Courses Taught by Kevin (Zhi) He
BIOSTAT651: Theory and Application of Generalized Linear Models
- Graduate level
- Residential
- Winter term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Baladandayuthapani, Veera He, Kevin (Zhi) (Residential);
- Prerequisites: BIOSTAT601 and BIOSTAT650
- Description: Introduction to maximum likelihood estimation; exponential family; proportion, count and rate data; generalized linear models; link function; logistic and Poisson regression; estimation; inference; deviance; diagnosis. The course will include application to real data.
- Syllabus for BIOSTAT651
| Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | MS | Perform generalized linear regression model fitting and diagnosis | BIOSTAT651 | BIOSTAT | PhD | Perform generalized linear regression model fitting and diagnosis | BIOSTAT651 |
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EPID742: Generalized Linear Models
- Graduate level
- Residential
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): He, Kevin (Zhi) (Residential);
- Prerequisites: None
- Advisory Prerequisites: Introductory level courses in epidemiology and biostatistics.
- Description: Course will cover regression methods for continuous, binary, and count data. The majority of epidemiologic data involve either binary or count data, and binary data often arise from underlying continuous data. Therefore, linear , logistic ,and Poisson regression analyses are important analytic approaches that provide valuable insights into data collected.
EPID784: Survival Analysis Applied To Epidemiologic And Medical Data
- Graduate level
- Residential
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Schaubel, Douglas He, Kevin (Zhi) (Residential);
- Offered Every Summer
- Last offered Summer 2019
- Prerequisites: An introductory course in (bio)statistics that covers regression is required. Familiarity with a statistical software package is helpful, but example code will be provided in labs.
- Advisory Prerequisites: The mathematical level is completely accessible with knowledge of high school algebra, one semester of calculus, and a one-year course in basic statistical methods.
- Description: The primary objectives of this course are to provide participants with the background required to understand commonly used survival analysis methods and to apply such methods using standard statistical software. The course material relies heavily on examples and intuitive explanations of concepts.
