Students will learn different probability based methods for sampling from a finite 
population. They will also learn strengths and weaknesses of these methods, enabling 
them to assess which method is preferable for a particular problem. Inference methods 
for population means, population totals and population proportions are discussed for 
each of the sampling methods, and students will also learn about the possible use of 
ratio estimators, regression estimators and difference estimators in the presence of 
auxiliary information. They will learn expressions for estimated variances of 
estimators and bounds for the error of estimation, and will also learn how to use these 
quantities in drawing and formulating conclusions about the population parameters of 
interest. In addition, students will learn how to select an appropriate sample size for 
each objective and for every sampling method discussed. They will learn how to perform 
inferences based on the methods in this course by using a statistical software package.

The topics covered in this course focus on different plans for probability sampling 
from a finite population, different estimation techniques, measures of uncertainty for 
the estimators, and implementation via statistical software. Keywords to describe the 
main topics are: Simple random sampling; stratified random sampling; ratio, regression 
and difference estimation; systematic sampling; cluster sampling; two-stage cluster 
sampling; estimating a population size.