Public Health, Biostatistics (PHST)

PHST 301. Quantitative Methods in Public Health3 Units

Term Typically Offered: Fall, Spring

Prerequisite(s): MATH 111 or Global Health minor.

Description: The course is an introduction to the concepts and theory behind quantitative analysis methods used in public health. The content focuses on how and why different statistical methods are used with minimal emphasis on statistical calculations. The skills of critical thinking, communication, and teamwork are promoted and cultivated throughout this course.

For class offerings for a specific term, refer to the Schedule of Classes

PHST 500. Introduction to Biostatistics for Health Sciences I3 Units

Prerequisite(s): Enrolled as a student in the Master's in Public Health (MPH) program, the MSc or Certificate in Clinical Investigation Sciences program.

Description: This course is a graduate level introduction to descriptive and inferential statistical methods including confidence intervals and hypothesis tests for 1- and 2-samples, power and sample size calculation, one-way analysis of variance, and simple linear regression. Requisite background material on basic probability, distributions, and sampling is covered. A statistical software package will be used to execute the descriptive, graphical, and inferential statistical techniques on real data sets.

For class offerings for a specific term, refer to the Schedule of Classes

PHST 501. Introduction to Biostatistics for Health Sciences II3 Units

Prerequisite(s): PHST 500 and enrolled as in the Master's in Public Health (MPH) program, the MSc or Certificate in Clinical Investigation Sciences program.

Description: This course is a continued graduate level introduction to inferential statistical methods, covering multi-way analysis of variance, multiple regression, the chi-square analysis of frequencies and logistic regression, survival analysis, and nonparametric statistical methods. A statistical software package will be used to execute the descriptive, graphical, and inferential statistical techniques on real data sets.

For class offerings for a specific term, refer to the Schedule of Classes