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STAT2 (2nd Edition)

Modeling with Regression and ANOVA

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WH Freeman

Pages: 1018
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Ebook - 9781319350413

25 March 2020

NZ$215.95

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STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course.  Building on basic concepts and methods learned in that course, STAT2 empowers...

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STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course.  Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model. Using STAT2, students will: Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors.   Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest.   Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios.   Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.

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STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course.  Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model. Using STAT2, students will: Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors.   Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest.   Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios.   Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.

  • About this Book
  • Cover Page
  • Title Page
  • Copyright Page
  • Brief Contents
  • Contents
  • To the Teacher
  • Media and Supplements
  • Acknowledgments
  • To the Student
  • Dedication
  • Chapter 0: What Is a Statistical Model?
  • 0.1 Model Basics
  • 0.2 A Four-Step Process
  • Chapter Summary
  • Exercises
  • Unit A: Linear Regression
  • Chapter 1: Simple Linear Regression
  • 1.1 The Simple Linear Regression Model
  • 1.2 Conditions for a Simple Linear Model
  • 1.3 Assessing Conditions
  • 1.4 Transformations/Reexpressions
  • 1.5 Outliers and Influential Points
  • Chapter Summary
  • Exercises
  • Chapter 2: Inference for Simple Linear Regression
  • 2.1 Inference for Regression Slope
  • 2.2 Partitioning Variability—ANOVA
  • 2.3 Regression and Correlation
  • 2.4 Intervals for Predictions
  • 2.5 Case Study: Butterfly Wings
  • Chapter Summary
  • Exercises
  • Chapter 3: Multiple Regression
  • 3.1 Multiple Linear Regression Model
  • 3.2 Assessing a Multiple Regression Model
  • 3.3 Comparing Two Regression Lines
  • 3.4 New Predictors from Old
  • 3.5 Correlated Predictors
  • 3.6 Testing Subsets of Predictors
  • 3.7 Case Study: Predicting in Retail Clothing
  • Chapter Summary
  • Exercises
  • Chapter 4: Additional Topics in Regression
  • Topic 4.1 Added Variable Plots
  • Topic 4.2 Techniques for Choosing Predictors
  • Topic 4.3 Cross-validation
  • Topic 4.4 Identifying Unusual Points in Regression
  • Topic 4.5 Coding Categorical Predictors
  • Topic 4.6 Randomization Test for a Relationship
  • Topic 4.7 Bootstrap for Regression
  • Exercises
  • Unit B: Analysis of Variance
  • Chapter 5: One-way ANOVA and Randomized Experiments
  • 5.1 Overview of ANOVA
  • 5.2 The One-way Randomized Experiment and Its Observational Sibling
  • 5.3 Fitting the Model
  • 5.4 Formal Inference: Assessing and Using the Model
  • 5.5 How Big Is the Effect?: Confidence Intervals and Effect Sizes
  • 5.6 Using Plots to Help Choose a Scale for the Response
  • 5.7 Multiple Comparisons and Fisher’s Least Significant Difference
  • 5.8 Case Study: Words with Friends
  • Chapter Summary
  • Exercises
  • Chapter 6: Blocking and Two-way ANOVA
  • 6.1 Choose: RCB Design and Its Observational Relatives
  • 6.2 Exploring Data from Block Designs
  • 6.3 Fitting the Model for a Block Design
  • 6.4 Assessing the Model for a Block Design
  • 6.5 Using the Model for a Block Design
  • Chapter Summary
  • Exercises
  • Chapter 7: ANOVA with Interaction and Factorial Designs
  • 7.1 Interaction
  • 7.2 Design: The Two-way Factorial Experiment
  • 7.3 Exploring Two-way Data
  • 7.4 Fitting a Two-way Balanced ANOVA Model
  • 7.5 Assessing Fit: Do We Need a Transformation?
  • 7.6 Using a Two-way ANOVA Model
  • Chapter Summary
  • Exercises
  • Chapter 8: Additional Topics in Analysis of Variance
  • Topic 8.1 Levene’s Test for Homogeneity of Variances
  • Topic 8.2 Multiple Tests
  • Topic 8.3 Comparisons and Contrasts
  • Topic 8.4 Nonparametric Statistics
  • Topic 8.5 Randomization F-Test
  • Topic 8.6 Repeated Measures Designs and Datasets
  • Topic 8.7 ANOVA and Regression with Indicators
  • Topic 8.8 Analysis of Covariance
  • Exercises
  • Chapter 8: Online Sections: More on Repeated Measures
  • Topic 8.9 Repeated Measures: Mixed Designs
  • Topic 8.10 Repeated Measures: Advanced Material
  • Topic 8.11 Randomization Testing for Repeated Measures
  • Exercises
  • Unit C: Logistic Regression
  • Chapter 9: Logistic Regression
  • 9.1 Choosing a Logistic Regression Model
  • 9.2 Logistic Regression and Odds Ratios
  • 9.3 Assessing the Logistic Regression Model
  • 9.4 Formal Inference: Tests and Intervals
  • Chapter Summary
  • Exercises
  • Chapter 10: Multiple Logistic Regression
  • 10.1 Overview
  • 10.2 Choosing, Fitting, and Interpreting Models
  • 10.3 Checking Conditions
  • 10.4 Formal Inference: Tests and Intervals
  • 10.5 Case Study: Attractiveness and Fidelity
  • Chapter Summary
  • Exercises
  • Chapter 11: Additional Topics in Logistic Regression
  • Topic 11.1 Fitting the Logistic Regression Model
  • Topic 11.2 Assessing Logistic Regression Models
  • Topic 11.3 Randomization Tests for Logistic Regression
  • Topic 11.4 Analyzing Two-way Tables with Logistic Regression
  • Topic 11.5 Simpson’s Paradox
  • Exercises
  • Unit D: Time Series Analysis
  • Chapter 12: Time Series Analysis
  • 12.1 Functions of Time
  • 12.2 Measuring Dependence on Past Values: Autocorrelation
  • 12.3 ARIMA Models
  • 12.4 Case Study: Residual Oil
  • Chapter Summary
  • Exercises
  • Answers to Selected Exercises
  • Notes and Data Sources
  • General Index
  • Dataset Index

