Fall 2023 - STAT 605 G100

Biostatistical Methods (3)

Class Number: 6347

Delivery Method: In Person


  • Course Times + Location:

    Sep 6 – Dec 5, 2023: Mon, 2:30–3:20 p.m.

    Oct 10, 2023: Tue, 2:30–3:20 p.m.

    Sep 6 – Dec 5, 2023: Thu, 2:30–4:20 p.m.

  • Exam Times + Location:

    Dec 10, 2023
    Sun, 7:00–10:00 p.m.

  • Prerequisites:

    Any course in Statistics. Open only to students in departments other than Statistics and Actuarial Science.



Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. Students with credit for STAT 305 may not take this course for further credit.


STAT Workshop Coordinator: Marie Loughin

Course Outline:

This upper-division course provides an opportunity for the further development of analytic skills acquired in basic courses in statistics and the health sciences. It concentrates on a few, basic techniques that are commonly used in health research, but it also seeks to provide a conceptual basis for understanding other techniques. The course focuses on unifying principles and widely applicable methods as opposed to rote memorization of an array of unrelated, ad-hoc procedures. The material is presented descriptively, from the point of view of understanding and practical use.

The emphasis of the course is on analysis (rather than design) of observational studies with one outcome variable of primary interest and where the outcomes observed on different individuals can be treated as independent. Important areas not covered are classical multivariate analysis (e.g., factor analysis, discriminant analysis, etc.), longitudinal data analysis, time series, random effects models, and experimental design.


By the end of the course, the participant should: 

1. Understand the concept of a statistical model and how such models correspond to specific hypotheses or questions,
2. Be able to interpret the results of an analysis in relation to the original questions or hypotheses that motivated the analysis,
3. Be familiar with basic data analysis methods commonly used in health sciences.


1. Review of introductory statistics from the pre-requisite course: Hypothesis testing, estimation, and confidence intervals for means and proportions.
2. Review of basic concepts of probability, with applications including diagnostic testing, sensitivity and specificity, the relative risk, and the odds ratio.
3. Contingency tables: The Chi-square test, r x c tables, multiple 2x2 tables, Simpson's paradox, Mantel- Haenszel method.
4. Simple linear regression: Interpretation, estimation and testing of regression coefficients, evaluation of the fit of the model.
5. Multiple linear regression: Interpretation, estimation and testing of regression coefficients, confounding and interaction, indicator variables, model selection, prediction, model assumptions and checking.
6. Logistic regression: Interpretation, inference for regression coefficients, model assumptions, case-control studies.



  • Assignments 20%
  • Midterm 1 20%
  • Midterm 2 20%
  • Final Comprehensive Exam 40%


There will be no make-up midterms.

Students must pass the final exam in order to pass the course.

Above grading is subject to change.



Principles of Biostatistics (2nd ed.) by M. Pagano, K. Gauvreau. Publisher: Brooks/Cole and CRC Press

Book is available through the SFU Bookstore

STAT2 Modeling with Regression and ANOVA, 2nd ed. by A. Cannon, G.W. Cobb, A. Hartlaub, et al. Publisher: Macmillan Learning

A hard copy of the book is available through the SFU Bookstore.
An e-version of the book is available through vitalsource.com.


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html


Students with a faith background who may need accommodations during the semester are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.