Course Descriptions

STA 111 Quantitative Reasoning

4 Credits, Fall Semester

Designed especially for students in the humanities or the social sciences. Focuses primarily on the fundamental ideas of probability and introduces statistics.

STA 119 Statistical Methods

4 Credits, Fall and Spring Semesters

Covers topics in descriptive statistics, probability, inference, and experimental design, all of which are put together to draw conclusions from uncertainty through analysis of experimental data. Although a general statistical methods course, the material (through examples) is geared towards sciences majors, especially those in the health sciences. The underlying reasoning behind the techniques will be explored.

STA 301 Introduction to Probability

4 Credits, Fall Semester (LEC/REC)

Provides students with probability and distribution theory necessary for the study of statistics. Topics include axioms of probability theory, independence, conditional probability, random variables, discrete and continuous probability distributions, functions of random variables, moment generating functions, the Laws of Large Numbers, and the Central Limit Theorem.

STA 302 Introduction to Statistical Inference

4 Credits, Spring Semester (LEC/REC)

Introduces principles of statistical inference. Introduces and develops classical methods of estimation, tests of significance, the Neyman-Pearson Theory of testing hypotheses, maximum likelihood methods, and Bayesian statistics.

STA 403 Regression Analysis

3 Credits, Fall Semester

Regression analysis and introduction to linear models. Topics: Multiple regression, analysis of covariance, least square means, logistic regression, and non-linear regression. This course emphasizes hands-on applications to datasets from the health sciences.

STA 404 Analysis-of-Variance

3 Credits, Spring Semester

Advanced presentation of statistical methods for comparing populations and estimating and testing associations between variables. Topics: Point estimation, confidence intervals, hypothesis testing, ANOVA models for 1, 2 and k way classifications, multiple comparisons, chi-square test of homogeneity, Fisher's exact test, McNemar's test, measures of association, including odds ratio, relative risks, Mantel-Haenszel tests of association, and standardized rates, repeated measures ANOVA, simple regression and correlation. This course includes a one-hour computing lab and emphasizes hands-on applications to datasets from the health-related sciences.