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Chapter 43 of the document discusses the MULTTEST procedure, which addresses the challenges of multiple hypothesis testing in statistical analysis. It highlights the increased risk of false significances when conducting multiple tests on the same dataset and presents various methods for adjusting p-values, including Bonferroni, Sidak, Hochberg's method, and resampling techniques like bootstrap and permutation. The chapter provides detailed syntax for using PROC MULTTEST, examples of its application in drug effect studies, and outlines how to interpret output data sets containing raw and adjusted p-values.

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What is the Fastclus procedure?

The FASTCLUS procedure combines an effective method for finding initial clusters with a standard iterative algorithm for minimizing the sum of squared distances from the cluster means. The result is an efficient procedure for disjoint clustering of large data sets.

What is the best analysis for categorical variables?

The Chi-square (2) probability distribution is particularly useful in analyzing categorical variables. A number of tests yield test statistics that fit, at least approximately, a 2 distribution and hence are referred to as 2 tests.

What is the BH procedure?

The CATMOD procedure performs categorical data modeling of data that can be represented by a contingency table. PROC CATMOD fits linear models to functions of response frequencies, and it can be used for linear modeling, log-linear modeling, logistic regression, and repeated measurement analysis.

What is the Catmod procedure?

Multiple testing correction adjusts the individual p-value for each gene to keep the overall error rate (or false positive rate) to less than or equal to the user-specified p-value cutoff or error rate.

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