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Practicing Statistics: Guided Investigations for the Second Course
- ©2013
- | Pearson
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Building on the introductory course, Practicing Statistics: Guided Investigations for the Second Course presents a variety of compelling topics for a second course in statistics, such as multiple regression, nonparametric methods, and survival analysis. Every topic is introduced in the context of a real-world research question, asking students to explore the concepts firsthand with guided activities and research projects.
The number of students taking AP Statistics continues to rise, and the number of students taking an introductory statistics course has more than doubled since 1990. As a result, the goals of the second course have changed. This course must engage students from multiple disciplines and demonstrate the broad applicability of statistics to their lives. Practicing Statistics takes an inquiry-based approach that teaches advanced statistical techniques through group work and hands-on exploration using real research questions.
The chapters are modular, so that instructors can select only the topics relevant to their course, and teach them in any order. The only prerequisite is an algebra-based introductory statistics or AP statistics course.
This title is appropriate for the following courses. Select a course to see additional titles
· Data sets formatted as .csv and .txt files.
· Student Answer Bank includes answers to selected activities from the text.
· R Manual contains detailed instructions for performing the text's activities using R.
· Minitab Manual contains detailed instructions for performing the text's activities using Minitab.
· A Review of Introductory Statistics, a brief review of the topics course in the intro stats course for student reference
· Glossary.
· Instructions on how to write a research paper and poster.
· Instructions on how to access applets referenced in the text.
1. Nonparametric Methods: Schistosomiasis
1.1 Investigation: Can a New Drug Reduce the Spread of Schistosomiasis?
1.2 Statistical Inference Through a Randomization Test
1.3 Performing a Randomization Test Using a Computer Simulation
1.4 Two-Sided Tests
1.5 What Can We Conclude from the Schistosomiasis Study?
1.6 Permutation Tests versus Randomization Tests
1.7 Permutation and Randomization Tests for Matched Pairs Designs
1.8 The Bootstrap Distribution
1.9 Using Bootstrap Methods to Create Confidence Intervals
1.10 Relationship Between the Randomization Test and the Two-Sample t-Test
1.11 Wilcoxon Rank Sum Tests for Two Independent Samples
1.12 Kruskal-Wallis Test for Two or More Independent Samples
1.13 Multiple Comparisons
Research Project: Gender Discrimination Among University Faculty
2. Making Connections: The Two-Sample t-Test, Regression, and ANOVA
2.1 Investigation: Do Distracting Colors Influence the Time to Complete a Game?
2.2 The Two-Sample t-Test to Compare Population Means
2.3 The Regression Model to Compare Population Means
2.4 ANOVA to Compare Population Means
2.5 Comparing Planned Variability to Random Variability
2.6 Random Sampling and Random Allocation
2.7 What Can We Conclude from the Game Study?
2.8 Normal Probability Plots to Assess Normality
2.9 Transformations
2.10 Calculating Test Statistics
2.11 Confidence Intervals
Research Project: Building a Better Paper Helicopter
3. Multiple Regression: How Much Is Your Car Worth?
3.1 Investigation: How Can We Build a Model to Estimate Used Car Prices?
3.2 Goals of Multiple Regression
3.3 Variable Selection Techniques to Describe or Predict a Response
3.4 Checking Model Assumptions
3.5 Interpreting Model Coefficients
3.6 Categorical Explanatory Variables
3.7 What Can We Conclude from the 2005 GM Car Study?
3.8 F-Tests for Multiple Regression
3.9 Developing a Model to Confirm a Theory
3.10 Interaction and Terms for Curvature
3.11 A Closer Look at Variable Selection Criteria
Research Project: Economic Growth in Third World Countries
4. The Design and Analysis of Factorial Experiments: Microwave Popcorn
4.1 Investigation: Which Microwave Popcorn Is the Best?
4.2 Elements of a Well-Designed Experiment
4.3 Analyzing a Two-Way Factorial Design
4.4 Analyzing a Three-Way Factorial Design
4.5 What Can We Conclude from the Popcorn Study?
4.6 Paper Towels: Developing a Statistical Model for a Two-Way Factorial Design
4.7 Paper Towels: The Relationship Between Effects and ANOVA
4.8 Contrasts and Multiple Comparisons
Research Project: Testing for the Effect of Distracters
5. Block, Split-Plot, and Repeated Measures Designs: What Influences Memory?
5.1 Investigation: What Influences Memory?
5.2 Elements of a Well-Designed Experiment
5.3 Statistical Analysis Based on the Experimental Design
5.4 Three Commonly Used Design Structures
5.5 Crossed and Nested Factors
5.6 Fixed and Random Factors
5.7 Model Assumptions
5.8 What Can We Conclude from the Memory Study?
5.9 Calculating Crossed and Nested Effects
5.10 Mathematical Calculations for ANOVA
5.11 Hasse Diagrams
5.12 Wash Your Hands: Analysis of Covariance (ANCOVA)
Research Project: What Impacts Memory?
