My time at the University of Delaware is largely spent introducing students to statistics – the science of extracting information from data. I am blessed to be married to a wonderful girl and when I’m not at UD, I enjoy spending time with her, our three energetic children, and our extended family. My other interests include playing tennis and racquetball, hiking and camping, choral singing, and my 1989 Mustang GT.
Current Courses
- STAT 200: Basic Statistical Practice
- STAT 470/670: Probability Theory in Statistics
- STAT 471/671: Mathematical Statistics
Publications
- Bryan R. Crissinger (2015) The Effect of Distributed Practice in Undergraduate Statistics Homework Sets: A Randomized Trial, Journal of Statistics Education, 23:3, DOI:10.1080/10691898.2015.11889743
- My latest effort has been an introductory statistics textbook that I now use in STAT 200. One way in which this text differs from most is the emphasis on the difference between random sampling and random assignment and what those differences imply for interpreting results. A related difference is a distinction between types of inferences (generalization, causal, and predictive). This difference is emphasized throughout the text, not just in a single chapter. For example, chapter 8 on two-group inference is divided into sections by purpose (generalizing to populations vs. causal inference about treatments) instead of by test type (e.g. two-sample t-test, paired t-test, two-proportion z-test). Hypothesis testing based on p-values (instead of rejection regions) is introduced immediately after sampling distributions, preserving that very natural flow. Confidence intervals are presented next, as “inverted tests.” Because of that, the score interval for a proportion is used (which has been shown to perform better in some cases) instead of the much more common Wald interval. Heavily influenced by my experience as an AP Statistics Reader and Table Leader, the text also emphasizes statistical concepts (e.g. the mean as the “balance point”), interpreting results (e.g. p-values and confidence intervals), conditions for inference, and common misconceptions. Exercises that either use or are based on real data are common throughout. I’d like to add topics on inference for categorical data and regression in a second edition.
Chapter 1: An Introduction to Data
Chapter 2: Summarizing Data
Chapter 3: Producing Data
Chapter 4: Probability
Chapter 5: Probability Distributions for Random Sampling
Chapter 6: Hypothesis Testing
Chapter 7: Confidence Intervals
Chapter 8: Inference About Two Groups
If you’re an instructor and you’d like a complementary review copy, please let me know at crissing@udel.edu.
Education
- M.S. Statistics (2001), The Pennsylvania State University
- B.A. Mathematics (1997), Messiah College
Contact
Bryan Crissinger
Department of Applied Economics and Statistics
University of Delaware
Newark, DE 19716
302-831-8142
crissing at udel dot edu
