Introduction to probability, independence, conditional independence, and Bayes' theorem. Discrete and continuous, univariate and multivariate distributions. Linear and nonlinear transformations of random variables. Classical and Bayesian inference, decision theory, and comparison of hypotheses. Experimental design, statistical quality control, and other applications in engineering. Not open to students who have taken Statistical Science 111, 250D, or 611. Recommended prerequisite: Mathematics 212 or equivalent.
Not open to students who have taken Statistical Science 111, 250D, or 611