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STAT2
Second Edition ©2019 Ann Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer Formats: Digital & Print
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Authors
-
Ann Cannon
Ann Cannon is the Watson M. Davis Professor of Mathematics and Statistics at Cornell College in Mount Vernon, Iowa, where she has taught statistics for 30 years. She earned her MA and PhD in statistics from Iowa State University, and her BA in mathematics from Grinnell College. Ann is a Fellow of the American Statistical Association (ASA) and won the Mu Sigma Rho (national statistics honor society) William D. Warde Statistics Education Award. Ann has been very involved with the Statistics and Data Science Education Section of the ASA, serving on the executive committee as member-at-large, secretary/treasurer, and chair. She has also served on the ASA/MAA Joint Committee on Undergraduate Statistics, and as the Secretary/Treasurer and Chair of the Iowa Chapter of the ASA. Ann is currently associate editor for the Journal of Statistics and Data Science Education. Ann has been involved with the AP® Statistics Reading for 20 years, serving as Reader, Table Leader, Question Leader, and Assistant Chief Reader. Ann is coauthor of STAT2: Modeling with Regression and ANOVA (now in its second edition), a textbook designed for the college statistics course following the introductory statistics course. In her spare time, Ann enjoys playing the French horn (particularly in pit orchestras for musical theater), reading, and traveling.
-
George W. Cobb
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.
-
Bradley A. Hartlaub
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 Associations 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 M. Legler
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
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 L. Moore
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 MAAs 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
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.
-
Jeffrey A. Witmer
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.
Table of Contents
0.1 Model Basics
0.2 A Four-Step Process
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 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 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 4 Additional Topics in Regression
4.1 Topic: Added Variable Plots
4.2 Topic: Techniques for Choosing Predictors
4.3 Cross-validation
4.4 Topic: Identifying Unusual Points in Regression
4.5 Topic: Coding Categorical Predictors
4.6 Topic: Randomization Test for a Relationship
4.7 Topic: Bootstrap for Regression
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 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 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 8 Additional Topics in Analysis of Variance
8.1 Topic: Levene’s Test for Homogeneity of Variances
8.2 Topic: Multiple Tests
8.3 Topic: Comparisons and Contrasts
8.4 Topic: Nonparametric Statistics
8.5 Topic: Randomization F-Test
8.6 Topic: Repeated Measures Designs and Data Sets
8.7 Topic: ANOVA and Regression with Indicators
8.8 Topic: Analysis of Covariance
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 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 11 Additional Topics in Logistic Regression
11.1 Topic: Fitting the Logistic Regression Model
11.2 Topic: Assessing Logistic Regression Models
11.3 Randomization Tests for Logistic Regression
11.4 Analyzing Two-Way Tables with Logistic Regression
11.5 Simpson’s Paradox
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
Answers to Selected Exercises
General Index
Dataset Index
Product Updates
- New statistical topics. Two topics that were requested most consistently from first edition users were repeated measures designs and time series. We have added new material (Topic 8.6) to give a brief introduction to repeated measures designs and for instructors who want more depth in this topic, we have included three more sections in the online material. We have also added Chapter 12, giving a brief introduction to working with time series data. In addition to these new sections and chapters, we have made numerous changes to include new ideas (e.g., effect sizes) and give more guidance to students (e.g., choosing a transformation).
- New organization. We reorganized the material in Unit B to better integrate ideas of experimental design with the topics of ANOVA. Chapter 6 now focuses on block designs and the additive ANOVA model, with interaction coming in Chapter 7, and additional ANOVA topics in Chapter 8.
- New exercises and examples. The second edition has 243 worked examples and 646 exercises for students, increases of 76% and 63% over the first edition. We have also updated and revised almost 100 examples and exercises that are carried over from the first edition.
- New datasets. We have 64 new datasets dealing with real data, many from research studies. We have also updated datasets from the first edition to bring the total dataset count to 172. Datasets are available in various formats for different software packages and a data index after the general index lists all datasets and where they are used in the text.
- New design. A new, full-color design incorporates all-new figures, charts, and graphs. In addition, important definitions and explanations are highlighted for emphasis. Our goal in creating this design was to make the reading and learning experience more approachable by instructors and students alike.
NEW PEDAGOGICAL FEATURES
- Chapter opening section lists give an at-a-glance look at the content therein.
- Learning objectives outline goals and expectations that help instructors create their syllabi and students understand where they’re headed.
- Caution icons and text signal common misconceptions and important ideas to help the student avoid pitfalls and grasp key concepts.
- Data icons highlight the data set in use for each example and exercise.
- Key terms are highlighted in the margins to help students build a solid statistics vocabulary.
