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The aim of this study was to explore how match-related statistics contribute to winning association football matches. This is relevant for stakeholders in the football industry to facilitate the understanding of what factors contribute to winning matches and can thus be of use when formulating match tactics. A model was constructed through the use of binary logistic regression, where winning/not winning was used as the response variable and standardized match-related statistics were used as predictor variables. Using the acquired coefficients, it was concluded that, among other variables, the home advantage and the ability of a team to finish on target has a strong correlation with winninggames. Further, the study explores the impact of a team’s ability to win football games on the financial landscape of the modern football world. The results show that some of the examined statistics are well correlated to winning a match, but that the tactical useability of these insights is low. / Syftet med denna studie var att utforska hur matchrelaterad statistik bidrar till att vinna fotbollsmatcher. Detta är relevant för aktörer inom fotbollsbranchen för att underlätta försåelsen av vilka matchrelaterade faktorer som bidrar till vinst och kan således användas för att forma matchtaktik. En modell konstruerades genom binär logistisk regression, där att vinna/att inte vinna användes som responsvariabel och standardiserad matchrelaterad statistik användes som prediktorvariabel. Genom att använda koefficienterna tillhörande modellen,kan man fastslå att bland annat hemmalagsfördel samt ett lags förmåga att träffa mål korrelerar starkt med att vinna matcher. Dessutom utforskar studien påverkan av ett lags förmåga att vinna fotbollsmatcher på det finansiella landskapet tillhörande den moderna fotbollen. Resultaten visar att vissa av de studerade variablerna korrelerar starkt med att vinna fotbollsmatcher, men attmöjligheterna att använda dessa insikter till att forma taktik är begränsade.

В рамках исследования были проанализированы данные о кредитных операциях, предоставленные коммерческими банками. Была проведена подробная предобработка и нормализация данных для подготовки их к дальнейшему анализу и использованию в моделях машинного обучения. Основной фокус работы был сосредоточен на применении двух моделей: логистической регрессии и случайного леса. Логистическая регрессия была выбрана из-за своей простоты и интерпретируемости, а случайный лес – из-за своей способности обрабатывать большие объемы данных и выявлять сложные зависимости. В ходе экспериментов было показано, что обе модели успешно справляются с задачей оценки кредитного риска. Логистическая регрессия показала хорошую производительность, быстроту и точность, что делает ее подходящей для применения в реальном времени, например, при личной подаче заявки в банке или при онлайн-заявках. Случайный лес, в свою очередь, достиг высокой точности, хотя требует больше вычислительных ресурсов. Дополнительно, в работе был использован метод генетического программирования для создания новых признаков на основе исходных данных. Этот подход позволил значительно улучшить производительность модели и повысить ее точность. Хотя не все созданные признаки вошли в топ-5 наиболее важных, генетическое программирование оказалось эффективным способом генерации признаков, что имеет важное значение в области оценки кредитного риска. / The study analyzed data on credit transactions provided by commercial banks. Detailed pre-processing and normalization of the data was carried out to prepare it for further analysis and use in machine learning models. The main focus of the work was on the use of two models: logistic regression and random forest. Logistic regression was chosen for its simplicity and interpretability, and random forest for its ability to handle large amounts of data and identify complex relationships. During the experiments, it was shown that both models successfully cope with the task of assessing credit risk. Logistic regression has demonstrated good performance, speed, and accuracy, making it suitable for real-time applications such as in-person applications at a bank or online applications. Random forest, in turn, has achieved high accuracy, although it requires more computing resources. Additionally, the work used the genetic programming method to create new traits based on the original data. This approach significantly improved the model's performance and accuracy. Although not all of the features generated were in the top 5 most important, genetic programming has proven to be an effective way to generate features, which has important implications in the field of credit risk assessment.

Credit modelling has traditionally been done by credit institutes based on financial data about the individuals requesting the credit. While this has been sufficient in lowering risk in developed economies with plenty of financial data it is inefficient in developing economies and fails to reach the unbanked population. As this is both limiting many responsible consumers from getting access to credit as well as limiting companies from reaching paying customers, it is evident that new strategies for credit modelling are needed. This paper explores the usage of behavioural data for credit modelling gathered from users of Klarna’s app. The models are based on the machine learning algorithms logistic regression, random forests, neural networks, and gradient boosted decision trees. In this study, models were trained on Swedish data in multiple timespans and tested in different timespans and countries. The results show that modelling on the data points developed in this study is effective and suggest that in certain cases be used in predicting new and unknown markets by training on similar markets. / Kreditvärderingar har traditionellt sätt utförts av kreditinstitut baserat på existerande finansiella data kring personen i fråga som ansöker om kredit. Denna metod har varit framgångsrik i att minimera risk inom utvecklade ekonomier där finansiella data har varit tillgänglig. Metoden har varit mindre framgångsrik i utvecklingsekonomier och misslyckas att utvärdera befolkningar som saknar finansiella tjänster. Då detta problem begränsar många pålitliga konsumenter att få tillgång till kredit och samtidigt begränsar företagen att nå ut till möjliga betalande kunder, blir det viktigt att ta fram nya strategier för att utvärdera kredit. Denna uppsats utforskar möjligheten att modellera kreditvärdighet baserat på användarbeteende med hjälp av data från Klarnas shopping app. Modellerna är baserade på maskininlärningsalgoritmerna logistisk regression, Random Forests, neurala nätverk och gradient boosted decision trees. I denna studie tränas modellerna på olika tidsspann inom den svenska marknaden och testas på olika tidsspann och marknader. Resultaten från studien visar att det går med hjälp av beteende data från Klarnas app att, under olika omständigheter, förutspå kreditvärdighet i framtiden och på olika marknader.

