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The thesis deals with the comparison of conventional and heuristic methods in data mining used for binary classification. In the theoretical part, four different models are described. Model classification is demonstrated on simple examples. In the practical part, models are compared on real data. This part also consists of data cleaning, outliers removal, two different transformations and dimension reduction. In the last part methods used to quality testing of models are described.

The presented thesis deals with the issue of statistical quality control of a specific production process. The thesis presents a range of statistical tools that can be used to identify the factors causing a high proportion of non-standard pieces. The diploma thesis practically introduces the application of these quality management tools to the production process characterized by an increased proportion of non-standard pieces, in which the factors causing their occurrence are unknown, and as following the thesis in detail introduces the approach how to detect these factors. The last part of the work summarizes the recommendations handed over to the company in order to verify the conclusions of the thesis.

The purpose of this study was to analyze the current and pre-service conditions that can affect student teachers' preparedness to pass the science portion of the EC-6 Texas Examinations for Educator Standards (TExES), one of the mandatory certification exam to become a teacher in Texas. Two types of prediction models were employed in this study: binomial logistic regression and multiple linear regression. The independent variables used in this study were: final grade in BIOL 1082, classification of students, transfer status, taken college biology, taken college chemistry, taken college physics, taken college environmental science, taken college earth science, attending college part-time, number of credits taken during the semester, first-generation college student, relatives with degree in education, and current GPA. The dependent variable of this study was the posttest score on science portion of the EC-6 TExES practice exam. A total of 170 preservice teachers participated this study. This study used students enrolled in BIOL 1082, who volunteered to take a Biology for Educators QualtricsTM survey and the EC-6 TExES practice exam in a pretest (start of semester) and posttest (end of semester) form. The findings of this study revealed that the single best predictor of preservice teachers' performance on the science portion of EC-6 TExES practice certification examination was the Grade in BIOL 1082.

abstract: Longitudinal studies contain correlated data due to the repeated measurements on the same subject. The changing values of the time-dependent covariates and their association with the outcomes presents another source of correlation. Most methods used to analyze longitudinal data average the effects of time-dependent covariates on outcomes over time and provide a single regression coefficient per time-dependent covariate. This denies researchers the opportunity to follow the changing impact of time-dependent covariates on the outcomes. This dissertation addresses such issue through the use of partitioned regression coefficients in three different papers. In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database. The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study. The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

Please read the abstract (Summary) in the 00front part of this document / Dissertation (MA (HMS))--University of Pretoria, 2002. / Biokinetics, Sport and Leisure Sciences / unrestricted

This cross-sectional, exploratory study aimed to determine the prevalence and characteristics of self-harming behaviours among a sample of South African university students (N = 603), as well as the relationship between deliberate self-harm (DSH) and symptoms of depression and anxiety. A battery of instruments, including the Beck Depression Inventory (BDI-II), State-Trait Anxiety Inventory (STAI), and Deliberate Self-Harm Inventory (DSHI) was administered to participants. Data were analysed by means of descriptive statistics, Chi Square tests, t-tests, and logistic regression analyses. The findings suggest high rates of DSH among the sample (46% lifetime prevalence; 36% 12-month prevalence). No significant gender differences were found in the rates of DSH. Participants from the combined Asian and Coloured racial group reported significantly higher rates of DSH than both White and Black participants. Participants aged 20-21 were significantly more likely to report DSH than those in other age groups. Overall, depression scores in the sample fell within the normal range (M = 15.79), while anxiety scores were found to be exceptionally high (state anxiety: M = 46.56; trait anxiety: M = 48.72). The findings suggest that participants with elevated levels of depression are significantly more likely to report DSH. A significant, negative relationship was found between DSH and state anxiety, while a positive yet insignificant relationship was found between DSH and trait anxiety. The findings of this exploratory study partially support the findings of international research investigating the relationship between DSH and depression and anxiety, but warrant further exploration in order to better understand the complexities of these relationships, particularly in the South African context. / Mini-Dissertation (MA)--University of Pretoria, 2014. / tm2015 / Psychology / MA / Unrestricted

