Statistics The Art and Science of Learning from Data

ISBN-10: 0130083690
ISBN-13: 9780130083692
Edition: 2007
List price: $137.33
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Description: For algebra-based Introductory Statistics Courses. The overarching goal of this text is to empower students to be statistical thinkers Alan Agresti and Chris Franklin have merged their research expertise, as well as their extensive  More...

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Book details

List price: $137.33
Copyright year: 2007
Publisher: Prentice Hall PTR
Binding: Hardcover
Pages: 768
Size: 8.50" wide x 11.00" long x 1.25" tall
Weight: 3.278
Language: English

For algebra-based Introductory Statistics Courses. The overarching goal of this text is to empower students to be statistical thinkers Alan Agresti and Chris Franklin have merged their research expertise, as well as their extensive real-world and teaching experience, to develop a new introductory statistics text that makes students statistically literate, while encouraging them to ask and answer interesting statistical questions. The authors have successfully crafted a text that 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 understand that most of the real-world data that students encounter outside the class room are categorical data. Unlike many texts at this level, Agresti/Franklin incorporates categorical data where appropriate and draws distinctions between theory and practical application of statistical ideas and methods. The text was written, from the ground up, to embrace and support the 6 recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report - http://www.amstat.org/education/gaise/GAISECollege.htm : Emphasize statistical literacy and develop statistical thinking. Use real data. Stress conceptual understanding rather than mere knowledge of procedures. Foster active learning in the classroom. Use technology for developing concepts and analyzing data. Use assessment to evaluate and improve student learning. Comes with a CD containing data sets, additional activities, and applets. The CD with the IE, furthermore, includes Instructor-to-Instructor videos which further detail, by chapter, the authors approach and provides suggestions on how to present concepts. These Instructor-to-Instructor videos are very complimentary to the IE chapter introductions.

Alan Agrestiis Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 30 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 four texts including "Statistics: The Art and Science of Learning From Data" (withnbsp;Christine Franklin,nbsp;Prentice Hall, 2nd 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 he 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 20 countries worldwide. He has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons.

Chapter Summary
Summary of notation
Chapter Exercises
Statistics: The Art and Science of Learning from Data
How Can You Investigate Using Data?
We Learn about Populations Using Samples
What Role do Computers Play in Statistics?
Exploring Data with Graphs and Numerical Summaries
1What 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 Are Descriptive Summaries Misused?
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?
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 Observational Studies?
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
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
How Likely Are the Possible Values of a Statistic?: The Sampling Distribution
How Close Are Sample Means to Population Means
How Can We Make Inferences about a Population?
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?
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 Type Error (Not Rejecting Even though it's False)?
Comparing Two Groups
Categorical Response: How Can We Compare Two Proportions?
Quantitative Response: How Can We Compare Two Means?
Other Ways of Comparing Means and Comparing Proportions
How Can We Analyze Dependent Samples?
How Can We Adjust for Effects of Other Variables?
Analyzing the Association Between Categorical Variables
What Is Independence and What Is Association?
WhaHow Can We Test whether Categorical Variables are Independent?
How Strong is the Association?
How Can Residuals Reveal the Pattern of Association?
If the Sample Size is Small? Fisher's Exact Test
Analyzing Association between Quantitative Variables: Regression Analysis
How Can We "Model" How Two Variables Are Related?
How Can We Describe Strength of Association?
How Can We Make Inferences about the Association?
What Do We Learn from How the Data Vary around the Regression Line?
Exponential Regression: A Model for Nonlinearity
Multiple Regression
How Can We Use Several Variables to Predict a Response?
Extending the Correlation and R-squared for Multiple Regression
How Can We Use Multiple Regression to Make Inferences?
Checking a Regression Model Using Residual Plots
How Can Regression Include Categorical Predictors?
How Can We Model a Categorical Response?
Comparing Groups: Analysis of Variance Methods
How Can We Compare Several Means?: One-Way ANOVA
How Should We Follow Up an ANOVA F test
What if there Are Two Factors?: Two-way ANOVA
Nonparametric Statistics
How Can We Compare Two Groups by Ranking?
Nonparametric Methods for Several Groups and for Matched Pairs
Appendices
Tables
Selected Answers
Index
Photo Credits

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