MS Error:Mean Squares of Error is the variability after we've explained everything we could with different groups.MS treatment:Mean Squares of Treatment/Group is the variances explained by the treatment category.
This is the sum of squares within the degree to which individuals are varying around their group means.įrom Sum of Squares, we also get Mean squares, or the variance associated with each source. SS Error:Sum of Squares of error is the raining variance.This value explains how much variability a treatment/group can explain in the Sum of Square Total. SS treatment:Sum of Squares of treatment is the sum of squares associated with a certain explanatory factor, which is the Airline group in this example.Our ANOVA model is working to explain this total variability on the basis of some treatment or group. Total, also known as the corrected total, is the sum of squares associated with the Y column (cost of flight), which is the value of sum of squares if we ignore the model all together and simply looking for the variability in cost of flight. Variability being the differences between individual costs and the grand mean.įor this module, we will be exploring the functionalities of some of the JMP features that allow us to perform ANOVA and Pairwise comparison.Īnalysis of Variance Sum of Squares It is our hope to analyze a factor that can either absorb or explain some of the variability. Using the Cost of Flight data generated, we will be seeking an explanation for variances among each group, trying to answer the question: why do people's cost of flight differ from the grand mean. In JMP, we can perform One-Factor (one way) ANOVA using two different methods: Fit Y by X, and Fit Model. 5.2 Controlling Alpha for Unplanned Comparison.5.1 Controlling Alpha for Planned Comparison.4.2.3 Inference about the Problem of Multiplicity and Alpha Escalation.4.2 JMP Demonstration with Sample Data-IQ Drugs.4 The Problem of Multiplicity and Alpha Escalation.
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