Fastigheter beläggs med olika pris beroende på en mängd olika faktorer. Denna uppsats syftartill att analysera hur några av dessa prispåverkande faktorer och hur de förhåller sig tillsmåhusfastigheter i Helsingborg. Utöver detta kommer även en faktor, kallad njutningsfaktorn,analyseras för att försöka fastställa hur individers preferenser påverkar värdet. Analysenbaseras på statistiska underlag som kommer att behandlas med hjälp av den hedoniskaprismodellen. Bid-rent modellen kommer att appliceras på staden Helsingborg och dessområden för att skapa en bild av lägesfaktorn. En fördjupning i psykologi och urban ekonomikommer att bidra till analysen av både fysiska och psykologiska aspekter. Resultaten vi fåttfram är att det inte enbart kan tas hänsyn till fysiska aspekter i förhållande till pris på fastigheterutan det är även viktigt att behandla de psykologiska aspekterna för varje individ. / Different real estates have different value, depending upon several factors. This essay aims toanalyse some of these values bearing factors and how they relate to properties in Helsingborg.Beside these factors another factor, called the pleasure factor, will be analysed in an attempt todetermine how individual preferences affects the value. The analysis will be based on statisticmaterial that will be processed with the hedonic price model. The bid-rent model will beapplied to the city and to its areas, to help create an understanding of the location factor. Adepression of psychology and urban economics will contribute to both physical andpsychological aspects of the analysis. The results that will be presented will show that inconsideration to price we can't just tend to the physical aspects but must also consider thepsychological aspects for each individual.

Det har länge pågått diskussioner om hur förhållandet mellan inflation och ekonomisk tillväxt ser ut och vilken påverkan, inflation har på ekonomisk tillväxt. I denna uppsats analyseras relationen mellan inflation och ekonomisk tillväxt inom OECD-länder samt Subsahariska Afrika med hjälp av paneldata från tidperioden 2002–2021. Studien använder BNP per capita tillväxt som beroende variabel samt sju oberoende variabler (inflation, initial BNP per capita, befolkningstillväxt, handel, investeringar, statliga utgifter och finansiellt djup) för att utforma en regressionsmodell.  Regressionsmodellen genomfördes med fixed effects för att kontrollera icke observerade länder med specifika egenskaper och tidsrelaterade chocker. Resultaten i studien visar att det finns ett negativ samband mellan inflation och ekonomisk tillväxt som är statistik signifikant. / There have long been discussions about what the relationship between inflation and economic growth looks like and what impact inflation have on economic growth. In this essay, the relationship between inflation and economic growth is analysed within OECD countries and Sub-saharan Africa in the time period 2002 – 2021 using paneldata. The thesis uses GDP per capita growth as the dependent variable as well as seven independent variables (inflation, initial GDP per capita, population growth, trade, investment, government expenditures and financial depth) to design a regression model. The regression is carried out with fixed effects to control for unobserved countries specifics charactheritstics and time related shocks . The results in the study shows that there exist a negativ relationship between inflation and economic growth which is statistical significant.

Syftet med denna studie är att undersöka hur nivån på humankapital i ett land kommer att påverka effekten av utländska direktinvesteringar på ekonomisk tillväxt. Studien omfattar 31 OECD-länder under tidsperioden 1989-2020. FDIs inverkan på ekonomisk tillväxt undersöks i en multipel regressionsmodell med paneldata där fokus ligger på samspelet mellan utländska direktinvesteringar och utbildning (en proxy för humankapital). Det teoretiska ramverket är huvudsakligen fokuserat på Romers endogena tillväxtteori och institutionell teori. Resultatet av den multipla regressionsmodellen visar att utländska direktinvesteringar som en enskild variabel har en positiv effekt på ekonomisk tillväxt. Samspelet mellan utländska direktinvesteringar och humankapital visade sig ha en negativ effekt på den ekonomiska tillväxten. Resultatet av humankapitalet som en enskild faktor visade sig dock vara positivt i förhållande till ekonomisk tillväxt. Resultatet av studien indikerar att utländska direktinvesteringar som en oberoende faktor har en positiv inverkan på den ekonomiska tillväxten samt humankapital som en oberoende faktor. Resultatet tyder på att en hög nivå på humankapitalet i ett land attraherar inte tillräckligt mycket utländska direktinvesteringar för att ha en positiv effekt på den ekonomiska tillväxten. / The purpose of this study is to examine how the level of human capital in a country will influence the effect FDI has on economic growth. The study covers 31 OECD-countries during the time period 1989-2020. The impact of FDI on economic growth is examined in a multiple regression model with panel data where the focus is on the interaction between FDI and education (a proxy for human capital). The theoretical framework is mainly focused on Romer’s endogenous growth theory and institutional theory. The result of the multiple regression model shows that FDI as a single variable has a positive effect on economic growth. The interaction between FDI and human capital was shown to have a negative effect on economic growth. However, the result of human capital as a single factor was proved to be positive in relation to economic growth. The results indicate that a high level of human capital in a country does not attract enough foreign direct investment to have a positive effect on economic growth.

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R2) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape.

Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to transform the data. There are two parametrization for the presented model, one using the common Dirichlet distribution's alpha parameters, and a reparametrization of the alpha's to set up a mean-and-dispersion-like model. By applying appropriate link-functions, a GLM-like framework is set up that allows for the analysis of such data in a straightforward and familiar way, because interpretation is similar to multinomial logistic regression. This paper gives a brief theoretical foundation and describes the implementation as well as application (including worked examples) of Dirichlet regression methods implemented in the package DirichletReg (Maier, 2013) in the R language (R Core Team, 2013). (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R2) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape.

Um den Planungsprozess von oberflächennahen Geothermieanlagen verbessern zu können, ist es von Vorteil, die Parameter zu kennen, welche besonders großen Einfluss auf die Leistung einer solchen Anlage haben. Um dies zu untersuchen, können globale Sensitivitätsanalysen durchgeführt werden. Die in dieser Arbeit vorgestellte Sensitivitätsanalyse beinhaltet ein Parameterscreening mit der One-Variable-At-a-Time-Methode und eine anschließend durchgeführte globale Sensitivitätsanalyse mithilfe von Sobol‘-Indizes. Hierbei werden die Eingabeparameter des verwendeten Berechnungsmodells innerhalb vorher definierter Wertebereiche und gemäß festgelegter Verteilungen variiert, was in einer großen Anzahl an Modelldurchläufen resultiert. Daher kommen bei Sensitivitätsanalysen oftmals approximierte Modelle zum Einsatz, welche das Verhalten des ursprünglichen Berechnungsmodells nachahmen sollen, um auf diese Weise eine geringere Rechenzeit zu erzielen. Hierfür werden in der vorliegenden Arbeit sogenannte Gauß-Prozess-Emulatoren verwendet. In dieser Arbeit werden die genannten Methoden aus mathematischer Sicht vorgestellt und eingeordnet und abschließend an einem analytischen Modell für die Untergrundparameter einer Geothermieanlage vorgeführt.