Courses

This applied advanced statistics course is designed to enhance foundational students will learn to compute and meaningfully interpret advanced statistical Equivalents: COUN 7374

Non-parametric statistics, time series analysis, Bayesian inference, and other topics

Non-parametric statistics, time series analysis, Bayesian inference, and other topics STAT 5310 and STAT 6310.

Correlation and regression, analysis of variance, nonparametric methods, multivariate analysis. Emphasis on applications. Includes introduction to statistical computing using SAS. Duplicate credit not allowed for MATH 3670 and BUSN 2750.

Advanced quantitative methods in psychology. Topics include linear regression, analysis of variance, non-parametric techniques, estimation procedures, individual and multiple comparisons, and experimental design.

Students develop competencies in the use of statistical methods as tools for screening, effect sizes, and simple effects tests, and an introduction to multiple

Parameter estimations, Maximum likelihood estimation, Sufficient statistic, Rao-Cramer bound, Hypothesis testing, Neyman-Pearson Lemma, p-value, Regression, Order statistics, Nonparametric methods: Wilcoxon test, Run test, and Kolmogorov-Smirnov test, Anderson-Darling test, P-P plot, Q-Q plot, testing for distribution type, location and scale parameters, mean squared error.

Students will gain a broad understanding of common research methods and data analysis that are used in healthcare research. Students will be exposed to common statistical data analysis for biostatistical problems.

This course covers advanced statistical principles foundational to modern machine learning, emphasizing both theoretical rigor and practical applications. Topics include exponential families, sufficiency, information theory, likelihood-based methods, and Bayesian inference. The course explores spectral methods, probabilistic graphical models, sufficient dimension reduction, contrastive learning, and embedding techniques, with applications in deep generative models and transformers.

Application of advanced statistical theory and methods to the analysis of social data.