Thesis (Ph.D.)--York University, 2004. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 182-192). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ99160

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Visual analytics; Visual knowledge discovery. Includes bibliographical references (leaves 79-83).

Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 190-201).

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Nov. 10, 2009). Includes bibliographical references (p. 48-50).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Physical-chemical water quality data from lotic water monitoring sites across Canada were integrated into one dataset. Two overlapping matrices of data were analyzed with principal component analysis (PCA) and cluster analysis to uncover structure and patterns in the data. The first matrix (Matrix A) had 107 sites located throughout Canada, and the following water quality parameters: pH, specific conductance (SC), and total phosphorus (TP). The second matrix (Matrix B) included more variables: calcium (Ca), chloride (Cl), total alkalinity (T_ALK), dissolved oxygen (DO), water temperature (WT), pH, SC and TP; for a subset of 42 sites. Landscape characteristics were calculated for each water quality monitoring site and their importance in explaining water quality data was examined through redundancy analysis. The first principal components in the analyses of Matrix A and B were most correlated with SC, suggesting this parameter is the most representative of water quality variance at the scale of Canada. Overlaying cluster analysis results on PCA information proved an excellent mean to identify the major water characteristics defining each group; mapping cluster analysis group membership provided information on their spatial distribution and was found informative with regards to the probable environmental influences on each group. Redundancy analyses produced significant predictive models of water quality demonstrating that landscape characteristics are determinant factors in water quality at the country scale. The proportion of cropland and the mean annual total precipitation in the drainage area were the landscape variables with the most variance explained. Assembling a consistent dataset of water quality data from monitoring locations throughout Canada proved difficult due to the unevenness of the monitoring programs in place. It is therefore recommended that a standard for the monitoring of a minimum core set of water quality variable be implemented throughout the country to support future nation-wide analysis of water quality data.

Three graphical methods, similar to Popov's approach, are derived to test a stability criterion for a general class of multivariable nonlinear feedback systems. Various simplifications of these methods are then derived for four particular classes of system: the series system, the parallel system, the internal feedback system, and the symmetric system. These stability tests may be applied to active networks that consist of passive linear time-invariant elements and many nonlinear controlled sources. It is further shown that certain special network structures may be represented as one of the four particular classes of system. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

The utility of multivariate analysis methods for the qualitative and quantitative analysis of complex ICP optical emission spectra is shown. A photodiode array is used to collect data which are simplified to a low resolution (approx. 4 A) spectrum for a number of knowns and unknowns. The digitized spectra thus obtained are plotted as points in multivariate space (pattern space). The spectra of different elements appear in well defined regions of pattern space. Qualitative analysis is accomplished by methods of cluster analysis and factor analysis. Quantitative analysis is carried out using multidimensional working curves. The pattern space coordinate system is transformed to give axes corresponding directly to elemental concentrations, allowing direct simultaneous determination of elemental composition. The effect of line intensities, detector dynamic range, and matrix effects are also discussed. / Science, Faculty of / Chemistry, Department of / Graduate

A wide array of procedures have been proposed for testing correlation pattern. Many, but not all, of the statistical techniques available for testing correlation pattern are derived under the distributional condition of multivariate normality which does not always hold in the behavioral, educational and social sciences. Though a number of studies have explored the performance of structure analysis techniques under conditions of multivariate nonnormality, very little is known about the actual performance of many correlation structure analysis techniques under conditions of multivariate nonnormality. In addition, very little is known about the actual concurrent performance of tests of multivariate normality. The present investigation ascertains how tests of correlation pattern hypotheses and indicators of multivariate normality perform when data are from multivariate normal or nonnormal parent populations. This paper reviews and examines, using a Monte Carlo simulation study, the concurrent performance of different approaches to testing (1) correlation pattern hypotheses, including, (i) normal theory (NT) and asymptotically distribution free (ADF) covariance structure analysis techniques, (ii) NT and ADF correlation structure analysis techniques, (iii) correlation pattern specific techniques; (2) the distributional assumption of multivariate normality using statistics based on Mardia's measures of multivariate skewness and kurtosis. This paper also examines the performance characteristics of test procedures based on joint consideration of tests of multivariate normality and structure analysis techniques. Performance of the covariance and correlation structure analysis techniques, tests of multivariate normality, and joint test procedures was assessed across different types of correlation pattern models, numbers of variables, levels of skew and kurtosis, sample sizes, and nominal alpha levels, on the primary Neyman-Pearson criterion for an optimal test, according to which an optimal procedure (1) controls experimentwise Type I error rate at or below the nominal level, (2) maximizes power. / Arts, Faculty of / Psychology, Department of / Graduate

Please read the abstract in the section, 00front, of this document / Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2005. / Mathematics and Applied Mathematics / unrestricted