Background: Investigators are often interested in using population-level health administrative data in inflammatory bowel disease (IBD) patients to study disease outcomes, risk factors and treatment effects to enhance knowledge, shape clinical practice and influence health care policy. A major limitation of using health administrative data for these purposes is the lack of detailed clinical data to adjust for the confounding effects of differential disease severity on observed associations. Methods to account for disease severity using administrative variables would offer a major advance to population-level studies in IBD patients. Thus, in this study we aimed to use a cohort of IBD patients from The Ottawa Hospital (TOH) to validate a model that was originally developed in Manitoba for estimating clinical disease course in IBD patients through healthcare utilization measures. Objectives: The objectives of this thesis are: 1) To identify and characterize a reference cohort of IBD patients in the ambulatory clinics of four gastroenterologists from TOH on clinical disease course in the preceding year (reference cohort), based on a Manitoba definition of clinical disease course; 2) To fit a partial proportional odds (PPO) model for predicting IBD course, derived using Manitoba health administrative data, to the reference cohort of IBD patients using Ontario health administrative data; 3) To derive new PPO models of IBD disease course for the reference cohort using Ontario administrative variables and compare model performance; and 4) To apply the models to the Ontario Crohn’s and Colitis cohort (OCCC) to estimate IBD course in Ontario, and compare the distribution to that of the Manitoba IBD population.Methods: We first identified a reference cohort of IBD patients in Ontario from the outpatient clinics at TOH during fiscal year 2015. Through chart review, we classified these patients into one of four clinical disease categories (remission, mild, moderate, or severe) using the Manitoba definition. We linked these patients to Ontario health administrative datasets. Given slight differences in data structure and coding between Manitoba and Ontario, we were unable to directly test the Manitoba model and instead fit a PPO model to the Ontario cohort using analogous administrative variables to those used in the final Manitoba model (“adapted model”). We subsequently derived new PPO models using unique Ontario administrative variables under three strategies: 1) Stepwise variable selection (“stepwise model”); 2) Forced fitting of all variables (“all-variables model”); and 3) Using a two-step modelling algorithm that considered IBD-related hospitalizations separate from other administrative variables (“two-step model”). We then compared model performance from the four strategies. Finally, we applied the models to the Ontario IBD population from 2004 to 2016 and compared model estimates to those from Manitoba. Results: We identified 963 patients with IBD from TOH outpatient clinics, of which 52.3% (n=504) were males, 64.6% (n=622) had Crohn's Disease, and 89.2% (n=859) resided in an urban setting. Based on the Manitoba definition, 64.9% of patients within our reference cohort were classified as remission, while 11.4%, 14.1%, and 9.6% were classified as mild, moderate, and severe disease course, respectively. The adapted model (c-statistic 0.77, goodness-fit p-value 0.28) performed comparably to the other models: the stepwise model (c-statistic 0.77, goodness-fit p-value 0.50), the all-variables model (c-statistic 0.77, goodness-fit p-value 0.53), and the two-step model (c-statistic 0.78, goodness-fit p-value 0.75). The adapted model also resulted in overall similar estimates with regards to the disease course distribution among the Ontario IBD population. However, on closer inspection, our two-step model, in which individuals who had been hospitalized for an IBD-related indication within the past year were assumed to have severe disease, performed better with respect to accurately classifying individuals with moderate or severe disease, without sacrificing discriminative ability. Based on the two-step model, from 2004 to 2016, 89.2-91.2% of the Ontario IBD population was in remission, 0% had mild disease, 2.4-3.2% had moderate disease, and 5.9-8.4% had severe disease. Distribution of disease course among IBD patients in Ontario differed considerably than that in Manitoba. Conclusion: In the absence of clinical information within health administrative data, we present and compare four different models that can be used to partially account for the confounding effect of disease course among IBD patients in future population-based studies using Ontario health administrative data. Given that our models did not perform as originally expected, especially with regards to accurately identifying individuals with more active disease states, we advise researchers to use these models at their own discretion.

Diploma thesis "Mediation Analysis in Sociology" deals with mediation analysis and possibilities of its application in sociology, depending on the type of the dependent variable that enters the analysis. In the first case the dependent variable is continuous - in this case the SPSS software and its PROCESS add-on are used to directly analyse the mediation. In the second case the dependent variable that enters the analysis is binary - the PROCESS add-on doesn't allow this option; therefore, the analysis is performed in SPSS software by the set of linear and logistic regressions according to the Baron & Kenny method. Two case studies from the field of sociology, GSS (General Social Survey) and ISSP (International Social Survey Programme), are used in the thesis and the consequences of the transition from continuous dependent variable to binary are examined using the secondary analysis of these data.

Year after years, the amount of data that we continuously generate is increasing. When this situation started the main challenge was to find a way to store the huge quantity of information. Nowadays, with the increasing availability of storage facilities, this problem is solved but it gives us a new issue to deal with: find tools that allow us to learn from this large data sets. In this thesis, a framework for Bayesian learning with the ability to scale to large data sets is studied. We present the Stochastic Gradient Langevin Dynamics (SGLD) framework and show that in some cases its approximation of the posterior distribution is quite poor. A reason for this can be that SGLD estimates the gradient of the log-likelihood with a high variability due to naïve sampling. Our approach combines accurate proxies for the gradient of the log-likelihood with SGLD. We show that it produces better results in terms of convergence to the correct posterior distribution than the standard SGLD, since accurate proxies dramatically reduce the variance of the gradient estimator. Moreover, we demonstrate that this approach is more efficient than the standard Markov Chain Monte Carlo (MCMC) method and that it exceeds other techniques of variance reduction proposed in the literature such as SAGA-LD algorithm. This approach also uses control variates to improve SGLD so that it is straightforward the comparison with our approach. We apply the method to the Logistic Regression model.