This dissertation illustrates how to detect the rater centrality effect in a simulation study that approximates data collected in large scale performance assessment settings. It addresses three research questions that: (1) which of several centrality-detection indices are most sensitive to the difference between effect raters and non-effect raters; (2) how accurate (and inaccurate), in terms of Type I error rate and statistical power, each centrality-detection index is in flagging effect raters; and (3) how the features of the data collection design (i.e., the independent variables including the level of centrality strength, the double-scoring rate, and the number of raters and ratees) influence the accuracy of rater classifications by these centrality-detection indices. The results reveal that the measure-residual correlation, the expected-residual correlation, and the standardized deviation of assigned scores perform better than the point-measure correlation. The mean-square fit statistics, traditionally viewed as potential indicators of rater centrality, perform poorly in terms of differentiating central raters from normal raters. Along with the rater slope index, the mean-square fit statistics did not appear to be sensitive to the rater centrality effect. All of these indices provided reasonable protection against Type I errors when all responses were double scored, and that higher statistical power was achieved when responses were 100% double scored in comparison to only 10% being double scored. With a consideration on balancing both Type I error and statistical power, I recommend the measure-residual correlation and the expected-residual correlation for detecting the centrality effect. I suggest using the point-measure correlation only when responses are 100% double scored. The four parameters evaluated in the experimental simulations had different impact on the accuracy of rater classification. The results show that improving the classification accuracy for non-effect raters may come at a cost of reducing the classification accuracy for effect raters. Some simple guidelines for the expected impact of classification accuracy when a higher-order interaction exists summarized from the analyses offer a glimpse of the "pros" and "cons" in adjusting the magnitude of the parameters when we evaluate the impact of the four experimental parameters on the outcomes of rater classification. / Ph. D.

Knowledge of species occurrence is a prerequisite for efficient and effective conservation and management. Unfortunately, knowledge of species occurrence is usually insufficient, so models that use environmental predictors and species occurrence records are used to predict species occurrence. Predicting the occurrence of stream fishes is often difficult because sampling data insufficiently describe species occurrence and important environmental conditions and predictive models insufficiently describe relations between species and environmental conditions. This dissertation 1) examines the sufficiency of fish species occurrence records at four spatial extents in Virginia, 2) compares modeling methods for predicting stream fish occurrence, and 3) assesses relations between species traits and model prediction characteristics. The sufficiency of sampling is infrequently addressed at the large spatial extents at which many management and conservation actions take place. In the first chapter of this dissertation I examine factors that determine the sufficiency of sampling to describe stream fish species richness at four spatial extents across Virginia using sampling simulations. Few regions of Virginia are sufficiently sampled, portending difficulty in accurately predicting fish species occurrence in most regions. The sufficient number of samples is often large and varies among regions and spatial scales, but it can be substantially reduced by reducing errors of sampling omission and increasing the spatial coverage of samples. Many methods are used to predict species occurrence. In the second chapter of this dissertation I compare the accuracy of the predictions of occurrence of seven species in each of three regions using linear discriminant function, generalized linear, classification tree, and artificial neural network statistical models. I also assess the efficacy of stream classification methods for predicting species occurrence. No modeling method proved distinctly superior. Species occurrence data and predictor data quality and quantity limited the success of predictions of stream fish occurrence for all methods. How predictive models are built and applied may be more important than the statistical method used. The accuracy, generality (transferability), and resolution of predictions of species occurrence vary among species. The ability to anticipate and understand variation in prediction characteristics among species can facilitate the proper application of predictions of species occurrence. In the third chapter of this dissertation I describe some conservation implications of relations between predicted occurrence characteristics and species traits for fishes in the upper Tennessee River drainage. Usually weak relations and variation in the strength and direction of relations among families precludes the accurate prediction of predicted occurrence characteristics. Most predictions of species occurrence have insufficient accuracy and resolution to guide conservation decisions at fine spatial grains. Comparison of my results with alternative model predictions and the results of many models described in peer-reviewed journals suggests that this is a common problem. Predictions of species occurrence should be rigorously assessed and cautiously applied to conservation problems. Collectively, the three chapters of this dissertation demonstrate some important limitations of models that are used to predict species occurrence. Model predictions of species occurrence are often used in lieu of sufficient species occurrence data. However, regardless of the method used to predict species occurrence most predictions have relatively low accuracy, generality and resolution. Model predictions of species occurrence can facilitate management and conservation, but they should be rigorously assessed and applied cautiously. / Ph. D.

