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A Monte Carlo Study of the Robustness and Power of Analysis of Covariance Using Rank Transformation to Violation of Normality with Restricted Score Ranges for Selected Group SizesWongla, Ruangdet 12 1900 (has links)
The study seeks to determine the robustness and power of parametric analysis of covariance and analysis of covariance using rank transformation to violation of the assumption of normality. The study employs a Monte Carlo simulation procedure with varying conditions of population distribution, group size, equality of group size, scale length, regression slope, and Y-intercept. The procedure was performed on raw data and ranked data with untied ranks and tied ranks.
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Efficiency Comparison of Distribution-Free Transformations in the Straight-Line Regression Problem / 非参直线回归问题中不同变换方法的有效性比较Zhang, Ling January 2010 (has links)
<p>In statistical inference of the distribution-free straight-line regression problem, two common transformations, rank transformation and sign transformation, are used to construct the test statistics. When shall we need to use the transformations and which transformation is more efficient are two common questions met by researchers. In this thesis, we will discuss the comparison of the efficiencies of the statistics before and after the rank transformation or the sign transformation in both theoretical and practical ways. Simulation is also used to compare the efficiencies of the statistics under different distributions. Some recommendations about when to use transformations and which one to choose are put forward associated with the conclusion drawn from the research work we have done.</p>
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Efficiency Comparison of Distribution-Free Transformations in the Straight-Line Regression Problem / 非参直线回归问题中不同变换方法的有效性比较Zhang, Ling January 2010 (has links)
In statistical inference of the distribution-free straight-line regression problem, two common transformations, rank transformation and sign transformation, are used to construct the test statistics. When shall we need to use the transformations and which transformation is more efficient are two common questions met by researchers. In this thesis, we will discuss the comparison of the efficiencies of the statistics before and after the rank transformation or the sign transformation in both theoretical and practical ways. Simulation is also used to compare the efficiencies of the statistics under different distributions. Some recommendations about when to use transformations and which one to choose are put forward associated with the conclusion drawn from the research work we have done.
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