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Activity Details

CE_02C Sat, 8/2/2014, 8:00 AM - 12:00 PM CC-160C
Adaptive Tests of Significance Using R and SAS — Professional Development Continuing Education Course
Adaptive tests of significance are usually more powerful than the traditional tests when the error distribution is not normal. In addition, there is very little power loss when the errors are normally distributed, so adaptive tests can be recommended for general use in studies having more than 20 observations. In this course we will begin by describing the two-sample adaptive test and demonstrate that it will always maintain its level of significance and that it usually is more powerful than traditional parametric and non-parametric tests with non-normal error distributions. We then will present an adaptive method of testing any subset of coefficients in a multiple regression model and will show how to perform the adaptive tests using R and SAS. Adaptive tests for interaction and main effects in the analysis of factorial experiments and adaptive tests for multicenter trial data will be described and their power will be compared to those of traditional tests. In addition, we will describe a method of computing adaptive confidence intervals. The attendees should be familiar with basic statistical modeling, including multiple regression and the analysis of variance, at the level of Applied Regression Analysis (1998) by Draper and Smith.
Instructor(s): Tom O'Gorman, Northern Illinois University

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