The diploma thesis deals with the financial analysis of the selected company. On the analysis base of ratio financial indicators, time series, regression analysis and with using of the Maple system, the past and actual financial situation have been evaluated and future potential development of the company has been predicted.

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The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.

In this study I explore the feminist critique of Reinhold Niebuhr’s theology of sin, both to understand what the Niebuhrian and feminist understandings of sin talk about, and to see whether or not, or to what extent they are tenable in theory and in practice. Niebuhr’s feminist critics argue that Niebuhr’s claim of pride as the primary human sin fits only with men’s experience; women’s sin, they contend, is not self-inflation but self-loss. While I acknowledge the value of Niebuhr’s feminist critics’ interpretation of sin, this study provides a Niebuhrian response to the feminist critique. My main contention is that by overemphasizing women’s sin of passivity, some feminist theologians go too far to deny women’s capability of committing sin actively against others and the divine in both socio-moral and religio-theological aspect. The total rejection of the applicability of pride to women’s situation, I contend, undermines the profoundness of the feminist critique. I firstly give detailed expositions of Niebuhr’s theology of sin and the feminist critique of Niebuhr’s theology of sin respectively. The main discrepancies between the Niebuhrian and feminist understandings of sin will be laid out. Then I respond to some feminist criticisms by pointing out that the feminist misreading of Niebuhr on the topics of pride, the self, love, justice and the family is prevalent. I also question the two presuppositions of the feminist critique—the idea of women’s innocence and the spirit of secularity. These two presuppositions, I argue, contain in them some insoluble dilemmas that cause trouble for understanding women’s secular and religious experience. Lastly, I try to pull the insights of Niebuhr and his feminist critics together to form a more integrated view of women’s sin

Includes bibliography. / The objective of this thesis is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding efficient methods to identify the set of control stations which are likely to yield the best regression estimates of the missing values in the target station. The second class of methods is based on the EM algorithm, proposed by Dempster, Laird and Rubin (1977). The emphasis here is to estimate the missing values directly without first making a detailed selection of control stations. All "relevant" stations are included. This method has not previously been applied in the context of estimating missing rainfall values.

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Colleges and universities are under mounting pressure to meet enrollment goals in the face of declining college attendance. Insight into student-level probability of enrollment, as well as the identification of features relevant in student enrollment decisions, would assist in the allocation of marketing and recruitment resources and the development of future yield programs. A logistic regression model was fit to predict which applicants will ultimately matriculate (enroll) at Concordia College. Demographic, geodemographic and behavioral features were used to build a logistic regression model to assign probability of enrollment to each applicant. Behaviors indicating interest (campus visits, submitting a deposit) and residing in a zip code with high alumni density were found to be strong predictors of matriculation. The model was fit to minimize false negative rate, which was limited to 18.1 percent, compared to 50-60 percent reported by comparable studies. Overall, the model was 80.13 percent accurate.

With drastic increases in electricity prices throughout 2021, the public’s en- gagement in the discourse about the cost of electricity has reached new heights. With this in mind, this project was set in motion to try to make sense of factors that affect electricity prices in Sweden and thus identify areas of interest that further research could focus on.The variables chosen to be investigated were: Temperature, Exchange rate be- tween SEK and EUR, Export of electricity, Import of electricity, Inflation, Price of coal, Price of natural gas, and Price of crude oil. The data was gathered from various sources and was used to create a linear regression model.Exchange rate between SEK and EUR, Export of electricity, Inflation, Price of coal and Price of natural gas were found to be significant predictors of elec- tricity prices in Sweden and the source of electricity, economic policy, and energy trade were the areas of interest for further research.

This research builds off previous research conducted in 2009 which included a survey of healthcare professionals assessing their organization’s levels of supply chain maturity (SCM) and data standard readiness (DSR) from 1 to 5 [Smith, 2011]. With the survey data, Smith developed a 0-1 quadratic program to conserve the maximum amount of survey data while removing non-responses. This research uses the quadratic program as well as other machine learning algorithms and analysis methods to investigate what factors contribute to an organization’s SCM and DSR levels the most. No specific factors were found; however, different levels of prediction accuracy were achieved across the five different subsets and algorithms. he best accuracy prediction SCM model was linear discriminant analysis on the Reduced subset at 50.84% while the highest prediction accuracy for DSR was stepwise regression on the PCA subset at 45.00%. Most misclassifications found in this study were minimal.

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