UGBS 301 Quantitative Methods for Business Session 5: Analysis of Variance Slide 1
Session Overview In the previous session, we looked at comparing means between two groups (independent and dependent group). In this session, we shall look at comparing average values for three independent groups.
Section Goals After completing this section, you should be able to: • Recognize situations in which to use analysis of variance • Understand different analysis of variance designs • Perform a single-factor hypothesis test and interpret results • Conduct and interpret post-analysis of variance pairwise comparisons procedures • Analyze two-factor analysis of variance test with replications results Chap 11-3
Reading List 1. Read chapter 12 of Groebner, Shannon, Fry, and Smith 2. Read chapter 13 of Anderson, Sweeney, and Williams Slide 4
General ANOVA Setting Chap 11-6 Investigator controls one or more independent variables Called factors (or treatment variables) Each factor contains two or more levels (or categories/classifications) Observe effects on dependent variable Response to levels of independent variable Experimental design: the plan used to test hypothesis
One-Way Analysis of Variance Evaluate the difference among the means of three or more populations Examples: Accident rates for 1 st , 2 nd , and 3 rd shift Expected mileage for five brands of tires Assumptions – Populations are normally distributed – Populations have equal variances – Samples are randomly and independently drawn Chap 11-7
Completely Randomized Design • Experimental units (subjects) are assigned randomly to treatments • Only one factor or independent variable – With two or more treatment levels • Analyzed by – One-factor analysis of variance (one-way ANOVA) • Called a Balanced Design if all factor levels have equal sample size Chap 11-8
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- Fall '19