This dissertation examines the association between transformational leadership and public organizational performance using different data and methods. Each of the three articles collected in this dissertation contributes to the scholarship exploring the interplay of transformational leadership and organizational performance using distinct data and interpretive methods, examining the interplay of its elements and a widely used outcome measure – organizational performance as a dependent variable. The first article presents evidence of the empirical studies analyzing the association between transformational leadership and public organizational performance. Based on a formal, replicable search method informed by current practices for the systematic evaluation of published evidence, this review collects and analyzes empirical research articles examining the association between transformational leadership and public organizational performance. A total of seven journal articles were identified that met the study criteria. The measures of the transformational leadership construct in these studies are different but show a common pattern of survey questionnaires with multiple-level responses. Moreover, transformational leadership is found to be positively associated with public organizational performance. Organizational performance is operationalized using several factors such as development, growth, creativity, and effectiveness in the included studies. Acknowledging the gap in the included studies, this review offers two research questions for further exploration to address the gaps in the existing studies. The second article explores the association between the elements of transformational leadership and federal organizational performance in very large and large federal agencies using Ordinal Logistic Regression (OLR) on quantitative survey data drawn from results of the OPM Federal Employment Viewpoint Survey. Statistical findings of this study show a positive relationship between the elements of transformational leadership and organizational performance. This study contributes to the transformational leadership and public management literature by adding the scholarship exploring the association between the elements of transformational leadership and public organizational performance. The third article explores the association between transformational leadership and organizational performance from the experiences and perspectives of U.S. federal agency employees. The data was collected through semi-structured interviews with federal leaders from very large and large agencies, as per the Office of Personnel Management's classification. The perceptions of federal employees reflect the interplay between transformational leadership behaviors and perceptions of organizational performance. The informants' interviews enhance the understanding of transformational leadership-organizational performance association from the employee perspective. A process of thematic coding and interpretation is employed to analyze the interview data. The study findings contribute to the management and leadership literature by exploring the lived experiences of federal employees and their perceptions of the influence of transformational leadership on organizational performance. Moreover, the replicable qualitative interview process and analysis methodology create a pathway for future qualitative research. / Doctor of Philosophy / The influence of leadership is widely studied in terms of organizational performance. Among the leadership theories thought to influence organizational performance, transformational leadership is the most widely studied theory. The theory has gained much attention in the past 40 years for its contribution to many positive organizational outcomes, including meaningful work, organizational commitment, job satisfaction, and organizational performance. Despite a rich history of research in positive organizational outcomes, scholars still argue that there are scopes further to advance the empirical research on transformational leadership theory. Hence, the purpose of this dissertation is to address this claim and analyze the interplay between transformational leadership and public organizational performance. Study findings will contribute to the literature on transformational leadership by empirically examining the interplay of its elements and federal organizational performance. Moreover, the application of three distinct methods and interpretative data analysis will add new evidence to the research of public management and leadership.

The timber rattlesnake (Crotalus horridus) is a forest dwelling terrestrial pit viper that utilizes several types of habitat within the forest environment. One type of habitat crucial to the species' survival in mountainous regions and at more northern latitudes is basking habitat, which typically is an exposed rocky area used by gravid females for gestation, and by other timber rattlesnakes for shedding, mating, and digesting. Understanding the range of the timber rattlesnake in Virginia will enable biologists and land managers to better manage the landscape in a way conducive to the survival and persistence of timber rattlesnakes. To improve our ability to identify and locate areas potentially containing timber rattlesnake basking habitat, I used 5 landscape-level habitat variables with logistic regression and geographic information systems (GIS) to model and map areas of western Virginia potentially containing timber rattlesnake basking habitat. Models were ranked using Akaike's Information Criterion (AIC) and were crossvalidated using the methods of Fielding and Bell (1997). Aspect, slope, elevation, landform index, and percentage of forest cover values were derived using GIS for 217 known basking sites in western Virginia. I then used data derived for the 217 known basking sites to create 22 a priori models. The best model used the variables of aspect, slope, landform index and percentage of forest cover. When I crossvalidated the top model, the kappa value, a measure of the proportion of specific agreement, and was 0.804. During field tests the predictive model was used to find timber rattlesnakes at 3 of 15 (20%) of the test sites in the Goshen Wildlife Management Area in southwestern Virginia. My predictive model has proven to be an effective tool that could be used by biologists and land managers to locate and protect timber rattlesnake basking habitat. The historic and current ranges for the timber rattlesnake in Virginia were determined using literature records, database records, place names, personal interviews, and site surveys. Historically, the timber rattlesnake ranged over the entire state. Currently, the timber rattlesnake is restricted to the mountainous regions of Virginia (not including the coastal plain population of the timber rattlesnake). The biology of Crotalus horridus and regulations and management practices used by other states within the range of the species were used to create a set of management recommendations to the Virginia Department of Game and Inland Fisheries. These recommendations include implementing (1) a no-take regulation, (2) enhanced public education, and (3) protection of critical habitat and location of new populations. / Master of Science