Measurements, Statistics, and Probability

Catalog description

A combined lecture and laboratory course providing an introductory treatment of probability theory, including random variables/vectors, probability distributions, calculations of expectations and variances, limit theorems, hypothesis testing, model fitting (frequentist and Bayesian perspectives), assessing goodness of fit, and quantifying uncertainty. Practical applications include linear regression, logistical regression, and their applications to biomedical data.