STAT 5290 OL1 (CRN: 15597)
Statistics: Survivl/Logistic Regression (OL1)
3 credit hours
About STAT 5290 OL1
Models and inference for time-to-event and binary data. Censored data, life tables, Kaplan-Meier estimation, logrank tests, proportional hazards models. Logistic regression-interpretation, assessment, model building, special topics. Prerequisite: Graduate student or Instructor permission; content knowledge of STAT 3210 or STAT 5210 assumed.
Notes
Online Asynchronous
Section Description
STAT 5290 is a fully online, asynchronous graduate-level course covering logistic regression and survival analysis. Through weekly modules, case studies, and hands-on analysis, students learn to model binary outcomes, interpret hazard ratios, and manage censored survival data. The course emphasizes both conceptual understanding and practical implementation using statistical software, preparing students to apply these methods in research and professional practice.
Section Expectation
Students should be comfortable using R for statistical computing and have prior coursework or experience in multiple linear regression and basic statistical inference.
Evaluation
Student learning will be assessed through participation in online discussions, short quizzes, applied assignments using R, and a final data analysis project integrating logistic regression and survival analysis techniques.
Important Dates
Courses may be cancelled due to low enrollment. Show your interest by enrolling.
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