In the epoch of advanced mHealth (mobile health) use in ophthalmology, there is a scientific call for regulating the validity and reliability of eye-related apps. For a positive health outcome that works towards enhancing mobile-application guided diagnosis in joint decision-making between eye specialists and individuals, the aging population should be provided with a reliable and valid tool for assessment of their eye status outside the physician office. This interdisciplinary study aims to determine through hypothesis testing validity and reliability of a limited set of five mHealth apps (mHAs ) and through binary logistic regression the prediction possibilities of investigated apps to exclude the four major eye diseases in the particular demographic population. The study showed that 189 aging adults (45- 86 years old) who did complete the mHAs’ tests were able to produce reliable results of selected eye function tests through four out of five mHAs measuring visual acuity, contrast sensitivity, red desaturation, visual field and Amsler grid in comparison with a “gold standard” - comprehensive eye examination. Also, part of the participants was surveyed for assessing the Quality of Experience on mobile apps. Understanding of current reliability of existing eye-related mHAs will lead to the creation of ideal mobile application’ self-assessment protocol predicting the timely need for clinical assessment and treatment of age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Detecting the level of eye function impairments by mHAs is cost-effective and can contribute to research methodology in eye diseases’ prediction by expanding the system of clear criteria specially created for mobile applications and provide returning significant value in preventive ophthalmology.

Background: US adolescent obesity rates have quadrupled over the past 3 decades. Research examining complex factors associatedwith obesity is limited.Objectives: The purpose of this study was to utilize a representative sample of students (grades 6 - 8) in Tennessee to determine theco-occurrence of risk behaviors with adolescent obesity prevalence and to analyze variations by strata. Methods: The 2010 youth risk behavior survey dataset was used to examine associations of obesity with variables related to sampledemographics, risk and protective behaviors, and region. Hierarchical logistic regression analyses stratified by demographics andregion were conducted to evaluate variation in obesity risk occurring on three hierarchical levels: class, school and district. Results: The sample consisted of 60715 subjects. The overall obesity rate was 22%. High prevalence of obesity existed in males, non-white race, those ever smoked and was positively correlated with age. Across three state regions, race, gender, and specific behaviors (smoking, weight misperception, disordered eating, +3 hours TV viewing, and no sports team participation) persisted as significantpredictors of adolescent obesity, although variations by region and demographics were observed. Multilevel analyses indicate that< 1%, 0 - 1.97% and4.03 - 13.06% of the variation in obesity was associated with district, school and class differences, respectively, whenstratifying the sample by demographic characteristics or region. Conclusions: Uniform school-based prevention efforts targeting adolescent obesity risk may have limited impact if they fail torespond to geographical and demographic nuances that hierarchal modeling can detect. Study results reveal that stratified hi-erarchical analytic approaches to examine adolescent obesity risk have tremendous potential to elucidate significant prevention insights.

Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for Google’s revenue. There are broadly two types of attribution models- data-driven and heuristics.This thesis focuses on the data driven attribution modeland explores the viability of using Bayesian Network as market attribution models andbenchmarks the performance against a logistic regression. The data used in this thesiswas prepossessed using undersampling technique. Furthermore, multiple techniques andalgorithms to learn and train Bayesian Network are explored and evaluated.For the given dataset, it was found that Bayesian Network can be used for market at-tribution modeling and that its performance is better than the baseline logistic model. Keywords: Market Attribution Model, Bayesian Network, Logistic Regression.

Habitat loss and fragmentation are major threats to biodiversity. Geostatistical methods, especially kriging, are widely used in ecology. Bird counts data often fail to show normal distribution over an area which is required for most of the kriging methods. Hence choosing an interpolation method without understanding the implications may lead to bias results. United Kingdom’s Exprodat Consulting Ltd had set an Exploratory Spatial Data Analysis (ESDA) workflow for optimising interpolation of petroleum dataset. This workflow was applied in this study to predict capercaillie bird species over whole Sweden. There was no trend found in the dataset. Also the dataset was not spatially auto-correlated. A completely regularized spline surface model was created with RMSE 1.336. Medium to high occurrences (8-16) were found over two very small areas, within Västerbottens county and Västra Götlands county. Low occurrences (1-3) were found all over Sweden. Urban areas like Stockholm city and Malmö city had low occurrences. Another kriging prediction surface was created with RMSE 1.314 to compare the results. There were no prediction values from 5 to 16 in kriging surface. In-depth studies were carried out by selecting three areas. The studies showed that the results of local kriging surfaces did not match with the results of global surface. Uncertainty in GIS may exist at any level. Having low RMSE value does not always mean a good result. Hence ESDA before choosing interpolation method is an effective way. And a post result field investigation could make it more valid. Regression analysis is also widely used in ecology and there are certain different methods that are available to be used. Ordinary Least Squares is the first method that was tested upon bird counts data set. Adjusted R-squared value was 0.008616 which indicated that explanatory variables pine, spruce, roads, urban areas and wetlands were just contributing to 0.8% to the dependent variable bird counts. It was also found that there was no linear relationship between dependent and explanatory variables. Logistic regression was the next step as it had the capability to work with nonlinear data also. The Spatial Data Modeller (SDM) tool was used to perform logistic regression in ArcGIS 9.3. Initially results of logistic regression were unexpected, hence focal statistics was performed upon all the independent variables. Logistic regression with these new independent variables generated meaningful results. This time the probability of occurrence of birds had weak positive relationship with all the independent variables. Coefficients of pine, spruce, roads, urban areas and wetlands were found to be 0.39, 0.23, 0.13, 0.24 and 0.14 respectively. Pine and spruce are natural attractors for birds, hence results were quite acceptable. But the overall model performance remained poor. Positive coefficient for roads, urban areas and wetlands may well be due to redundancy in these datasets or observer bias in bird species reporting. IDRISI Andes also came up with almost the same results when logistic regression with same dependent and independent variables was performed. IDRISI Andes output contained the pseudo R-square value, found to be 0.0416. This was an indication of biasness in the dataset also. The results of in-depth studies by selecting three areas also showed that LR with focal statistics were having better results than LR without focal statistics, but the overall performance remained poor. The SDM tool is a good choice for performing logistic regression on small scale datasets due to its limitation. Comparison of results between the two geostatistical methods, interpolation and regression depicts the similarity at discrete places; an unbiased dataset might have resulted in a better comparison of two methods.

