Der Zusammenhang zwischen den Begriffen "Chance" (odd) und "Wahrscheinlichkeit" (probability) und die Anwendung des Chancenverhältnisses (odds ratio) im Bereich der Biometrie und bei der logistischen Regression werden erläutert. Es wird auf mögliche Fehlinterpretationen der Begriffe Chance und Chancenverhältnis hingewiesen.

The aim of this diploma thesis was to analyze support for democracy and find out whether there are differences in democracy support by younger and older generations. Furthermore, differences in democracy support were determined for subgroups defined by socio- demographic factors (education, gender, size of the place of residence) and political attitudes (the left-right scale of political orientations, sympathy for the KSČM, satisfaction with the economic situation and the Czech government) and was analyzed the influence of these factors on democracy support. For the needs of this work were used secondary data from the 4th wave of the research Transformations of Czech Society from 2018. Attitudes towards democracy and support for democracy in subgroups were analyzed through descriptive statistics and pairwise comparisons. The influence of selected factors on democracy support was analyzed by binary logistic regression. The results show a relatively high level of support for democracy, but also differences in the level of support according to the type of question which was used. In the evaluation of regime types, democracy was evaluated as the best regime. But exists a group of people who would support alternative forms under certain conditions, or who are resigned to the type of regime in which they...

With the increasing demand and interest in wind power worldwide, it is interesting to study the effects of running windfarms on the activity of reindeers and estimate the associated Zone of Influence (ZOI) relative to these disturbances. Through simulation, Hierarchical Likelihood (HL) and adaptive Lasso methods are used to estimate the ZOI of windfarms and catching the correct threshold at which the negative effect of the disturbances on the reindeer behaviour disappears. The results found some merit to the explanation that the negative effect may not disappear abruptly and more merit to the fact that a linear model was still a better choice than the smooth polynomial models used. A real-life data related to reindeer faecal pellet counts from an area in northern Sweden were windfarms were running were analyzed. The yearly time series data was divided into three periods : before construction, during construction and during operation of the windfarms. Logistic regression, segmented model, and HL methods were implemented for data analysis by using covariates as distance from wind turbine, vegetation type, the interaction between distance to wind turbine and time period. A significant breakpoint could be estimated using the segmented model at a distance of 2.8 km from running windfarm, after which the negative effects of the windfarm on the reindeer activity disappeared. However, further work is needed for estimation of ZOI using HL method and considering other possible factors causing disturbances to the reindeer habitat and behaviour.

The thesis investigates a publicly available dataset which covers morethan three million events in football matches. The aim of the study isto train machine learning models capable of modeling the relationshipbetween a shot event and its outcome. That is, to predict if a footballshot will result in a goal or not. By representing the shot indifferent ways, the aim is to draw conclusion regarding what elementsof a shot allows for a good prediction of its outcome. The shotrepresentation was varied both by including different numbers of eventspreceding the shot and by varying the set of features describing eachevent.The study shows that the performance of the machine learning modelsbenefit from including events preceding the shot. The highestpredictive performance was achieved by a long short-term memory neuralnetwork trained on the shot event and six events preceding the shot.The features which were found to have the largest positive impact onthe shot events were the precision of the event, the position on thefield and how the player was in contact with the ball. The size of thedataset was also evaluated and the results suggest that it issufficiently large for the size of the networks evaluated.

There are ongoing discussions about a new reform of the mandatory fully funded individual accounts in the Swedish public pension system. Since the initial round in 2000, several funds have been excluded from the platform due to deceptive, and sometimes criminal, behavior towards the consumers. This paper analyzes which individuals that have invested in these funds, examines possible explanations for this, and sheds light on the current structure of the Premium Pension Scheme. By using a rich dataset on 650,000 individuals that consist of both those who have been in six particular dubious funds and a random sample of the rest of the Swedish pension savers, the variables of interest are evaluated in a logistic setting. The results show that individuals who are men, unmarried, divorced, in their older-middle age, have lower-incomes, live in rural areas, and the North of Sweden are more likely to have invested in one of the dubious funds. The results also reveal that some funds have clearer target-groups, while others have targeted more randomly. The study emphasizes the need for improving people’s financial decision-making through improved information.

