Download Free A Monte Carlo Simulation Comparing The Empirical Power Of Three Multiple Comparison Procedures In A Two Group Multiple Dependent Variable Scenario Book in PDF and EPUB Free Download. You can read online A Monte Carlo Simulation Comparing The Empirical Power Of Three Multiple Comparison Procedures In A Two Group Multiple Dependent Variable Scenario and write the review.

A study was conducted on the multiple comparison methods presented by Scheffe, Tukey, Student-Newman-Keuls, and Duncan under the experimental situation in which all populations were normal with equal variances and all means but one were equal. The characteristics of all four test procedures were compared for the case of multiple comparisons of pairs of means. These tests were conducted both with and without the prior performance of an analysis of variance. The Tukey and Scheffe procedures were compared in tests of linear combinations of three means. Estimates were made of the power of the tests and of Type I error rates under both the null and alternate hypotheses. Scheffe's method was found to be too conservative for pairwise comparisons of means, but it was to be preferred over Tukey's method for combinations of more than two means. Duncan's method was the most powerful test of pairwise comparisons, but it maintained little control over one kind of Type I error. The S-N-K procedure showed a good balance between power and control of Type I errors. (Author).
A study was conducted on the multiple comparison methods presented by Scheffe, Tukey, Student-Newman-Keuls, and Duncan under the experimental situation in which all populations were normal with equal variances and all means but one were equal. The characteristics of all four test procedures were compared for the case of multiple comparisons of pairs of means. These tests were conducted both with and without the prior performance of an analysis of variance. The Tukey and Scheffe procedures were compared in tests of linear combinations of three means. Estimates were made of the power of the tests and of Type I error rates under both the null and alternate hypotheses. Scheffe's method was found to be too conservative for pairwise comparisons of means, but it was to be preferred over Tukey's method for combinations of more than two means. Duncan's method was the most powerful test of pairwise comparisons, but it maintained little control over one kind of Type I error. The S-N-K procedure showed a good balance between power and control of Type I errors. (Author).
Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
Clear and user-friendly A-Z format, in handy a pocket size, allows speedy access to information in all settings Fully updated and expanded to cover over 500 statistical terms for comprehensive coverage Enhanced explanations of statistical concepts and methods, including more illustrative content, for greater accessibility Frequent use of examples from the medical literature, with reference to landmark studies, ensures clinical relevance Those new to medical statistics and the more experienced reader will find something of interest here
Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.
Monte Carlo Simulation is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples. Christopher Z. Mooney explains the logic behind Monte Carlo Simulation and demonstrates its uses for social and behavioral research in conducting inference using statistics with only weak mathematical theory, testing null hypotheses under a variety of plausible conditions, assessing the robustness of parametric inference to violations of its assumptions, assessing the quality of inferential methods, and comparing the properties of two or more estimators. In addition, Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and illustrates these principles using several research examples. is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples. Christopher Z. Mooney explains the logic behind and demonstrates its uses for social and behavioral research in conducting inference using statistics with only weak mathematical theory, testing null hypotheses under a variety of plausible conditions, assessing the robustness of parametric inference to violations of its assumptions, assessing the quality of inferential methods, and comparing the properties of two or more estimators. In addition, Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and illustrates these principles using several research examples. Monte Carlo Simulation will enable researchers to effectively execute Monte Carlo Simulation and to interpret the estimated sampling distribution generated from its use. will enable researchers to effectively execute Monte Carlo Simulation and to interpret the estimated sampling distribution generated from its use.