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This master thesis uses applied mathematicalstatistics to analyse purchase behaviour based on customer data of the Swedishbrand Indiska. The aim of the study is to build a model that can helppredicting the sales quantities of different product classes and identify whichfactors are the most significant in the different models and furthermore, tocreate an algorithm that can provide suggested product combinations in thepurchasing process. Generalized linear models with a Negative binomial distributionare applied to retrieve the predicted sales quantity. Moreover, conditionalprobability is used in the algorithm which results in a product recommendationengine based on the calculated conditional probability that the suggestedcombinations are purchased.From the findings, it can be concluded that all variables considered in themodels; original price, purchase month, colour, cluster, purchase country andchannel are significant for the predicted outcome of the sales quantity foreach product class. Furthermore, by using conditional probability andhistorical sales data, an algorithm can be constructed which createsrecommendations of product combinations of either one or two products that canbe bought together with an initial product that a customer shows interest in. / Matematisk statistik tillämpas i denna masteruppsats för att analysera köpbeteende baserat på kunddata från det svenska varumärket Indiska. Syftet med studien är att bygga modeller som kan hjälpa till att förutsäga försäljningskvantiteter för olika produktklasser och identifiera vilka faktorer som är mest signifikanta i de olika modellerna och därtill att skapa en algoritm som ger förslag på rekommenderade produktkombinationer i köpprocessen. Generaliserade linjära modeller med en negativ binomialfördelning utvecklades för att beräkna den förutspådda försäljningskvantiteten för de olika produktklasserna. Dessutom används betingad sannolikhet i algoritmen som resulterar i en produktrekommendationsmotor som baseras på den betingade sannolikheten att de föreslagna produktkombinationerna är inköpta.Från resultaten kan slutsatsen dras att alla variabler som beaktas i modellerna; originalpris, inköpsmånad, produktfärg, kluster, inköpsland och kanal är signifikanta för det predikterade resultatet av försäljningskvantiteten för varje produktklass. Vidare är det möjligt att, med hjälp av betingad sannolikhet och historisk försäljningsdata, konstruera en algoritm som skapar rekommendationer av produktkombinationer av en eller två produkter som kan köpas tillsammans med en produkt som en kund visar intresse för.

Abstract Pedestrian /bicycle safety of school children has been a growing menace that has been attracting attention from transportation professionals, school boards, media and the community all over the country. As such there has been a necessity to identify critical variables and assess their importance in pedestrian/bicycle crashes occurring in and around school zones. The current study is an endeavor in this direction. The literature review identified some studies that were conducted on school zone safety related to pedestrian/bicyclist crashes. Most of the studies pertained to crashes with all age groups. There have been few studies with emphasis only on school aged children. In this study we focus on pedestrian age group (4 to 18 years), the time of the day when the school children are expected to be commuting (6:30 AM to 10:00 AM and 1:00 PM to 5:00.PM), the day of week (Monday through Friday) and the days when the school is opened (January 6th to May 31st and August 6th to December 21st). Geographical Information Systems was used to locate buffer zones around schools with higher crash incidence rates. The use of log-linear analysis has culminated in explaining the relationship between various variables and crash incidence or crash frequency Crash data for this study was obtained in the form of crash database and GIS maps from the Department of Highway Safety and Motor Vehicles and the Orange County School Board respectively. Crash reports were downloaded from the CAR database of the FDOT mainframe website. The crash data was related to the GIS maps to visually depict the proximity of crashes to the school zones and thus identified risky schools and school districts. It was concluded from the spatial analysis that the incidence of crashes was higher at middle schools. In the log-linear analysis different models were i tested to explain the effects of driver characteristics, geometric characteristics and pedestrian characteristics on the crash frequency. It was found that driver age, number of lanes, median type, pedestrian age and speed limit are the critical variables in explaining crash frequency. By examining the levels of the variables that were significantly involved in the crashes we would get an insight on ways to explain and control pedestrian/bicyclists crashes at school zones. It is hoped that this thesis would make an active contribution in improving the safety of bicyclists and pedestrians in and around school zones and make the schools much safer for the children.

Characterizing streamflow trends is important for water resources management. Streamflow conditions, and trends thereof, are critical drivers of all aspects of stream geomorphology, sediment and nutrient transport, and ecological processes. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and urban development. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land change rates and patterns within the upstream contributing areas. Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions. / Master of Science / Water availability is important for resources management. Streamflow is a measure of available surface water and is an important component in the hydrological cycle. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and city expansion. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land cover change rates within the area upstream of the gage (or drainage/contributing area). Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions.

In this thesis, we explore the impact of problem representation on the ability for the genetic algorithms (GA) to evolve a binary prediction model to predict whether a physical therapist is paid above or below the median amount from Medicare. We explore three different problem representations, the vector GA (VGA), the binary GA (BGA), and the proportional GA (PGA). We find that all three representations can produce models with high accuracy and low loss that are better than Scikit-Learn’s logistic regression model and that all three representations select the same features; however, the PGA representation tends to create lower weights than the VGA and BGA. We also find that mutation rate creates more of a difference in accuracy when comparing the individual with the best fitness (lowest binary cross entropy loss) and the most accurate solution when the mutation rate is higher. We then explore potential of biases in the PGA mapping functions that may encourage the lower values. We find that the PGA has biases on the values they can encode depending on the mapping function; however, since we do not find a bias towards lower values for all tested mapping functions, it is more likely that it is more difficult for the PGA to encode more extreme values given crossover tends to have an averaging effect on the PGA chromosome.

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