Statistical and Data Sciences 390 - TOPICS/STATISTICAL/DATA SCIENC

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining. This course may be repeated for credit with different topics. Prerequisites: MTH/SDS 290 or MTH/SDS 291 or MTH/SDS 292. : Theory and applications of statistical methods for the analysis of categorical data. The course includes an overview of statistical methods for analyzing discrete data including binary, multinomial, and count response variables. Nominal and ordinal responses will be considered. Topics may include contingency table and chi-squared analyses, logistic, Poisson, and negative-binomial regression models. R statistical software will be used. Prerequisites: Regression Analysis (MTH 291), Research Design (MTH 290), or Data Science (MTH 292), or permission of the instructor.