Statistics I

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2)

ST-Categorical Data Analysis

Distribution & inference for binomial and multinumial variables with contingency tables, generalized linear models, logistic regression for binary responses, logit models, loglinear models, inference for matched-pairs and correlated clustered data. PreReq: Previous course work in pfobablity & math stat inslcuding distribution theory, estimation confidence intervals, hyp testing and multiple regression (e.g. STAT 505 & 516 or equiv.)

Statistics II

Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.

[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]

Estmtn Th&Hypo Tst I

The advanced theory of statistics, including methods of estimation (unbiasedness, equivariance, maximum likelihood, Bayesian, minimax), optimality properties of estimators, hypothesis testing, uniformly most powerful tests, unbiased tests, invariant tests, relationship between confidence regions and tests, large sample properties of tests and estimators. Prerequisites: Statistc 605 and 608.

Statistics I

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2)
Subscribe to