 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 students 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 students to be comfortable with analyzing both quantitative and categorical data. Every day in the media, students see and hear percentages and rates being 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 statistic texts.


The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report.


Datasets and other resources (where applicable) for this book are available here.
 
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Specificații

ISBN-13: 9780131357464
ISBN-10: 0131357468
Pagini: 848
Dimensiuni: 216 x 276 mm
Greutate: 1.72 kg
Ediția:2Nouă
Editura: Pearson Education
Colecția Pearson Education
Locul publicării:Upper Saddle River, United States

Cuprins

PART 1: GATHERING and EXPLORING DATA
 
1. Statistics: The Art and Science of Learning from Data
1.1 How Can You Investigate Using Data?
1.2 We Learn about Population Using Samples
1.3 What Role do Computers Play in Statistics?
            Chapter Summary
            Chapter Exercises
 
2. Exploring Data with Graphs and Numerical Summaries
2.1 What Are the Types of Data?
2.2 How Can We Describe Data using Graphical Summaries?
2.3 How Can We Describe the Center of Quantitative Data?
2.4 How Can We Describe the Spread of Quantitative Data?
2.5 How Can Measures of Position Describe Spread?
2.6 How Can Graphical Summaries Be Misused?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
3. Association: Contingency, Correlation, and Regression
3.1 How Can We Explore the Association between Two Categorical Variables?
3.2 How Can We Explore the Association between Two Quantitative Variables?
3.3 How Can We Predict the Outcome of a Variable?
3.4 What are Some Cautions in Analyzing Associations?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
4. Gathering Data
4.1 Should We Experiment or Should We Merely Observe?
4.2 What Are Good Ways and Poor Ways to Sample?
4.3 What Are Good Ways and Poor Ways to Experiment?
4.4 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
 
PART 2: PROBABILITY AND PROBABILITY DISTRIBUTIONS
 
5. Probability in our Daily Lives
5.1 How Can Probability Quantify Randomness?
5.2 How Can We Find Probabilities?
5.3 Conditional Probability: What’s the Probability of A, Given B?
5.4 Applying the Probability Rules
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
6. Probability Distributions
6.1 How Can We Summarize Possible Outcomes and Their Probabilities?
6.2 How Can We Find Probabilities for Bell-Shaped Distributions?
6.3 How Can We Find Probabilities when Each Observation has Two Possible Outcomes?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
7. Sampling Distributions
7.1 How Likely Are the Possible Values of a Statistics? The Sampling Distribution
7.2 How Close Are Sample Means to Population Means?
7.3 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
 
PART 3: INFERENCE STATISTICS
 
8. Statistical Inference: Confidence Intervals
8.1 What Are Point and Interval Estimates of Population Parameters?
8.2 How Can We Construct a Confidence Interval to Estimate a Population Proportion?
8.3 How Can We Construct a Confidence Interval to Estimate a Population Mean?
8.4 How Do We Choose the Sample Size for a Study?
8.5 How Do Computers Make New Estimation Methods Possible?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
9. Statistical Inference: Significance Tests about Hypotheses
9.1 What Are the Steps for Performing a Significance Test?
9.2 Significance Tests about Proportions
9.3 Significance Tests about Means
9.4 Decisions and Types of Errors in Significance Tests
9.5 Limitations of Significance Tests
9.6 How Likely is a Type II Error (Not Rejecting H0, Even though it’s False)?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
10. Comparing Two Groups
10.1 Categorical Response: How Can We Compare Two Proportions?
10.2 Quantitative Response: How Can We Compare Two Means?
10.3 Other Ways of Comparing Means and Comparing Proportions
10.4 How Can We Analyze Dependent Samples?
10.5 How Can We Adjust for Effects of Other Variables?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
            PART 3 REVIEW
            Part 3 Summary
            Part 3 Exercises
 
PART 4: ANALYZING ASSOCIATIONS AND EXTENDED STATISTICAL METHODS
 
11. Analyzing the Association Between Categorical Variables
11.1 What is Independence and What is Association?
11.2 How Can We Test Whether Categorical Variables are Independent?
11.3 How Strong is the Association?
11.4 How Can Residuals Reveal the Pattern of Association?
11.5 What if the Sample Size is Small? Fisher’s Exact Test
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
12. Analyzing the Association Between Quantitative Variables: Regression Analysis
12.1 How Can We “Model” How Two Variables Are Related?
12.2 How Can We Describe Strength of Association?
12.3 How Can We Make Inferences about the Association?
12.4 What Do We Learn from How the Data Vary around the Regression Line?
12.5 Exponential Regression: A Model for Nonlinearity
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
13. Multiple Regression
13.1 How Can We Use Several Variables to Predict a Response?
13.2 Extending the Correlation and R-squared for Multiple Regression
13.3 How Can We Use Multiple Regression to Make Inferences?
13.4 Checking a Regression Model Using Residual Plots
13.5 How Can Regression Include Categorical Predictors?
13.6 How Can We Model a Categorical Response?
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
14. Comparing Groups: Analysis of Variance Methods
14.1 How Can We Compare Several Means?: One-Way ANOVA
14.2 How Should We Follow Up an ANOVA F Test
14.3 What if there are Two Factors?: Two-way ANOVA
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
15. Nonparametric Statistics
15.1 How Can We Compare Two Groups by Ranking?
15.2 Nonparametric Methods for Several Groups and for Matched Pairs
            Answers to Chapter Figure Questions
            Chapter Summary
            Chapter Exercises
 
