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Statistical Methods in Public Health II

2nd Term
Academic Year
2023 - 2024
Instruction Method
Asynchronous Online with Some Synchronous Online
Lab Note
After the course opens, students will sign up in the CoursePlus Sign-up Sheets for a weekly Lab Session with review of a structured Lab Exercise. - The format is either onsite or online (synchronous virtual via Zoom). - The data analysis tool is either Stata or R. Note: Students choosing R should have prior experience using a computer programming language (Python, C, R, MATLAB, etc.)
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year


Presents use of confidence intervals and and hypothesis tests to draw scientific statistical inferences from public health data. Introduces generalized linear models, including linear regression and logististic regression models. Develops unadjusted analyses and analyses adjusted for possible confounders. Outlines methods for model building, fitting and checking assumptions. Focuses on the accurate statement of the scientific question, appropriate choice of generalized linear model, and correct interpretation of the estimated regression coefficients and confidence intervals to address the question.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Use statistical reasoning to formulate public health questions in quantitative terms
  2. Distinguish between the appropriate generalized linear regression models for expressing the relationship between a response (dependent variable or outcome) and one or more independent variables
  3. Recognize the assumptions required in using regression models and performing statistical tests to assess relationships between an outcome and a risk factor
  4. Use statistical methods for inference, including confidence intervals and tests, to draw valid public health inferences from study data
  5. Formulate and correctly interpret relationships in a linear regression model.
  6. Interpret the correlation coefficient as a measure of the strength of a linear association between a continuous response variable and a continuous predictor variable
  7. Interpret the coefficients, including interaction coefficients, obtained from a multiple linear regression analysis
  8. Estimate a confidence interval for a linear regression coefficient; interpret the interval estimates within a scientific context
  9. Distinguish the summary measures of association applicable to retrospective and prospective study designs
  10. Estimate two proportions and their difference, and confidence intervals for each; interpret the interval estimates within a scientific context
  11. Estimate an odds ratio, or relative risk, and its associated confidence interval; explain the difference between the two and when each is appropriate
  12. Interpret the coefficients, including interaction coefficients, obtained from a multiple logistic regression analysis
  13. Assess whether the relationship between a response (dependent) variable and an independent variable varies by the level of a second independent variable (effect modification)
  14. Recognize the influence of sample size on statistical inferences
  15. Use the Stata statistical analysis or R packages to perform regression analyses
Methods of Assessment
This course is evaluated as follows:
  • 20% Assessments
  • 10% Quizzes
  • 70% Exam(s)
Special Comments

Lectures are asynchronous and pre-recorded. LiveTalks: synchronous online weekly and recorded. After the course opens, each student signs up in CoursePlus for one 80-minute Lab Session.