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STAT 471 Sampling and Experimental Design Units: 3.00
Simple random sampling; Unequal probability sampling; Stratified sampling; Cluster sampling; Multi-stage sampling; Analysis of variance and covariance; Block designs; Fractional factorial designs; Split-plot designs; Response surface methodology; Robust parameter designs for products and process improvement.
NOTE Offered jointly with STAT 871.
NOTE Offered jointly with STAT 871.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: Prerequisite ([STAT 361/3.0 or ECON 351/3.0] and STAT 463/3.0) or per¾ÞÈéÊÓƵ of the Department.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Develop a theoretical understanding of analysis of variance in simple comparative experiments, factorial designs, and fractional factorial designs with and without blocking; gain a deep understanding of optimal design theory.
- Apply designs of experiments and sampling methods to collect data; analyze data from designs of experiments and different sampling schemes; draw conclusions from data analysis in various types of experiments.
- Use functions in R packages and writing R codes to analyze data from designs of experiments and different sampling scheme.
- Apply resampling methods such as bootstrap, Jackknife, balanced repeated replication for variance estimation.
- Determine proper factorial experiments and sampling schemes for problems in applications.
- Identify the problems of designs of experiments and/or survey sampling in many real applications.
- Present topics of design of experiments and/or survey sampling clearly and effectively.