24anova_crd2

# 24anova_crd2 - Statistical Techniques I EXST7005 CRD...

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Statistical Techniques I EXST7005 CRD Summary and more

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CRD ANOVA The analysis of variance we have seen is called the Completely Randomized Design (CRD) because the treatments are assigned t the experimental units completely at random The analysis is also called a "one-way analysis of variance". Later we will discuss the Randomized Block Design (RBD).
Key aspects of the analysis Everything is important, but there are some aspects that I consider more important. These are discussed below. The 7 steps of hypothesis testing: Understand particularly the hypothesis being tested and the assumptions we need to make to conduct a valid ANOVA. Understand the tests of the assumptions, particularly the HOV tests and evaluation of normality, particularly Shapiro-Wilks.

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Key aspects of the analysis (continued) Calculations: We will primarily do the ANOVA using SAS. However, it is important to understand that the calculations are based on th marginal totals or means, averaging or summing over all observations in the treatment. This will take on additional significance when we talk about two-way ANOVA.
Key aspects of the analysis (continued) The ANOVA table: Understand the table usually used to express the results of an Analysis of Variance. This same table will also be used for regression. Sum of Mean Source DF Squares Square F Value Pr > F Model 4 838.5976 209.6494 15.38 0.0001 Error 20 272.6680 13.6334 Corrected Total 24 1111.2656

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Key aspects of the analysis (continued) Understand the post-hoc tests. The range tests and contrasts. Be able to interpret these from SAS output. Understand the differences between the post-ho tests (error rates). Only one is correct for a particular objective. Understand that contrasts are best done as a priori tests, and there is less concern with inflated Type I error rates if these are a priori tests. What is the error rate for contrasts by the way?
Expected Mean Square What do we estimate when we calculate a poole variance estimate (MSE) or the sum of squared treatment (SSTreatments) effects divided by its d.f.? The MSE estimates

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## This note was uploaded on 12/29/2011 for the course EXST 7087 taught by Professor Wang,j during the Fall '08 term at LSU.

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24anova_crd2 - Statistical Techniques I EXST7005 CRD...

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