            PART 4 REVIEW
            Part 4 Summary
            Part 4 Exercises
 
Tables
Selected Answers
Index
Index of Applications     
Photo Credits

Notă biografică

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 article 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.

Caracteristici

  • Examples emphasize thinking about and understanding statistics through analysis of current real-world data. The unique five-step format encourages students to model the thought processes required to examine issues in statistics.
    1. Picture the Scenario presents background information so that students can picture the context of the data.
    2. Questions to Explore show students the appropriate questions to ask about the scenario and focus on what students should learn from the example.
    3. Think It Through is the heart of each example, as the Questions to Explore are investigated and answered using the appropriate statistical methods.
    4. Insight clarifies the central ideas investigated in the example and places them in a broader context that states the conclusions in less technical terms. This step also connects concepts from other sections in the book.
    5. To Practice This Concept, try…: Following each example, students are directed to an end-of-section exercise that allows immediate practice of the concept.
  • More than 100 exercises per chapter motivate and test student comprehension through interesting real-data problems. Exercises address relevant and through-provoking topics, such as cell phone usage and cancer, and public support for the death penalty.

    Exercises are divided into three categories to challenge students on different levels:
    • Practicing the Basics reinforce basic applications of methods.
    • Concepts and Investigations require students to explore the theory and concepts presented in the chapter through real data sets.
    • Student Activities are appropriate for individual or group work and often make use of the applets that accompany the text.
  • Examples and exercises reflect the data that students see in the popular press–opinion polls, medical studies, sports data, and economic reports, for example–that are summarized using percentages and rates. To help students make sense of these data, greater emphasis is placed on proportions at various points in the text.
    • Contingency tables are introduced early (Chapter 3) to illustrate the concept of association between two categorical variables and to show the potential influence of a lurking variable.
    • Sampling distributions and inference methods are introduced first using proportions for a binary outcome and then means for quantitative data are examined.
  • Active Learning is encouraged through the use of simulations and hands-on activities. Activities and exercises that direct students to the applets, the web, and other resources are built into the book and are on the CD. While most activities are optional, the use of the Sampling Distributions Applet now plays a central role in introducing that challenging concept (Chapter 7). Additional suggestions for activities may be found in the Instructor’s “blue pages.”
  • Modern technological techniques develop concepts and analyze data. Output from computer software (such as Minitab®, SPSS®, Microsoft Excel®) and the TI-83/84 Plus graphing calculator are used to illustrate many concepts.
  • Helpful text features offer at-a-glance information to aid students in comprehending material in the main text.
    • In Words summarizes complicated symbolic notation and formal definitions in a non-technical, less formal way to help students understand “what it really means.”
    • In Practice boxes alert students to the way statisticians actually analyze data in practice.
    • Recall directs students back to previous presentations in the text to review definitions/formulas and to reinforce key concepts in context.
  • Annotated figures feature labels to identify noteworthy aspects in each illustration that may not be obvious to inexperienced students. 

Caracteristici noi

  • A new chapter devoted exclusively to Sampling Distributions (Chapter 7) separates this material from Probability Distributions (Chapter 6) to provide a more focused and better paced introduction. The use of the sampling distribution applet motivates the discussion and provides a compelling visual introduction to this challenging concept.
  • 30% new or updated exercises ensure students have ample opportunity to practice techniques and apply their knowledge. Updated, current data comes from a variety of sources, including the 2006 General Social Survey.
  • A New Part organization divides the book into four Parts. Each Part concludes with a substantive Part Review to help students understand the “big picture” and solve exercises that review the key concepts, ideas, and techniques.  Included are Summary Questions that conclude each Part; Summary Examples that continue the example-driven presentation of this text; and comprehensivePart Exercises.
  • Refined and expanded applets provide students with greater opportunity and more flexibility to conduct simulations that will help them visualize and understand key concepts.
  • Every new copy of the Student Edition comes with a CD-ROM that contains data sets, additional activities, and applets, which have been expanded and enhanced for this edition.
  • The CD-ROM included with the Instructor’s Edition contains Instructor-to-Instructor videos for every chapter that detail the authors’ approach to concepts and their suggestions for presentation. These videos compliment the Instructors Edition’s chapter introductions - the “blue pages.”