Fall 2021 - HSCI 410 D100

Exploratory Data Analysis (3)

Class Number: 2138

Delivery Method: Remote

Overview

  • Course Times + Location:

    Fr 2:30 PM – 5:20 PM
    BLU 11660, Burnaby

  • Exam Times + Location:

    Dec 16, 2021
    2:00 PM – 2:00 PM
    TAKE HOME-EXAM, Burnaby

  • Prerequisites:

    STAT 302 or STAT 305, with a minimum grade of C-. Recommended: HSCI 330.

Description

CALENDAR DESCRIPTION:

Regression and data analysis techniques for health research. Practical approaches to linear and logistic regression, multivariable modelling, interaction, variable selection, confounding, and measures of association. Computer-based laboratory exercises using statistical software applied to health datasets.

COURSE DETAILS:

By completing the required pre-requisites for this course you obtained a solid foundation in statistics. This is a practice-based course that builds on your previous work and provides an opportunity for you to grow by applying what you learned. This will be achieved by showing you how to use R Studio, an open source statistical software programming interface. To learn how to use R Studio, you will (1) prepare for class by completing assigned readings, (2) attend weekly lectures via Zoom, (3) complete weekly assignments, (4) conduct statistical analyses and write a term paper based on the results of these analyses, and (5) complete a cumulative final exam. The table below provides a brief overview of the content covered in this course:

Date

Topic

September 10

Lecture 1: Introduction to HSCI 410

September 17

Lecture 2: Introduction to R Studio and R Programming

September 24

Lecture 3: Descriptive Statistics

October 1

Lecture 4: Data manipulation

October 8

Lecture 5: Supervised Analysis Time for Term Paper

October 15

Lecture 6: Correlation

October 22

Lecture 7: Linear Regression

October 29

Lecture 8: Logistic Regression

November 5

Lecture 9: Supervised Analysis Time for Term Paper

November 12

Lecture 10: Variable and Model Selection

November 19

Lecture 11: Mediation and Moderation Analyses

November 26

Lecture 12: Principal Component and Factor Analysis

December 3

Lecture 13: Supervised Analysis Time for Term Paper

COURSE-LEVEL EDUCATIONAL GOALS:

Upon completion of this course, you should be able to:

  • Describe the basic concepts in linear and logistic regression modelling;
  • Describe and apply modelling concepts from epidemiology including interaction, confounding and summary measures of effect, and what variables to put in your model;
  • Describe common applications of regression in the health sciences;
  • Be able to interpret and critically assess reports in the literature and media; and
  • Apply statistical software for linear and logistic regression models.

Grading

  • Weekly Worksheets 10%
  • Term Paper 60%
  • Final Exam 30%

NOTES:

HSCI 410 is an upper division undergraduate course. In the Faculty of Health Sciences, student performance for upper division undergraduate courses is typically evaluated such that the median letter grade is a B or B+ and no more than 8% of students receive an A+. The table below describes the standards of student performance associated with each letter grade:

Letter Grade

Description

A+

Excellent performance. Work and learning exemplifying the highest quality possible.

A

Superior performance in all elements of the course. Unquestionably prepared for subsequent courses in field.

A-

Superior performance in most aspects of the course. Unquestionably prepared for subsequent courses in field.

B+

Good. High quality performance in all or most elements of the course. Very good chance of success in subsequent courses.

B

Good. High quality performance in some of the course; satisfactory in others. Good chance of success in subsequent courses.

B-

Satisfactory performance in the course. Evidence of sufficient learning to succeed in subsequent courses.

C+

Satisfactory performance in most of the course, with the remainder being somewhat substandard. Evidence of sufficient learning to succeed in subsequent courses, with effort.

C

Evidence of some learning, but generally marginal performance. Marginal chance of success in subsequent courses.

C-

Poor. Minimal learning and substandard performance throughout the course. Doubtful chance of success in subsequent courses.

D

Poor. Minimal learning and low quality performance. Doubtful chance of success in subsequent courses.

F

Failure. Complete absence of evidence of learning. Completely unprepared for subsequent courses.


Your final grade in this course is based on the letter grading system described above. Your final grade is not based on numerical cut-offs, scores, or percentages.

Materials

MATERIALS + SUPPLIES:

Required readings for this course are listed in the Course Schedule section. All readings are free and publically available. As such, you are not required to purchase a textbook for this course. However, you will need access to an internet-enabled computer capable of accessing Canvas and running R and R-Studio. These programs are free to download and use.

If you are not confident in your statistical knowledge or ability, you may wish to purchase a statistics textbook, such as one of the following listed below:

  • Pagano & Gauvreau. Principles of Biostatistics, 2E
  • Gould, Ryan, Stallard, Boue. Introductory Statistics: Exploring the World Through Data, Canadian Edition
  • Dohoo, Martin, Stryhn. Methods in Epidemiological Research
  • Szklo, Nieto. Epidemiology, Beyond the Basics. Fourth Edition

You may also rely on any web-based resources, such as youtube channels, statistics wikis, or online statistics courses.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site 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

TEACHING AT SFU IN FALL 2021

Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place.  Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.