Course Course Summary Section 1 Content Section 1 Content Left Section 1 Content Right Credit Type: Course ACE ID: SOEL-0193 Organization's ID: RRENG 82519 Organization: AMC Logistics Leadership Center Location: Classroom-based Length: 8 weeks (60 hours) Dates Offered: 6/1/2012 - 5/30/2015 Credit Recommendation & Competencies Section 2 Content Section 2 Content Left Section 2 Content Right Level Credits (SH) Subject Graduate 3 Multivariate Analysis Ii Student must achieve a minimum of 80 percent for graduate credit. Description Section 3 Content Section 3 Content Left Section 3 Content Right Objective: The course objective is to provide the students, who are current and future engineers for the Army and other Federal agencies, the capability and expertise required to plan, conduct, analyze and evaluate the results of multi-factor experiments. Learning Outcomes: Assess which factors most influence the outcome of an experiment, which factors, if any, exhibit interactive effects with each other and also whether the model which results from the analysis will be adequate to assess the performance of the system or p Develop, from basic principles, the maximum likelihood estimators for various models when the response vector is binary in nature Extend the already-developed models to provide the flexibility afforded by the inclusion of a binary indicator covariate term, in order that realistic situations involving subpopulations may be appropriately modeled and statistical tests of significance applied Develop and derive approximate procedures which accommodate nonlinear models in the complete analysis, particularly the Weibull model General Topics: Design of experiments and factorial designs, main effects and interactions, analysis of 2k experiments, square and cube plots of results, corrective measures and transformations, modeling variation, model building and diagnostics for fractional factorial experiments, maximum likelihood estimation methods for multivariate analysis, estimating reliability as a function of covariates, and modeling categorical covariates using indicator variables Instruction & Assessment Section 4 Content Section 4 Content Left Section 4 Content Right Instructional Strategies: Case Studies Classroom Exercise Computer Based Training Discussion Lectures Practical Exercises Supplemental Materials Section 5 Content Section 5 Content Left Section 5 Content Right Section 6 Content Section 6 Content Left Section 6 Content Right Button Content Rail Content 1 Page Content