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Statistics: The Art and Science of Learning from Data [With CDROM]

Statistics: The Art and Science of Learning from Data [With CDROM] Cover

 

Synopses & Reviews

Publisher Comments:

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 Edition helps 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.

 

KEY TOPICS: GATHERING and EXPLORING DATA; Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; PROBABILITY AND PROBABILITY DISTRIBUTIONS; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; INFERENCE STATISTICS; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypotheses; Comparing Two Groups; ANALYZING ASSOCIATIONS AND EXTENDED STATISTICAL METHODS; Analyzing the Association Between Categorical Variables; Analyzing the Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics

 

MARKET: for all readers interested in statistics.

Synopsis:

Never HIGHLIGHT a Book Again! Virtually all testable terms, concepts, persons, places, and events are included. Cram101 Textbook Outlines gives all of the outlines, highlights, notes for your textbook with optional online practice tests. Only Cram101 Outlines are Textbook Specific. Cram101 is NOT the Textbook.

Synopsis:

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 Edition helps 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.

 

KEY TOPICS: GATHERING and EXPLORING DATA; Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; PROBABILITY AND PROBABILITY DISTRIBUTIONS; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; INFERENCE STATISTICS; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypotheses; Comparing Two Groups; ANALYZING ASSOCIATIONS AND EXTENDED STATISTICAL METHODS; Analyzing the Association Between Categorical Variables; Analyzing the Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics

 

MARKET: for all readers interested in statistics.

About the Author

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

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

Product Details

ISBN:
9780135131992
Subtitle:
The Art and Science of Learning from Data
Publisher:
Pearson
Author:
Lewis Priestley, Jennifer
Author:
Franklin, Christine
Author:
Mocko, Megan
Author:
Petkewich, Maureen
Author:
Flanagan-Hyde, Peter
Author:
Cram101 Textbook Reviews, Textbook Revie
Author:
Franklin
Author:
Morse, Jack
Author:
Agresti, Alan
Author:
Streett, Sarah
Author:
Cram101 Textbook Reviews
Author:
Christine
Author:
Hydorn, Debra
Author:
Franklin, Chris
Author:
Ripol, Maria
Author:
Kowalski, Michael
Subject:
Statistics
Subject:
Probability & Statistics - General
Subject:
General
Subject:
Mathematics | Probability and Statistics
Subject:
Education-General
Copyright:
Edition Description:
Trade paper
Series:
MyStatLab Series
Publication Date:
December 2007
Binding:
Hardback
Grade Level:
College/higher education:
Language:
English
Illustrations:
Y
Pages:
848
Dimensions:
11.1 x 8.8 x 1.3 in 1882 gr

Related Subjects

Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics
Textbooks » General

Statistics: The Art and Science of Learning from Data [With CDROM]
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$ In Stock
Product details 848 pages Pearson Prentice Hall - English 9780135131992 Reviews:
"Synopsis" by , Never HIGHLIGHT a Book Again! Virtually all testable terms, concepts, persons, places, and events are included. Cram101 Textbook Outlines gives all of the outlines, highlights, notes for your textbook with optional online practice tests. Only Cram101 Outlines are Textbook Specific. Cram101 is NOT the Textbook.
"Synopsis" by ,

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 Edition helps 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.

 

KEY TOPICS: GATHERING and EXPLORING DATA; Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; PROBABILITY AND PROBABILITY DISTRIBUTIONS; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; INFERENCE STATISTICS; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypotheses; Comparing Two Groups; ANALYZING ASSOCIATIONS AND EXTENDED STATISTICAL METHODS; Analyzing the Association Between Categorical Variables; Analyzing the Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics

 

MARKET: for all readers interested in statistics.

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