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    Ann R. Cannon has been a faculty member at Cornell College since 1993. She is currently Watson M. Davis Professor of Mathematics and Statistics in the Department of Mathematics and Statistics. She is the 2017 recipient of the Mu Sigma Rho William D. Warde Statistics Education Award. She has served terms as secretary/treasurer and at-large member of the executive committee for the Stat-Ed section as well as Council of Sections rep for Stat-Ed and as Treasurer (8 years) and President (1 year) for the Iowa Chapter of the ASA. She was Associate editor for JSE from 2000 to 2009 and was moderator for Isostat from 2003 to 2007. She has been reader, table leader, question leader, and assistant chief reader for the AP...

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    Ann R. Cannon has been a faculty member at Cornell College since 1993. She is currently Watson M. Davis Professor of Mathematics and Statistics in the Department of Mathematics and Statistics. She is the 2017 recipient of the Mu Sigma Rho William D. Warde Statistics Education Award. She has served terms as secretary/treasurer and at-large member of the executive committee for the Stat-Ed section as well as Council of Sections rep for Stat-Ed and as Treasurer (8 years) and President (1 year) for the Iowa Chapter of the ASA. She was Associate editor for JSE from 2000 to 2009 and was moderator for Isostat from 2003 to 2007. She has been reader, table leader, question leader, and assistant chief reader for the AP Statistics exam. She is also currently serving on the School Board for the Lisbon Community School District.

    George Cobb is Robert l. Rooke Professor emeritus at Mount Holyoke College, where he taught from 1974 to 2009 after earning his PhD in statistics from Harvard University.  He is a Fellow of the American Statistical Association, served a term as ASA vice-president, and received the ASA Founder’s award.  He is also recipient of the of the Lifetime Achievement award of the US Conference on Teaching Statistics.  He is author or co-author of several books, including Introduction to Design and Analysis of Experiments and Statistics in Action.  His interests include Markov chain Monte Carlo, applications of statistics to the law, and bluegrass banjo.

    Brad Hartlaub is a Professor in the Department of Mathematics and Statistics at Kenyon College. He is a nonparametric statistician who has served as the Chief Reader of the AP Statistics Program and is an active member of the American Statistical Association's Section on Statistical Education. Brad was selected as a Fellow of the American Statistical Association in 2006. He has served the College as a department chair, a division chair, a supervisor of undergraduate research, and an associate provost. He has received research grants to support his work with undergraduate students from the Andrew W. Mellon Foundation, the Council on Undergraduate Research, and the National Science Foundation. Brad received the Trustee Award for Distinguished Teaching in 1996, and the Distinction in Mentoring Award in 2014.

    Julie Legler earned a BA and MS in Statistics from the University of Minnesota and later a doctorate in biostatistics from Harvard.  She has taught statistics at the undergraduate level for nearly 20 years. In addition, she spent 7 years at the National Institutes of Health,  first as a postdoc and then as a mathematical statistician at the National Cancer Institute.  She has published in the areas of latent variable modeling, surveillance modeling, and undergraduate research.  Currently she is professor of statistics and director of the Statistics Program at St. Olaf College.  Recently she was named the Director of Collaborative Undergraduate Research and Inquiry  at St. Olaf.

    Robin H. Lock is the Jack and Sylvia Burry Professor of Statistics at St. Lawrence University where he has taught since 1983 after receiving his PhD from the University of Massachusetts- Amherst. He is a Fellow of the American Statistical Association, past Chair of the Joint MAA-ASA Committee on Teaching Statistics, a member of the committee that developed GAISE (Guidelines for Assessment and Instruction in Statistics Education), and on the editorial board of CAUSE (the Consortium for the Advancement of Undergraduate Statistics Education). He has won the national Mu Sigma Rho Statistics Education award and numerous awards for presentations on statistics education at national conferences.

    Thomas Moore earned a B.A. from Carleton College, an M.S. from the University of Iowa, and a Ph.D. from Dartmouth.  He has been on the faculty at Grinnell College since 1980 and has concentrated his scholarship on statistics education.  He chaired the Statistics Education Section of ASA in 1995 and the MAA's SIGMAA for Statistics Education in 2004.  He is a Fellow of American Statistical Association and was the2008 Mu Sigma Rho Statistical Education Award winner.

    Allan J. Rossman is Professor and Chair of the Statistics Department at Cal Poly – San Luis Obispo. He served as Chief Reader of the Advanced Placement program in Statistics from 2009-2014. He was Program Chair for the 2007 Joint Statistical Meetings and for the U.S. Conference in Teaching Statistics since 2013. He is a Fellow of the American Statistical Association and has received the Mathematical Association of America’s Haimo Award for Distinguished College or University Teaching of Mathematics and the ASA’s Waller Distinguished Teaching Career Award.

    Jeff Witmer is Professor of Mathematics at Oberlin College.  He earned a doctorate in statistics from the University of Minnesota in 1983. His scholarly work has been primarily in the areas of Bayesian decision theory and statistics education.  He is a Fellow of the American Statistical Association and served as editor of STATS magazine.  Among the books he has written or co-authored are Activity Based Statistics and Statistics for the Life Sciences.

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