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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Self Learning Strategies For Experimental Design And Response Surface Optimization, Adel Alaeddini
Self Learning Strategies For Experimental Design And Response Surface Optimization, Adel Alaeddini
Wayne State University Dissertations
Most preset RSM designs offer ease of implementation and good performance over a wide range of process and design optimization applications. These designs often lack the ability to adapt the design based on the characteristics of application and experimental space so as to reduce the number of experiments necessary. Hence, they are not cost effective for applications where the cost of experimentation is high or when the experimentation resources are limited. In this dissertation, we present a number of self-learning strategies for optimization of different types of response surfaces for industrial experiments with noise, high experimentation cost, and requiring high …
A Comparison Of The Effects Of Non-Normal Distributions On Tests Of Equivalence, Linda Ellington
A Comparison Of The Effects Of Non-Normal Distributions On Tests Of Equivalence, Linda Ellington
Wayne State University Dissertations
Statistical theory and its application provide the foundation to modern systematic inquiry in the behavioral, physical and social sciences disciplines (Fisher, 1958; Wilcox, 1996). It provides the tools for scholars and researchers to operationalize constructs, describe populations, and measure and interpret the relations between populations and variables (Weinbach & Grinnell, 1997; Wilcox, 1996). Given that the majority of real data analysis in the behavioral and social sciences is comprised of non-normally distributed data, it is important that researchers be aware of the effects of non-normal distributions on the probability of detecting equivalence between populations.
The present study examined the effects …
Approximate Vs. Monte Carlo Critical Values For The Winsorized T-Test, Michael Lance
Approximate Vs. Monte Carlo Critical Values For The Winsorized T-Test, Michael Lance
Wayne State University Dissertations
Historically, it has been accepted practice for critical values for the Winsorized t test for independent samples to be based on adjusted degrees of freedom depending on the number of total non-Winsorized (approximate) values. Recently, a new such table of Winsorized critical values has been developed via approximate randomization by Monte Carlo simulation.
Based on eight common data distributions estimated from Psychology and Education along with the normal and five Mathematical distributions, these two tables of values were compared with respect to robustness to types I and II errors through Monte Carlo simulations for one and 10% Winsorized values per …