300

Continuation of STAT 200. Review of the basics of estimation, confidence intervals and hypothesis testing. Simple and multiple regression, time series, analysis of variance and non-parametric methods. A statistical software package will be used extensively.

Basics of probability; descriptive statistics; discrete and continuous distributions; confidence intervals and tests of hypotheses concerning means and proportions; simple linear regression; statistical software. MATH 210 is recommended, in addition to the prerequisites listed.

Simple linear regression and multiple regression including inference, diagnostics and transformations. One-way and multi-way analysis of variance including inference, diagnostics and transformations. Use of professional statistical software.

Techniques for analyzing categorical response data – confidence intervals, tests of significance for a proportion, the difference of two proportions, contingency tables, regression, odds, odds ratios, logistic regression, logit models, loglinear models and diagnostics.

A first course in probability with selected applications. Definition of probability and basic axioms; calculation of probabilities; mutually exclusive and independent events; conditional probability and Bayes Theorem; discrete random variables and distributions; continuous random variables and distributions; calculation of expected value, mode, median, percentiles, variance, standard deviation, and coefficient of variation; functions of random variables and transformations. Applications selected from Markov chains, random walks, queueing theory, and inventory theory

Multivariate distributions, functions of random variables, sampling distributions and central limit theory, theory of estimation, the method of moment and maximum likelihood, and hypothesis testing.

A continuation of STAT 260: statistical foundations of data science; bootstrap methods; supervised learning; unsupervised learning; simulation; interactive data graphics; working with spatial data and text; working with large data sets.