ANOVA: ANalysis Of VAriance. In the general linear model x = μ + σ 2 (Age) + σ 2 (Genotype) + σ 2 (Measurement) + σ 2 (Condition) + σ 2 ( ε ) Each of the terms σ 2 can be questioned. Moreover, their particular combinations can be studied:
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
x = μ + σ2(Age) + σ2(Genotype) + σ2(Measurement) + σ2(Condition) + σ2(ε)
Each of the terms σ2can be questioned.
Moreover, their particular combinations can be studied:
x = μ + … σ2(Age X Genotype) +…+ σ2(Age X Genotype X Condition) + … + σ2(ε)
P(H0) = f(SSF, SSe, df)
To estimate every effect, all the 3 components shall be known for it! In ANOVA, due to its complexity, it is more problematic than in t-tests
F = MSfactor /MSerror
The F value is distributed in accordance with the F statistics, and provides a p-value for the null hypothesis (σ2(effect) = 0) given the dffactor and dferror
σ2(a factor) derived from the ANOVA results: MSs, Ns, etc.
Allow not only prove an effect of the factor, but to show its strength. Especially useful to compare multiple ANOVA results with each other.