Available Winter term
or equivalent statistical material
416 Rogers Hall
Systematic analysis of processes through the use of statistical analysis, methods, and procedures. Application of statistical techniques including use of classical process analysis techniques, regression and design of experiments.
- Introduction, principles of experimental design
- Sampling distributions, p-value, and operating characteristic curves
- Multiple-linear regression models
- Single factor, completely randomized design
- Single-factor, randomized block design
- Factorial design
The student, upon completion of this course, will be able to:
- Describe (identify/write) how a designed experiment is conducted to investigate the performance of processes and systems based upon the principles of replication, randomization, and blocking.
- Show (write) how the sampling distributions, P-value, and the operating characteristic (OC) curve can be used in experimental design.
- Show (write) how matrix algebra can be conveniently used to illustrate the applicability of multiple-linear regression models to define the association that exists between a response variable and two or more regressor variables.
- Develop the linear statistical model to illustrate the use of analysis of variance (ANOVA) as a technique for a single-factor, completely randomized design.
- Develop the linear statistical model to illustrate the use of ANOVA as a technique for a single-factor, randomized completer block design.
- Describe (write) how the main effects and interaction can be assessed in a two-factor factorial design, and develop (write) a linear statistical model for a two-factor factorial design to perform ANOVA.
- Using the concepts of Course Learning Outcomes 1-3, work as a team of 4-5 students on an open-ended (term project) problem on housing to produce a team-written report to effectively communicate the responses to a series of seven questions.