STAT 440: SAMPLING THEORY

COURSE OUTLINE

FALL 2021 (Section 0201)

Instructor: Paul J. Smith, Statistics Program

Office hours: By arrangement using Zoom

Telephone: (301) 405-5104

E-mail: pjs@umd.edu

Schedule: Fall 2021, MWF 11, MTH 0403

Textbook: Thompson, S. K. (1999). Sampling (3rd ed.). Hoboken, NJ: J. Wiley.

Prerequisites: At least one semester of statistics, preferably STAT 401 or STAT 420.

Course Description:

Sampling refers to the statistical techniques used in political polls, marketing surveys, federal data gathering, environmental surveys and many other areas of social science and public health.

This course provides an introduction to methods of sampling and analyzing data from finite populations from both a theoretical and applied perspective. It is intended for Statistics and Mathematics students interested in applications and students in the Applied Statistics track of the Survey Methodology program, as well as students in disciplines such as business, life science or social science who need sampling in their research.

We will use the computer package R throughout the course, both to analyze real data and to illustrate statistical principles using simulation.

The mathematics used in the course is not deep, but it can be intricate. It is essential that you understand basic probability and statistical concepts such as point estimation, confidence limits, regression and the central limit theorem.
STAT 440 is part of the required material for the Written Examinations in Applied Statistics.

Topics:

Basic concepts: populations, probability samples, sample design, bias, sampling and nonsampling errors.
Simple random sampling: statistics, estimates, choosing the sample size, confidence limits.
Estimating proportions, ratios and subpopulation means.
Sampling with unequal selection probabilities.
Using auxiliary information: ratio and regression estimation.
Stratified sampling.
Cluster sampling and complex surveys.
Nonresponse: effects and adjustments.
Advanced topics: double sampling, variance estimation, categorical data analysis and regression in complex surveys.

Examinations and Grading

Midterms: Monday, Oct. 4 and Friday, Oct. 29 (tentative).

Final: Tuesday, December 21, 8-10 a.m., MTH 0403.

Homework: Frequent problem sets will be assigned. These will be a mix of theoretical and applied problems involving analysis of real data sets on the computer. Assignments will be posted on ELMS.

Grading: The midterms will each count for approximately 20% of the grade, the final will count for 30%, and the homework will count for 30%. Homework will not be accepted late.

References:

Cochran, W. J. (1977). Sampling Techniques (3rd. ed.). New York: J. Wiley.

Dalgaard, P. (2008). Introductory Statistics with R (2nd. ed.). New York: Springer

Lohr, S. L. (2019). Sampling: Design and Analysis (2nd ed.). Chapman & Hall/CRC Press.

Sarndal, C.-E., Swensson, B., and Wretman, J. (1992). Model Assisted Survey Sampling. New York: Springer.

Spector, P. (2008). Data Manipulation with R. New York: Springer.

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