Chapter 1
Introductory Lab:  An Introduction to Randomization Tests: Schistosomiasis
Advanced Lab:      A Closer Look at Randomization and Permutation Tests
Project:                  Infant Handling in Yellow Baboons (Anthropology)

Chapter 2
Introductory Lab:   The Two-Sample t-test, Regression, and ANOVA: Making Connections
Advanced Lab:      Extending the Linear Model to More Than One Explanatory Variable
Project:                  The Simon Memory Game (Psychology)

Chapter 3
Introductory Lab:   An Introduction to Multiple Regression: How Much is Your Car Worth?
                              Prerequisite: Chapter 2 or Regression
Advanced Lab:      A Closer Look at Multiple Regression
Project:                  Cash for Condoms (Economics)

Chapter 4
Introductory Lab:   Designing Factorial Experiments: Popcorn
                              Prerequisite: Chapter 2
Advanced Lab:      A Closer Look at Design of Experiments
Project:                  Seed Germination (Ecology)

Chapter 5
Introductory Lab:   Block, Split-Plot and Repeated Measure Designs: Memory
                              Prerequisite: Chapter 4
Advanced Lab:      A Closer Look at Advanced Designs
Project:                  The Perfection Game (Psychology)

Chapter 6
Introductory Lab:   An Introduction to Categorical Data Analysis: The Challenger Space Shuttle Disaster
Advanced Lab:      A Closer Look at Categorical Data Analysis
Project:                  Electric Fish (Neurobiology)

Chapter 7
Introductory Lab:   An Introduction to Logistic Regression: Malignant or Benign?  
                              Prerequisite: Chapter 6 or the chi-square test
Advanced Lab:      A Closer Look at Logistic Regression
Project:                  Substance Abuse among Youth (Sociology)

Chapter 8
Introductory Lab:   An Introduction to Poisson Regression: Detecting Cancer Clusters
                              Prerequisite: Chapter 7
Advanced Lab:      A Closer Look at Poisson Regression (Ch. 7)
Project:                  Superfund Sites and Leukemia (Biostatistics)

Chapter 9
Introductory Lab:   An Introduction to Survival Analysis: Chocolate Chips
Advanced Lab:      A Closer Look at Survival Analysis
                              Prerequisite: Calculus I and Chapter 6
Project 1:               Lung Cancer (Biology)
Project 2:               Perfection (Psychology)

Chapter 10
Introductory Lab:   An Introduction to Principal Component Analysis: Stock Market Values
Advanced Lab:      A Closer Look at Principal Component Analysis
Project:                  Global Warming and the Hockey Stick Graph (Environmental Science)

Chapter 11
Introductory Lab:   An Introduction to Bayesian Data Analysis: What Colors Come in Your M&M's Candy Bag?
Advanced Lab:      A Closer Look at Bayesian Data Analysis
                              Prerequisite: Calculus II
Project:                  Adaptive Stopping Rules for Clinical Trials (Medical Ethics)

 

Appendices
Appendix A: Minitab Instructions and Selected Answers
Appendix B: R Instructions and Selected Answers
Appendix C: Review of Introductory Statistics
Appendix D: Just Enough Matrix Algebra for Statistics

Each chapter is written in a workbook style with the intention that students will progress through much of the material on their own. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class. Although not necessary, you may choose to expand on a particular topic by supplementing with your own lecture notes or working through one of the exercises as an example before students attempt the material on their own. Note that at least some class time will certainly be needed to discuss the chapters as students progress.

The Advanced Lab is optional and provides several additional exercises and more in-depth coverage than the Introductory Lab.  Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.

Clearly one chapter on a statistical topic covers the material in less depth than a dedicated course on that topic, but that is not our intent. Exposure to a broad set of topics and methods will give students a solid foundation when they are expected to analyze data in their future careers. We believe that this text will have broad appeal and application, allowing students to see the many exciting possibilities within the discipline of statistics.  

PREREQUISITS: These materials are accessible to students who have taken a one-term, algebra-based introductory statistics course, such as a high school advanced placement statistics course. The text assumes a rudimentary knowledge of the concepts of hypothesis testing and confidence intervals and familiarity with statistical inference by t- and z-procedures; however, these topics are often briefly reviewed when referenced in the text.