This study seeks to show that a higher usage of debt instruments by consumers with limited available funds leads to the usage of savings tools to finance debt costs, which subsequently results in lower levels of savings. This was espoused by the literature on PFM and also proven by the test results from the research hypotheses that were computed by means of a logistic regression. The test results showed that there is a statistically significant relationship between the usage of debt instruments and the usage of savings tools. An emphasis is placed on the importance of savings as an integral component of the PFM concept: it is namely seen to be indispensable to good financial planning to ensure current and future consumer financial security. Therefore, this study concludes by highlighting the importance of consumers’ financial- management skills in minimising debt costs to increase levels of savings by controlling higher consumption expenditure through debt. / Business Management / M. Com. (Business management)

This thesis provides an introduction to methods for handling missing data. A thorough review of earlier methods and the development of the field of missing data is provided. The thesis present the methods suggested in today’s literature, multiple imputation and maximum likelihood estimation. A simulation study is performed to see if there are circumstances in small samples when any of the two methods are to be preferred. To show the importance of handling missing data, multiple imputation and maximum likelihood are compared to listwise deletion. The results from the simulation study does not show any crucial differences between multiple imputation and maximum likelihood when it comes to point estimates. Some differences are seen in the estimation of the confidence intervals, talking in favour of multiple imputation. The difference is decreasing with an increasing sample size and more studies are needed to draw definite conclusions. Further, the results shows that listwise deletion lead to biased estimations under a missing at random mechanism. The methods are also applied to a real dataset, the Swedish enrollment registry, to show how the methods work in a practical application.

This project will explore machine learning approaches that are used in creditscoring. In this study we consider consumer credit scoring instead of corporatecredit scoring and our focus is on methods that are currently used in practiceby banks such as logistic regression and decision trees and also compare theirperformance against machine learning approaches such as support vector machines (SVM), neural networks and random forests. In our models we addressimportant issues such as dataset imbalance, model overfitting and calibrationof model probabilities. The six machine learning methods we study are support vector machine, logistic regression, k-nearest neighbour, artificial neuralnetworks, decision trees and random forests. We implement these models inpython and analyse their performance on credit dataset with 30000 observations from Taiwan, extracted from the University of California Irvine (UCI)machine learning repository.

A child dies of pneumonia every 39 seconds, and the process of preventing deaths caused by pneumonia has been considerably slower compared to other infectious diseases. Meanwhile, the traditional method of manually diagnosing patients has reached its ceiling on performance. With the support of a machine learning classification algorithm to help with the screening of pneumonia from x-ray images combined with the expertise of a physician, the identification and diagnosis of pediatric pneumonia should be both quicker and more accurate. In this study, four different types of supervised machine learning algorithms have been trained, tested, and evaluated to see which model could predict most accurately whether a patient in an x-ray image has pneumonia or not. The four models included in this study have been trained by four different supervised machine learning algorithms: logistic regression, k-nearest-neighbor, support vector machine, and neural network. The results show that KNN has the highest sensitivity, NN adapts to new data the best by not being under- or overfit. SVM had the highest balanced accuracy on both train and test data but a proportionally high difference between the in- and out-sample error. In conclusion, relatively high performance can be achieved when classifying x-ray images of pneumonia even with limited resources.

The aim of this paper is to study to what extent socio-economic factors affect the grade in social studies 1b in upper secondary school at municipal level during the period of 2015-2021. The method in this study is a binomial logistic regression and the dependent variable is the grade, and the independent variables are gender, background, parental education level and certified teacher. The data for the dependent and the independent variables are collected from the Swedish National Agency for Education's database Siris at municipal level and for the upper secondary school for the college preparatory programs. Data for 30 municipalities was collected and categorized according to the municipal group division from 2017 of Sweden's Municipalities and Regions. Pierre Bourdieu's capital theory about how education has a social stratification function is used to explain the results from the binomial logistic regression.  The result of this paper indicates that socio-economic factors affect the grade. All the independent variables affect the grade in social studies during the different years. The independent variable parental education level affected the grade in 2015-2017 and 2020/2021. The independent variable gender affected the grade in 2017-2019. Certified teacher ratio affected the grade only in 2018/2019. The independent variable background affected the grade in 2017/2018. The binomial logistic regression indicates that the residual looks similar for all municipal groups A-C for almost all the years apart from in 2017/2018. Thus, the predictive power of the binomial logistic regression model is good for all municipal groups. The results indicate that socio-economic factors affect students' grades in social studies. This is relevant for professional teachers and school principals to be aware of in order to be able to provide all the students with an equal education regardless oft their socio-economic background.