6. Categorical Data Analysis: Is a Tumor Malignant or Benign?
6.1 Investigation: Is Cell Shape Associated with Malignancy?
6.2 Summarizing Categorical Data
6.3 A Simulation Study: How Likely Is It That the Observed Sample Would Occur by Chance?
6.4 Fisher’s Exact Test
6.5 Two-Sided Hypothesis Tests
6.6 The Chi-Square Test
6.7 What Can We Conclude from the Cancer Study?
6.8 Relative Risk and the Odds Ratio
6.9 Sampling Designs
6.10 Comparing Tests of Homogeneity and Independence
6.11 Chi-Square Goodness-of-Fit Tests
Research Project: Infant Handling in Female Baboons
7. Logistic Regression: The Space Shuttle Challenger
7.1 Investigation: Did Temperature Influence the Likelihood of an O-Ring Failure?
7.2 Review of the Least Squares Regression Model
7.3 The Logistic Regression Model
7.4 The Logistic Regression Model Using Maximum Likelihood Estimates
7.5 Interpreting the Logistic Regression Model
7.6 Inference for the Logistic Regression Model
7.7 What Can We Conclude from the Space Shuttle Study?
7.8 Logistic Regression with Multiple Explanatory Variables
7.9 The Drop-in-Deviance Test
7.10 Measures of Association
7.11 Review of Means and Variances of Binary and Binomial Data
7.12 Calculating Logistic Regression Models for Binomial Counts
7.13 Calculating Residuals for Logistic Models with Binomial Counts
7.14 Assessing the Fit of a Logistic Regression Model with Binomial Counts
7.15 Diagnostic Plots
7.16 Maximum Likelihood Estimation in Logistic Regression
Research Project: Substance Abuse Among Youth
8. Poisson Log-Linear Regression: Detecting Cancer Clusters
8.1 Investigation: Are Cancer Rates Higher for People Living near a Toxic Waste Area?
8.2 Comparing Count Data for Groups
8.3 Building Models for Count Data
8.4 The Binomial Model for Count Data
8.5 The Poisson Model for Count Data
8.6 Adding a Covariate to the Poisson Count Model
8.7 Interpreting Poisson Regression Model Parameters
8.8 Poisson Regression Models with More Than One Covariate
8.9 Inference for Poisson Regression Models
8.10 Assessing the Fit of the Poisson Regression Model
8.11 What Can We Conclude from the Cancer Rate Study?
8.12 Estimation Methods for Generalized Linear Models
8.13 Do No-Smoking-at-Work Policies Keep Smoking at Home?
8.14 Is the Number of Species on Archipelago Islands Related to Island Area, Elevation, and Neighboring Islands?
Research Project: Hitting a Grand Slam in Baseball
9. Survival Analysis: Melting Chocolate Chips
9.1 Investigation: How Long Does It Take for Chocolate Chips to Melt?
9.2 Overview of Survival Analysis Studies and Data
9.3 The Survival Function
9.4 Descriptive Statistics for Survival Data
9.5 Confidence Intervals for Survival Probabilities
9.6 Comparing Survival Functions
9.7 What Can We Conclude About Melting Chocolate Chips?
9.8 The Hazard Function
9.9 The Cumulative Hazard Function
9.10 Additional Types of Incomplete Data
Research Project: Shapesplosion: A Study of Reaction Time
10. Principal Component Analysis: Stock Market Values
10.1 Investigation: Can a Single Variable Explain Patterns in the Stock Market?
10.2 A Visual Interpretation of PCA
10.3 Calculating Principal Components for Two Variables
10.4 Understanding Eigenvalues
10.5 A Three-Dimensional Example
10.6 What Can We Conclude from the Stock Market Investigation?
10.7 The Impact of Standardizing Each Variable
10.8 Determining the Number of Components to Retain
10.9 Interpreting Principal Components
10.10 Comparing Regression and Principal Components
10.11 Incorporating Principal Components into Other Statistical Methods
10.12 Calculating Eigenvectors and Eigenvalues Using Matrix Algebra
Research Project: The Global Warming Hockey Stick Controversy
11. Bayesian Data Analysis: What Colors Come in Your M&M’s Candy Bag?
11.1 Investigation: Do Prior Beliefs Improve Your Estimate of the Proportion of Brown or Orange M&M’s?
11.2 Combining Prior Information About π with Data
11.3 Prior Distributions for π
11.4 Calculating the Posterior Distribution for π
11.5 The Posterior Mean
11.6 What Can We Conclude About Colors of M&M’s?
11.7 Screening for the HIV Virus in the U.S. Blood Bank Supply: Applications of Bayes’ Rule
11.8 Ganzfeld Experiments: Continuous Prior Distributions for π
11.9 Return to M&M’s: Bayesian Credible Intervals
Research Project: Do You Believe in ESP?
Appendix of Tables
Index
Shonda Kuiper was the recipient of a three-year grant from the National Science Foundation to develop materials for a second course in statistics. She has held the position of Associate Professor at Grinnell College since September 2003. The courses she teaches include Introductory Statistics, Statistical Methods, Design of Experiments, and calculus-based Probability and Statistics. Shonda received her BA in mathematics from Wartburg College, and has an MS and a PhD in statistics from Iowa State University. In addition to previously holding teaching positions at Iowa State University and Wartburg College, Shonda served four years as Consulting Statistician, and later Senior Engineer, for Hallmark Greeting Cards.
Jeffrey Sklar is an Associate Professor in the Statistics Department at California Polytechnic State University, San Luis Obispo, and teaches various introductory statistics courses, as well as linear regression, multivariate statistics, and survival analysis courses. Before joining the faculty at Cal Poly in September 2005, he taught classes in the Department of Statistics and Applied Probability and the Gevirtz Graduate School of Education at the University of California, Santa Barbara. Jeffrey received his Bachelor of Arts and Sciences degree in mathematics and philosophy from the University of California, Davis, and his MA and PhD in statistics from University of California, Santa Barbara.
Kuiper
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