Poisson Processes

The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

• An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
• A guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
• Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
• Data sets from multiple disciplines and software instructions for Minitab and R.

The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. 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. 

This introductory probability textbook, freely available here in pdf format, emphasizes the use of computing to simulate experiments and make computations. A set of programs that go with the book and the answers to the odd-numbered problems are also available from this site. Chapter headings include: Discrete Probability, Continuous Probability Densities, Combinatorics, Conditional Probability, Distributions and Densities, Expected Value and Variance, Sums of Random Variables, Law of Large Numbers, Central Limit Theorem, Generating Functions, Markov Chains, and Random Walks.