Spring 2022 - STAT 302 D100

Analysis of Experimental and Observational Data (3)

Class Number: 6692

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 12:30 PM – 2:20 PM
    SWH 10081, Burnaby

    We 12:30 PM – 1:20 PM
    SWH 10081, Burnaby

    6:00 PM – 8:00 PM
    Location: TBA

    6:00 PM – 8:00 PM
    Location: TBA

  • Prerequisites:

    One of STAT 201, STAT 203, STAT 205, STAT 270, or BUS 232, with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Quantitative.

COURSE DETAILS:

STAT Workshop Coordinators: Marie Loughin/Harsha Perera

Outline:

  1. Review: Important concepts from the first course in statistics will be reviewed.
  2. Simple linear regression: models summarizing the relationship between two quantitative variables. This unit includes the estimation and interpretation of model parameters, assessment of the model’s fit, inference, and prediction.
  3. Multiple regression: models in which several explanatory variables combine to help explain the variability in a quantitative response variable. This unit includes model assessment, comparison of two regression lines, interactions between explanatory variables, and multicollinearity. Additional topics may include identifying unusual points, variable selection, and/or coding categorical predictors.
  4. Analysis of variance (ANOVA): models that allow the comparison of means of a quantitative response variable across groups defined by a categorical explanatory variable. This unit includes model assessment, inference methods for comparison of means, and tests for homogeneity of variances.
  5. Other topics may include analysis of covariance, the problem of multiple testing, and/or block designs.

 

Grading

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

NOTES:

You must pass the final exam to pass the course.

Above grading is subject to change.

Materials

MATERIALS + SUPPLIES:

We will be using the R programming language, which you can access via Jupyter, an online platform, at https://sfu.syzygy.ca/. Alternatively, you can download R Studio and R statistical software free of charge from https://www.rstudio.com/ and https://cran.r-project.org/, respectively.

REQUIRED READING:

STAT2 Modeling with Regression and ANOVA, 2nd ed. by Cannon, Cobb, 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


Department Undergraduate Notes:

Tutor Requests:
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.

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 SPRING 2022

Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place.  Some courses will still be offered through remote methods, and if so, this 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 spring 2022 term.