Authors
-
Ann Cannon
Ann Cannon is the Watson M. Davis Professor of Mathematics and Statistics at Cornell College in Mount Vernon, Iowa, where she has taught statistics for 30 years. She earned her MA and PhD in statistics from Iowa State University, and her BA in mathematics from Grinnell College. Ann is a Fellow of the American Statistical Association (ASA) and won the Mu Sigma Rho (national statistics honor society) William D. Warde Statistics Education Award. Ann has been very involved with the Statistics and Data Science Education Section of the ASA, serving on the executive committee as member-at-large, secretary/treasurer, and chair. She has also served on the ASA/MAA Joint Committee on Undergraduate Statistics, and as the Secretary/Treasurer and Chair of the Iowa Chapter of the ASA. Ann is currently associate editor for the Journal of Statistics and Data Science Education. Ann has been involved with the AP® Statistics Reading for 20 years, serving as Reader, Table Leader, Question Leader, and Assistant Chief Reader. Ann is coauthor of STAT2: Modeling with Regression and ANOVA (now in its second edition), a textbook designed for the college statistics course following the introductory statistics course. In her spare time, Ann enjoys playing the French horn (particularly in pit orchestras for musical theater), reading, and traveling.
-
George W. Cobb
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.
-
Bradley A. Hartlaub
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 Associations 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 M. Legler
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
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 L. Moore
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 MAAs 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
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.
-
Jeffrey A. Witmer
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.
Table of Contents
0.1 Model Basics
0.2 A Four-Step Process
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 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 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 4 Additional Topics in Regression
4.1 Topic: Added Variable Plots
4.2 Topic: Techniques for Choosing Predictors
4.3 Cross-validation
4.4 Topic: Identifying Unusual Points in Regression
4.5 Topic: Coding Categorical Predictors
4.6 Topic: Randomization Test for a Relationship
4.7 Topic: Bootstrap for Regression
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 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 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 8 Additional Topics in Analysis of Variance
8.1 Topic: Levene’s Test for Homogeneity of Variances
8.2 Topic: Multiple Tests
8.3 Topic: Comparisons and Contrasts
8.4 Topic: Nonparametric Statistics
8.5 Topic: Randomization F-Test
8.6 Topic: Repeated Measures Designs and Data Sets
8.7 Topic: ANOVA and Regression with Indicators
8.8 Topic: Analysis of Covariance
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 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 11 Additional Topics in Logistic Regression
11.1 Topic: Fitting the Logistic Regression Model
11.2 Topic: Assessing Logistic Regression Models
11.3 Randomization Tests for Logistic Regression
11.4 Analyzing Two-Way Tables with Logistic Regression
11.5 Simpson’s Paradox
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
Answers to Selected Exercises
General Index
Dataset Index
Product Updates
- New statistical topics. Two topics that were requested most consistently from first edition users were repeated measures designs and time series. We have added new material (Topic 8.6) to give a brief introduction to repeated measures designs and for instructors who want more depth in this topic, we have included three more sections in the online material. We have also added Chapter 12, giving a brief introduction to working with time series data. In addition to these new sections and chapters, we have made numerous changes to include new ideas (e.g., effect sizes) and give more guidance to students (e.g., choosing a transformation).
- New organization. We reorganized the material in Unit B to better integrate ideas of experimental design with the topics of ANOVA. Chapter 6 now focuses on block designs and the additive ANOVA model, with interaction coming in Chapter 7, and additional ANOVA topics in Chapter 8.
- New exercises and examples. The second edition has 243 worked examples and 646 exercises for students, increases of 76% and 63% over the first edition. We have also updated and revised almost 100 examples and exercises that are carried over from the first edition.
- New datasets. We have 64 new datasets dealing with real data, many from research studies. We have also updated datasets from the first edition to bring the total dataset count to 172. Datasets are available in various formats for different software packages and a data index after the general index lists all datasets and where they are used in the text.
- New design. A new, full-color design incorporates all-new figures, charts, and graphs. In addition, important definitions and explanations are highlighted for emphasis. Our goal in creating this design was to make the reading and learning experience more approachable by instructors and students alike.
NEW PEDAGOGICAL FEATURES
- Chapter opening section lists give an at-a-glance look at the content therein.
- Learning objectives outline goals and expectations that help instructors create their syllabi and students understand where they’re headed.
- Caution icons and text signal common misconceptions and important ideas to help the student avoid pitfalls and grasp key concepts.
- Data icons highlight the data set in use for each example and exercise.
- Key terms are highlighted in the margins to help students build a solid statistics vocabulary.
The unifying theme of this text is the use of models in statistical data analysis.
Now available with Macmillan’s online learning platform Achieve Essentials, 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.
Achieve Essentials for Stat2 connects the problem-solving techniques and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve Essentials support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.
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Ann Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer | Second Edition | ©2019 | ISBN:9781319209513
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STAT2
Now available with Macmillan’s online learning platform Achieve Essentials, 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.
Achieve Essentials for Stat2 connects the problem-solving techniques and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve Essentials support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.
Select a demo to view:
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