Thesis (M. S.)--Public Policy, Georgia Institute of Technology, 2007. / Jan Youtie, Committee Member ; David Sawicki, Committee Member ; Phil Shapira, Committee Chair.

Theses (Ed. D.)--Marshall University, 2005. / Title from document title page. Includes abstract. Document formatted into pages: contains ix, 180 p. Bibliography: p. 121-129.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Daniel L. McGee, Sr., Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept. 21, 2006). Document formatted into pages; contains xii, 147 pages. Includes bibliographical references.

Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 199-204).

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 121-128).

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For many young individuals in their junior year of high school the pressures of getting into the desired secondary education institution of their choice is a nerve-wrecking task. For months they prepare to study for standardized tests and compile their greatest achievements to prove they are worthy enough to be accepted in to these prestigious universities. However, preparation for college starts way before the application season. It leads one to wonder what influential factors surrounding them could affect their odds of being successful in college once they are accepted. This study examines the influential factors that effect a student’s college readiness. The factors tested will be student’s parent income, total enrollment of the high school, total number of high school days in a year, average class size in the high school, and the teacher quality of that high school. A multiple regression will be used to test these independent variables against the high school graduate ready for college percentage for each high school. The slope parameters of the model will be tested through t-tests, p-values, and f-tests. The sample size will consist of Illinois High Schools who have completed an Illinois High School Report Card required by the No Child Left Behind law. In addition, a ten question survey will be dispersed to a population of fifty college students at SIU focusing on factors they believe have been influential on their college success. This study will aim to improve the understanding of all the factors that go into equipping high school students for a milestone that can ultimately affect their economic outcomes in life.

In this thesis, we investigate model checking problems for parametric single-index regression models when the variables are measured with different types of errors. The large sample behaviours of the test statistics can be used to develop properly centered and scaled model checking procedures. In addition, a dimension reduction model-adaptive strategy is employed, with the special requirements for the models with measurement errors, to improve the proposed testing procedures. This makes the test statistics converge to their weak limit under the null hypothesis with the convergence rates not depending on the dimension of predictor vector. Furthermore, the proposed tests behave like a classical local smoothing test with only one-dimensional predictor. Therefore the proposed methods have potential for alleviating the difficulties associated with high dimensionality in hypothesis testing.. Chapter 2 provides some tests for a parametric single-index regression model when predictors are measured with errors in an additive manner and validation dataset is available. The two proposed tests have consistency rates not depending on the dimension of predictor vector. One of these tests has a bias term that may become arbitrarily large with increasing sample size, but has smaller asymptotic variance. The other test is asymptotically unbiased with larger asymptotic variance. Both are still omnibus against general alternatives. Besides, a systematic study is conducted to give an insight on the effect of the ratio between the size of primary data and the size of validation data on the asymptotic behavior of these tests. Simulation studies are carried out to examine the finite-sample performances of the proposed tests. Also the tests are applied to a real data set about breast cancer with validation data obtained from a nutrition study.. Chapter 3 introduces a minimum projected-distance test for a parametric single-index regression model when predictors are measured with Berkson type errors. The distribution of the measurement error is assumed to be known up to several parameters. This test is constructed by combining the minimum distance test with a dimension reduction model-adaptive strategy. After properly centering, the minimum projected-distance test statistic is asymptotically normal at a convergence rate of order nh^(1/2) and can detect a sequence of local alternatives distinct from the null model with a rate of order n^(-1/2) h^(-1/4) where n is the sample size and h is a sequence of bandwidths tending to 0 as n tends infinity. These rates do not depend on the dimensionality of predictor vector, which implies that the proposed test has potential for alleviating the curse of dimensionality in hypothesis testing in this field. Further, as the test is asymptotically biased, two bias-correction methods are suggested to construct asymptotically unbiased tests. In addition, we discuss some details in the implementation of the proposed tests and then provide a simplified procedure. Simulations indicate desirable finite-sample performances of the tests. Besides, we illustrate the proposed model checking procedures by using two real datasets to illustrate the effects of air pollution on Emphysema.. Chapter 4 provides a nonparametric test for checking a parametric single-index regression model when predictor vector and response are measured with distortion errors. We estimate the true values of response and predictor, and then plug the estimated values into a test statistic to develop a model checking procedure. The dimension reduction model-adaptive strategy is also employed to improve its theoretical properties and finite sample performance. Another interesting observation in this work is that, with properly selected bandwidths and kernel functions in a limited range, the proposed test statistic has the same limiting distribution as that under the classical regression setup without distortion measurement errors. Simulation studies are conducted.

The real property tax has a major impact on real property owners in all Canadian municipalities. As with all systems of taxation it is important that the burden of this tax be distributed fairly and equitably. Legislators have attempted to ensure equitable treatment among real property owners by requiring that the basis of assessment should be 'actual value'. However, due to the large numbers of properties to be valued, assessors have not been able to use the market approach to value, a valuation technique known to produce 'actual values'. Rather, they have resorted to the more subjective cost approach to value. While the mechanics of the cost approach lend themselves to the mass valuation problem, they rarely produce values that can be equated with actual market values. The application of multiple regression analysis is presented as a solution to this valuation problem. Multiple regression analysis enables the assessor to produce objectively the 'actual value' of all single family homes in a municipality. After presenting multiple regression analysis as a modern application of the market approach to value, the applicability of this valuation technique is tested on actual sales data. A sample of approximately four hundred recently sold single family homes is subjected to valuation by multiple regression analysis. Various experiments, including means of stratifying the data are presented in an attempt to produce high standards of solution. While the statistical results of the experiment are not of sufficient calibre for practical assessment purposes, they do reveal how continued experimentation can improve the applicability of this valuation technique to mass appraisal. Multiple regression analysis is the assessor's tool of the future. It facilitates the application of a valuation technique that will permit the assessor to meet his statutory obligation while still allowing him to adhere to sound appraisal methodology. / Business, Sauder School of / Graduate

Bibliography: p. 178-182. / The purpose of this thesis is to provide a study of the linear model. The whole work has been split into 6 chapters. In Chapter 1 we define and examine the two linear models, i.e. the regression and the correlation model. More specifically we show that the regression model is the conditional version of the correlation model. In Chapter 2 we deal with the problem of multicollinearity. We investigate the sources of near singularities, we give some methods of detecting the multicollinearity, and we state briefly methods for overcoming this problem. In Chapter 3 we consider the least squares method with restrictions, and we dispose of some tests for testing the linear restrictions. The theory concerning the sign of least squares estimates is discussed, then we deal with the method for augmenting existing data. Chapter 4 is mainly devoted to ridge regression. We state methods for selecting the best estimate for k. Some extensions are given dealing with the shrinkage estimators and the linear transforms of the least squares. In Chapter 5 we deal with the principal components, and we give methods for selecting the best subset of principal components. Much attention was given to a method called fractional rank and latent root regression analysis. In Chapter 6 comparisons were performed between estimators previously mentioned. Finally the conclusions are stated.