Unlike most probability textbooks, which are often written only for the mathematically-oriented students, Mark Ward and Ellen Gundlach's Introduction to Probability makes the subject much more accessible, reaching out to a much wider introductory-level audience. Its approachable and conversational style, highly visual approach, practical examples, and step-by-step problem solving procedures help all kinds of students understand the basics of probability theory and its broad applications in the outside world.
This textbook has been extensively class-tested throughout its preliminary edition in order to make it even more effective at building confidence in students who have viable problem-solving potential but are not fully comfortable in the realm of mathematics. Its rich pedagogy, combined with a thoughtful structure, provides an accessible introduction to this complex subject.
Mark Daniel Ward is an Associate Professor of Statistics at Purdue University. Dr. Ward is currently the Principal Investigator for the NSF grant "MCTP: Sophomore Transitions: Bridges into a Statistics Major and Big Data Research Experiences via Learning Communities". He is also an Associate Director of the Center for Science of Information. Ellen Gundlach has been teaching introductory statistics and probability classes at Purdue University as a continuing lecturer since 2002. She is an associate editor of CAUSEweb and editor of the MERLOT Statistics Board. Her research interests include K12 outreach activities and online and hybrid teaching.
I. Randomness.- 2. Probability.- 3. Independent Events.- 4. Conditional Probability.- 5. Bayes' Theorem.- 6. Review of Randomness. Part II Discrete Random Variables7. Discrete Versus Continuous Random Variables.- 8.- Probability Mass Functions and CDFs.- 9. Independence and Conditioning.- 10. Expected Values of Discrete Random Variables.- 11. Expected Values of Sums of Random Variables.- 12. Variance of Discrete Random Variables.- 13. Review of Discrete Random Variables. Part III Named Discrete Random Variables 14.- Bernoulli Random Variables.- 15. Binomial Random Variables.- 16. Geometric Random Variables.- 17.Negative Binomial Random Variables.- 18. Poisson Random Variables.- 19. Hypergeometric Random Variables.- 20. Discrete Uniform Random Variables.- 21. Review of Named Discrete Random Variables. Part IV Counting 22. Introduction to Counting.- 23. Two Case Studies in Counting. Part V Continuous Random Variables24. Continuous Random Variables and PDFs.- 25. Joint Densities.- 26. Independent Continuous Random Variables.- 27. Conditional Distributions.- 28. Expected Values of Continuous Random Variables.- 29. Variance of Continuous Random Variables.- 30. Review of Continuous Random Variables Part VI Named Continuous Random Variables 31. Continuous Uniform Random Variables.- 32. Exponential Random Variables.- 33. Gamma Random Variables.- 34. Beta Random Variables.- 35. Normal Random Variables.- 36. Sums of Independent Normal Random Variables.- 37. Central Limit Theorem Part VII Additional Topics39. Variance of Sums; Covariance; Correlation.- 40. Conditional Expectation.- 41. Markov and Chebyshev Inequalities.- 42. Order Statistics.- 43. Moment Generating Functions.- 44.