Gary Davis, PhD

An introduction to statistics and probability. Topics include: measures of central tendency and dispersion, elementary probability, binomial, normal, and t-distributions, hypothesis testing and confidence intervals, and a survey of correlation and regression analysis. It is recommended for Psychology, Sociology, Nursing, or other Liberal Arts and Fine Arts majors whose degree program does not require any other courses in mathematics. The course fulfills the general education requirements for sociology, psychology, nursing, education, and other related majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.

Introduction to exploratory data analysis using R, including graphical techniques, confirmatory statistics, interval estimates, hypothesis tests, bootstrap estimates.

Continuation of MTH 331. Classical estimation methods and hypothesis testing are studied. This course also covers Chi square tests for goodness-of-fit and independence, regression and correlation analysis, and one-way and two-way analysis of variance including factorial designs and tests for the separation of means.

Investigation in applied statistics. Topics for investigation chosen in consultation with course Instructor and other academic advisers. Individual projects will address: question or issue under investigation; significance; literature review; methods used including experimental design, data collection, forms of analysis; results; discussion; conclusions.

Communicating uncertainty, risk and statistical analyses. Topics chosen from: data quality and interpretability; presentation of complex data and findings; Lasswell-type models of communication; understanding audience, feedback; user-centric communication; audience segmentation; simplification and clarity; informed understanding; assessing reliability; avoiding manipulation, pedantry, jargon; use of AI; natural frequencies versus probabilities; explaining uncertainty and risk; aleatory and epistemic uncertainty; graphic, visual, verbal presentation of risk.

Continuation of MTH 332. Covering topics are advanced mathematical statistics topics, including detailed hypothesis testing, linear models, and regression analysis. This course also covers concepts and selected algorithms in machine learning.