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

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

This work investigates the factors that contribute to financial crises. We first study the Dow Jones index performance by grouping the daily adjusted closing value into a two-month window and finding several critical quantiles in each window. Then, we identify severe downturn in these quantiles and find that the 5th quantile is the best to identify financial crises. We then matched these quantiles with historical financial crises and gave a basic explanation about them. Next, we introduced all exogenous factors that could be related to the crises. Then, we applied a rapid Bayesian variable selection technique - Stochastic Search Variable Selection (SSVS) using a Bayesian logistic regression model. Finally, we analyzed the result of SSVS, leading to the conclusion that that the dummy variable we created for disastrous hurricane, crude oil price and gold price (GOLD) should be included in the model. / text

<p>På bara några decennier har kravet på utbildning vuxit markant och därmed också antalet universitetsstuderande. Syftet med denna uppsats har varit att undersöka huruvida det lönar sig att vidareutbilda sig på universitetsnivå och i vilken utsträckning det finns skillnader för män respektive kvinnor inom en mans och en kvinnodominerad utbildning. Fyra regressioner har genomförts. Därefter har en jämförelse skett mellan de olika livsinkomsternas nuvärden. Livsinkomsten har räknats fram genom att subtrahera nuvärdet av alternativkostnaderna från nuvärdet av livsinkomsterna. Vi har funnit att man gör en avkastning på att utbilda sig samt att det finns påtagliga skillnader mellan män och kvinnor både inom den mans- och den kvinnodominerade utbildningen.</p>

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In this thesis the air infiltration through the envelope of a naturally ventilated stone church located in Bergby (Gävle, Sweden) is studied. The project is focused on Multiple Linear Regression (MLR) modeling the air change rate (ACH) inside the church hall and studying the factors (stack effect and wind effect) that influence the air infiltration. The weather parameters outside the building were recorded in a weather station and the properties of the air inside the church was analyzed with different methods. Infrared thermography techniques and thermistors were used to measure the temperature inside, the tracer gas method to measure the ACH and the blower door technique to measure the tightness of the building envelope. In order to know the pressure coefficients on the church envelope a physical model of the building was studied in a wind tunnel. Firstly, only the values obtained from the weather station were used to calculate the predictors of ACH and see which parameter influence more on its variation:  temperature difference (∆T) indicating the stack effect; and wind speed (WS), the component of wind speed perpendicular to the long-side facades of the church (WS90) and their square values (WS2 and WS902) indicating the wind effect. The data obtained in the wind tunnel were later used to do the MLR study with new predictors for indicating wind effect (∆Cp∙WS, ∆Cp∙WS2, ∆CpOUT-IN·A∙WS, ∆CpOUT-IN·A∙WS2, ∆CpC-H∙WS, ∆CpC-H∙WS2). Better prediction of ACH was obtained with the square of the wind speed (WS2) instead of the magnitude itself (WS). However, the latter (WS) provided better results than the regression with the magnitude of the perpendicular component of the wind (WS90). Although wind speed influences in ACH, it alone seems to be a very poor predictor of ACH since has a negative correlation with ΔT when the data under study include both day and night. However when high wind speed are detected it has quite strong influence. The most significant predictions of ACR were attained with the combined predictors ∆T &amp; WS and ∆T &amp; ∆CpOUT-IN·A∙WS2. The main conclusion taken from the MLR analysis is that the stack effect is the most significant factor influencing the ACH inside the church hall. This leads to suggest that an effective way of reducing ACH could be sealing the floor and ceiling of the church because from those areas the air infiltration has big influence on the ACH inside the church hall, and more in this case that have been noted that the floor is very leaky. Although different assumptions have been done during the analyses that contribute to make the predictions deviate from reality, at the end it would be possible to asses that MLR can be a useful tool for analyzing the relative importance of the driving forces for ACR in churches and similar buildings, as long as the included predictors not are too mutually correlated, and that attained models that are statistically significant also are physically realistic. / Church project

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No description available.

I denna studie undersöks förklaringsfaktorer till lärares trivsel med sin skolledning. Studien använder data från Skolverkets enkätundersökning Attityder till skolan 2015 samt 2014/2015 års version av Lärarregistret. Bland annat betraktas förutsättningar för undervisningen, inflytande på arbetsplatsen, stressnivå samt bakgrundsvariabler för lärare, rektorer och skolor. För att undersöka effekterna används ordinal logistisk regression. De variabler och faktorer som uppvisar en positiv signifikant effekt på trivsel med skolledningen är lärarens inflytande över resursfördelning, lärarens upplevelse av skolledningens mottaglighet för kritik, tillgången till teknisk IT-support samt stöd för undervisningen. Lärarens stressnivå har en negativ signifikant effekt.

Approved for public release; distribution in unlimited. / In this thesis we develop a procedure for detecting erroneous payments in the Defense Finance Accounting Service, Internal Review's (DFAS IR) Knowledge Base Of Erroneous Payments (KBOEP), with the use of supervised (Logistic Regression) and unsupervised (Classification and Regression Trees (C & RT)) modeling algorithms. S-Plus software was used to construct a supervised model of vendor payment data using Logistic Regression, along with the Hosmer-Lemeshow Test, for testing the predictive ability of the model. The Clementine Data Mining software was used to construct both supervised and unsupervised model of vendor payment data using Logistic Regression and C & RT algorithms. The Logistic Regression algorithm, in Clementine, generated a model with predictive probabilities, which were compared against the C & RT algorithm. In addition to comparing the predictive probabilities, Receiver Operating Characteristic (ROC) curves were generated for both models to determine which model provided the best results for a Coincidence Matrix's True Positive, True Negative, False Positive and False Negative Fractions. The best modeling technique was C & RT and was given to DFAS IR to assist in reducing the manual record selection process currently being used. A recommended ruleset was provided, along with a detailed explanation of the algorithm selection process. / Lieutenant Commander, United States Navy