Electric Vehicles is regarded as an important solution for emission reduction. But, the adoption to it is still a problem in many countries. With survey data containing demographic and attitude factors of respondents, this paper proposes two classification models: logistic regression and random forest using the Multiple Correspondence Analysis (MCA) as an intermediate step to identify the factors affecting the willingness of electric vehicles purchase. The analysis shows that the addition of MCA does enhance the explanatory power while it takes a low cost on prediction performance, and the results reveal that characteristics such as frequency of using modern transport services, car-sharing subscription, living place, mode of frequent trip do have a significant impact on EV purchases.

The aim of this thesis is to analyze factors responsible for onset of ethnic conflicts and selected characteristics. By comparing to non-ethnic conflicts it was determined, if there are any differences in onset mechanisms of these two types of conflicts and thus if there is a space for explanatory role of ethnicity as a cause of ethnic conflicts. Selection of examined factors is congruent with the relevant literature and existing analyses and reflects the context of contemporary conflict research. The influence of male unemployment rate, level of Human development index and its inequality-adjusted version, human rights and finally the influence of conflicts in neighbouring countries on the onset of conflict is tested by statistical methods in component analyses. Also the intensity of ethnic and non-ethnic conflicts, war years and HDI are also compared. The comparative style of the research helps to understand the true nature of causes of intrastate conflicts and indicates, that there is no difference between the two types. Empirical character of this thesis is also the reason for assessing it within the context of other quantitative studies of conflict, comparing the results and defining the proper level of analysis for reaching tangible contributions.

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene–steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P < 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10−3); while the next best signal was rs951613 (P = 7.46 × 10−3). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene–steroid interaction effects (OR = 2.18, 95% CI = 1.31−3.63 with P = 2.9 × 10−3 for rs6532496 and OR = 2.07, 95% CI = 1.24 −3.45 with P = 5.43 × 10−3 for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR = 2.26, 95% CI = 1.2 −3.38 for rs6532496 and OR = 2.14, 95% CI = 1.14 −3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene–steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene–steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene–steroid interaction effect (OR = 2.49, 95% CI = 1.5 −4.13 with P = 4.0 × 10−4 based on the classic logistic regression and OR = 2.59, 95% CI = 1.4 −3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Background: Oral health disparities exist in the USA. However, little is known of the relationship between oral health disparity and citizenship. The aims of this study were: (i) to describe the differences in self-rated oral health (SROH) between adult American citizens and non-citizens (>20 years of age); and (ii) to test whether factors such as frequency of dentist visits and socio-economic status (SES) are differently associated with SROH in these two groups. Methods: The data used in this study were drawn from the National Health and Nutrition Examination Survey conducted in 2011–2012. Weighted logistic regression models were used to detect the strengths of the association between a series of predictors and SROH. Results: More non-citizens (59.54%) than their citizen peers (26.24%) rated their oral health as fair/bad. All factors analysed in this study were differently associated with SROH based on citizenship. More specifically, natural characteristics, such as ethnicity and age, were significantly associated with SROH among non-citizens, and SES was significantly associated with American citizens. Among non-citizens, Hispanic, Non-Hispanic Black and Asian subjects were more likely than Non-Hispanic White subjects to report their oral health as being ‘good’. Family poverty level, education and the frequency of dentist visits were significantly associated with SROH among citizens. Conclusion: The findings of this study indicate that American immigrants report their oral health across most dimensions as being worse than do American citizens. Each explanatory factor may have a different strength of association with SROH in immigrants and citizens, which implies that different steps should be taken within these groups to reduce disparities in oral health.

This article examines predictors of the future market value of microcap pharmaceutical companies. This is problematic since the large majority of these firms seldom report positive net income. Their value comes from the potential of a liquidity event such as occurs when a key drug is approved by the FDA. The typical scenario is one in which the company is either acquired by a larger pharmaceutical firm or enters into a joint venture with another pharmaceutical firm. Binary logistic regression is used to determine the impact of the firm's drug treatment pipeline and its investment in research and development on the firm's market cap. Using annual financial data from 2007 through 2010, this study finds that the status of the firm's drug treatment pipeline and its research and development expenses are significant predictors of the firm's future stock value relative to other microcap pharmaceutical firms.

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.