Tentative Class Schedule

Tentative Class Schedule

Week 1: Introductions

Week 2: Chapter 1

Week 3: Sampling Distributions and R

Week 4: Statistical Basics

Week 5: Confidence Intervals

  • Monday September 23
    • Confidence Intervals
    • Quiz 5 due
    • Weekly Notes (continued from week 4)
  • Wednesday September 25
    • Sample Size Calculations
  • Friday September 27
    • Sample Size Calculations/ Finish Lab 3

Week 6: Sample Size Calculations

Week 7: Midterm Week

Week 8: Stratified Sampling

Week 9: Stratified Sampling / Ratio Estimation

Week 10: Ratio & Regression Estimation

Week 11: Ratio & Regression Estimation

Week 12: Cluster Sampling

Week 13: Sampling with Unequal Probabilities

Week 14: Sampling with Unequal Probabilities

Week 15: Bootstrap

Week 15: Final Exam Week

  • Thursday December 12, 4:00 - 5:50: In Class Final
  • Friday December 13, 8:00 AM: Take Home Final Due

Course Description

This course provides an overview of design and estimation for statistical sampling. Computational procedures in the class will be conducted using R and/or SAS.

Learning Outcomes:

Upon successful completion of this course, students will:

  • Learn the classical sampling survey procedures applied to finite populations (including simple random sampling, stratified sampling, cluster sampling, and systematic sampling).

  • Learn sampling methods that utilize unequal probability sampling.

  • Learn ratio and regression analytical methods that utilize auxiliary variable information.

  • Learn bootstrap resampling estimation methods.

  • Gain practical experience via proposing and running a sampling design, and presenting the results in a statistical report and formal class presentation.

Office Hours

  • M: 2 - 3
  • WF 12 - 1

Prerequisites

  • Required: One of: STAT 217Q, STAT 332, or STAT 401

Textbooks

  • Sampling: Design and Analysis, second edition, by Sharon Lohr. Note: the first edition acceptable

Additional Resources

Analysis and data visualization will be implemented with:

Course Policies

Grading Policy

  • 10% of your grade will be determined by weekly quizzes to be completed prior to class on Mondays.

  • 15% of your grade will be determined by homework assignments. Students are allowed and encouraged to work with classmates on homework assignments, but each student is required to complete their own homework.

  • 15% of your grade will be determined by lab assignments. Labs will be in-class group assignments. Often there will be a large computational element in the labs.

  • 20% of your grade will be determined by a midterm exam. The midterm exam will have two parts: an in class exam and a take home portion. The in class portions will be largely conceptual including some short mathematical derivations. The take home portions will focus on analysis of data and implementation of Bayesian computational methods.

  • 20% of your grade will be determined by a final exam. The final exam will have two parts: an in class exam and a take home portion. The in class portions will be largely conceptual including some short mathematical derivations. The take home portions will focus on analysis of data and implementation of Bayesian computational methods.

  • 20% of your grade will be determined by a project. There are two options for the project: carryout a sampling scheme to address a research question of your choosing or conduct a sampling scheme on a synthetic example. Additional details about the project can be found here. The final project will consist of written summary including an overview of the sampling design, and analysis of the data, and a summary of the results, as well as an in-class presentation.

Collaboration

University policy states that, unless otherwise specified, students may not collaborate on graded material. Any exceptions to this policy will be stated explicitly for individual assignments. If you have any questions about the limits of collaboration, you are expected to ask for clarification.

In this class students are encouraged to collaborate on homework assignments, but quizzes should be completed without collaboration.

Academic Misconduct

Section 420 of the Student Conduct Code describes academic misconduct as including but not limited to plagiarism, cheating, multiple submissions, or facilitating others’ misconduct. Possible sanctions for academic misconduct range from an oral reprimand to expulsion from the university.

Disabilities Policy

Federal law mandates the provision of services at the university-level to qualified students with disabilities. Make sure to include all that relevant information here.

Course Outline

The course will cover the following topics:

  1. Introduction to sampling and scope of interest,
  2. Simple random sampling,
  3. Stratification,
  4. Bootstrap method,
  5. Ratio and regression estimators,
  6. Cluster sampling,
  7. Sampling with unequal probability, and
  8. Other topics based on student interest as time permits (including capture-recapture procedures, …).