Spring 2020 - STAT 410 D100

Statistical Analysis of Sample Surveys (3)

Class Number: 3988

Delivery Method: In Person


  • Course Times + Location:

    Jan 6 – Apr 9, 2020: Wed, 3:30–4:20 p.m.

    Jan 6 – Apr 9, 2020: Fri, 2:30–4:20 p.m.

  • Exam Times + Location:

    Feb 12, 2020
    Wed, 6:00–7:50 p.m.

  • Prerequisites:

    STAT 350.



An introduction to the major sample survey designs and their mathematical justification. Associated statistical analyses. Quantitative.


Course Outline:

This course covers the major ideas and methods of modern survey sampling.

  1. Ideas of sampling, overview of application areas.  Use of the free statistical software package R to select random samples and explore sampling ideas through simulation and graphics.
  2. Simple Random Sampling: Selecting random samples with and without replacement, concept of population and sampling frame, estimating means, totals, and proportions, the finite population correction factor, confidence intervals, use of the normal approximation, choosing the sample size.
  3. Unequal probability sampling.  How to select a sample of units with unequal selection or inclusion probabilities, unbiased estimation with unequal probability designs.
  4. Stratified Random Sampling: Stratification of a population, selecting stratified random samples, advantages of stratification, gains in precision, confidence limits, optimal sample sizes, stratification after selection.
  5. Ratio and Regression Estimation: Use of auxiliary information, bias, mean square error, gains in precision, confidence intervals, design versus model based approaches.
  6. Cluster and systematic Sampling: Selection and estimation methods, potential advantages and disadvantages.


  • Assignments 20%
  • Midterm 25%
  • Project 10%
  • Final 45%


Above grading is subject to change.



Required Text:

Sampling: Design and Analysis, 2nd ed. 
by Sharon Lohr, 2010. Publisher: Cengage
ISBN: 9780495105275

Department Undergraduate Notes:

Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or csdo@sfu.ca

Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.

Registrar Notes:

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