PhD basic courses in transferable skills

 

Sampling

Objective, including learning outcomes The objective of the course is to give a broad introduction to basic sampling theory and related statistical inference. The course is built on lectures, exercises and computer exercises. Exercises and computer exercises are mandatory. On completion of the course, the student will be able to: • understand basic concepts of sampling from finite and infinite populations • describe some basic sampling methods including conditions and assumptions • describe and apply basic estimators for survey sampling • select an appropriate sampling method for a given problem • carry out a basic probability sampling survey • interpret and evaluate results from basic surveys correctly and draw reasonable conclusions • clearly and concisely communicate results and conclusions • use statistical software for sampling Content The main contents are as follows: • Simple random sampling • Systematic sampling • Cluster sampling • Stratified sampling • The Horvitz-Thompson estimator and unequal probability sampling • Two-stage and two-phase sampling • Basic point and plot sampling of an infinite population • Line-intercept sampling • Detectability in sampling • Line-transect sampling • Capture-recapture estimation

Extent:
4 credits

Prerequisites
Statistics I: Basic statistics, 4 hp or similar. Admitted to a Ph.D. program at SLU. If you are not a Ph.D. student or not at SLU, contact Anton.Grafstrom@slu.se to check the availability of places in the course.

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