By the end of this chapter, we will understand how to proceed when the ANOVA tells us that the mean responses differ, (i.e., the levels are significantly different), among our treatment levels. We will also briefly discuss the situation that the levels are a random sample from a larger set of possible levels, such as a sample of brands for a product. We will briefly discuss multiple comparison procedures for qualitative factors, and regression approaches for quantitative factors. These are covered in more detail in the STAT 502 course and discussed only briefly here.
Focus more on the design and planning aspects of these situations:
- How many observations do we need?
- to achieve a desired precision when the goal is estimating a parameter, and
- to achieve a desired level of power when hypothesis testing.
- Understanding which multiple comparison procedure is appropriate for your situation
- Be able to allocate our observations among the k treatment groups.
- Understanding that the Dunnett Test situation has a different optimum allocation
- Describes the F-test as an example of the General Linear Test.