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Den här uppsatsen inleds med att studera de moment som används för multinomial logistisk regression och hur resultaten mäts. Teorin tar sin avsats i den binomiala logistiska regression, för att stegvis ta sig vidare till den multinomiala logistiska regressionen. Begreppen logit, oddskvoten och sannolikheterna förtydligas, effekterna av de oberoende variablerna diskuteras och kopplingen till vanlig linjär regression åskådliggörs. Det blir även en fördjupning av matematiken bakom den logistiska funktionen. Därefter tillämpas den multinomial logistisk regressionsanalysen med ett praktiskt exempel. Analysmodellen är användbar inom flertalet områden och den här uppsatsen ligger inom ramen för sportanalys. Matchstatistik från ishockey och närmare bestämt Örebro Hockeys matcher från säsongerna 2012/13 till 2017/18 nyttjas och den slutgiltiga modellen använder sig av tre förklarande variabler. Resultatet visar att utfallet efter ordinarie tid kan förklaras till 60,9% med hjälp av matchstatistiken, vilket tyder på att den multinomiala regressionsmodellen presterar likvärdigt med andra metoder som tillämpar kategorisk dataanalys inom sportanalys. / This thesis starts by studying the multinomial logistic regression and its moments and how the results are measured. The theory begins with the binomial logistics regression and gradually moves on towards the multinomial logistics regression. Concepts as logit, odds ratio and probabilities are explained, the effects of the independent variables discussed and the link to ordinary linear regression is illustrated. There will also be a deeper, mathematical look at the function of logistic growth. Thereafter the multinomial logistic regression model will be applied. The model is useful within several domains and this thesis lies within sportsanalytics. For this thesis matchstatistics from ice hockey, that is Örebro Hockey’s matches from season 2012/13 to 2017/18, has been used and the final model has three exploratory variables. The outcome of the result performs equivalent to other methods, which applies categorical data analysis within sportsanalytics.

Financial markets have been crucial in driving capital investments across the world. Anessential piece of these markets is the presence of risk takers, or market speculators, who will hold financial portfolios in hopes of profit. Portfolios with cash flowsgenerated from floating interest rate derivatives will often be subjected to fixing risk, also called second-order basis risk, stemming from a discrepancy in time with the hedge and the original position. Using data from a fixing risk mitigation service, named RESET, this thesis aims todeepen the understanding of accumulation of fixing risk on the the USD dollar market for 3-month interest rate swaps. This is done by modeling customer behavior using machine learning methods. Macroeconomic factors such as market volatility and the January effect amongst others were incorporated as variables into the set. The two models explored are logistic regression and neural networks, the first one chosen for interoperability and the latter for its generality. Neither of the two models could accurately predict customer behavior, with a balanced accuracy short of 70 percent. The strongest influence of the final prediction turned out to be previous behavior, the January effect and how many of their financial positions the customer previously put into the service.

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

published_or_final_version / abstract / toc / Statistics and Actuarial Science / Master / Master of Philosophy

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In this paper I have separated three periods of probability theory teaching: 1. Probability theory teaching in Lithuania during 1920-1940’s. 2. Probability theory teaching in Lithuania during 1960-1990’s. 3. Probability theory teaching in Lithuania during 1991-2000’s. In the prewar Lithuania probability theory was a separate science already, examined in university, applied in other disciplines. In secondary schools subject was not taught yet. At that moment first textbook of probability theory was published: V. Biržiška, Fundamentals of mathematical probability theory: lectures in Lithuanian university during 1928 – 1929’s in Kaunas, 1930, p. 588. This science was very interesting for the mathematicians. Probability theory gained importance in the everyday life, too. At the end of 1940’s elements of probability theory were included into mathematics programs in the further education schools. During 1960-1990’s probability theory was studied in the Lithuanian schools as an optional subject. Course was rather wide and complicated. At that period many additional training aids appeared, scholars carried out research and experiments, searched for optimal methods of teaching probability theory. Science as such was interesting not only for the scientists, methodologists, but for the psychologists, too, who examined children, trying to decide, what age is the best to start learning probability theory. At that moment Vytautas Liutikas published his book „How probabilities of events... [to full text]