This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy of the models.This thesis attempts to show that Lasso gives the best score among other algorithms when using the public dataset in training. The correlation graphs show the variables' level of dependency. In addition, the empirical results show that crime, deposit, lending, and repo rates influence the house prices negatively. Where inflation, year, and unemployment rate impact the house prices positively.

The aim of this work is to get the best detailed knowledge about hardness distribution in first 60 mm below the surface of backing roll. To this end, a method for obtaining multi-dimensional polynomial regression was developed and then a computer program for its processing was written.Way of finding suitable regression surfaces and their subsequent interpretation, is a pivotal part of this work.

The load forecasting has become an important role in the operation of power system, and several models by using different techniques have been applied to solve these problems. In the literature, the linear regression models are considered as a traditional approach to predict power consumption, and more recently, the artificial neural network (ANN) models have received more attention for a great number of successful and practical applications. This report introduces both linear regression and ANN models to predict the power consumption for Fortum in Ekerö. The characteristics of power consumption of different kinds of consumers are analyzed, together with the effects of weather parameters to power consumption. Further, based on the gained information, the numerical models of load forecasting are built and tested by the historical data. The predictions of power consumption are focus on three cases separately: total power consumption in one year, daily peak power consumption during winter and hourly power consumption. The processes of development of the models will be described, such as the choice of the variables, the transformations of the variables, the structure of the models and the training cases of ANN model. In addition, two linear regression models will be built according to the number of input variables. They are simple linear regression with one input variable and multiple linear regression with several input variables. Comparison between the linear regression and ANN models will be carried out. In the end, it finds out that the linear regression obtains better results for all the cases in Ekerö. Especially, the simple linear regression outperforms in prediction of total power consumption in one year, and the multiple linear regression is better in prediction of daily peak load during the winter.

The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determination of wind’s CV should involve the use of long-term (i.e., multiple decades) coincident load and wind data. In addition to the challenge of finding high-quality, long-term wind data, existing load data more than several years old is of limited utility due to shifting end usage patterns that alter a region’s electricity load profile. Due to a lack of long-term data, current industry practice does not adequately account for the effects of weather variability in CV calculations. To that end, the objective of this thesis is to develop a model to “hindcast” what the historic regional load in New England would have been if governed by the conjoined influence of historic weather and a more current load profile. Modeling focuses exclusively on summer weekdays since this period is typically the most influential on CV. The summer weekday model is developed using multiple linear regression (MLR), and features a separate hour-based model for eight sub-regions within New England. A total of eighty-four candidate weather predictors are made available to the model, including lagged temperature, humidity, and solar insolation variables. A reanalysis weather dataset produced by the National Aeronautics and Space Administration (NASA) – the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset – is used since it offers data homogeneity throughout New England over multiple decades, and includes atmospheric fields that may be used for long-term wind resource characterization. Weather regressors are selected using both stepwise regression and a genetic algorithm(GA) based method, and the resulting models and their performance are compared. To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hours of the summer weekday period, suggesting that the modeling approach developed as part of this research has merit and that further development of the hindcasting model is warranted.

This academic work is devoted to the examination of the relationship between geography and sport. Firstly, it presents the geography of sport as a subdiscipline in the system of geography. Afterwards it discusses the relationship between geography and sport in general in different environments and at different scale levels. After the general introduction, the work deals with sports attendance as a possible characteristics of the position of sport in the area. The analytical part examines the relationship between geography and sport on specific mechanisms in a particular environment. The research studies ice hockey in the Czechia. In the first part of the analysis, ice hockey is generally presented from the beginning to the present in the area, then the attendance of the highest domestic hockey league in the period not marked by significant changes in spatial administration (from 1993, when Czechoslovakia ceased to exist, or from 2000, when the self-governmental regions were established), until 2019, when the league under investigation was not affected by the coronavirus pandemic. The analyzed data come mostly from official internet sources. The position of sport in society is changing in time and space. Sport is creating the identity and social community. The attendance of the league is growing...

This paper examines the impact of several macroeconomic variables on property prices in Sweden. Linear regression is used to construct severalmathematical models relating the macroeconomic variables to property prices. Using methods of variables selection and goodness of fit measures,two final models are selected and subsequently compared, resulting in one final model. From this model, we conclude that GDP per capita, unemployment rate, inflation and repo interest rate have a significant relationship with property price changes in Sweden. Unemployment, GDPper capita, and inflation have positive relationships with property price changes, while repo interest rate has a negative relationship with propertyprice changes. However, as to what extent these variables affect property prices, no certain conclusions can be drawn from this study. / Följande studie undersöker inverkan av sex makroekonomiska variabler på bostadspriser i Sverige. Linjär regressionsanalys används för att skapaflera matematiska modeller som relaterar makroekonomiska variabler till bostadspriser. Vidare används variabelselektion och statistikor för modellevaluering för att välja ut två slutgiltiga modeller. Dessa två modeller jämförs och en slutgiltig modell väljs ut. Studiens slutsatser dras fråndenna modell. BNP per capita, arbetslöshetsgrad, inflationstakt, och reporänta har enligt den slutgiltiga modellen signifikanta förhållandentill bostadspriser i Sverige. Vidare har arbetslöshet, BNP per capita, och inflation positiva förhållanden till bostadsprisförändringar, medan reporänta har ett negativt förhållande. Studien kan inte dra några slutsatser om till vilken grad dessa variabler påverkar bostadspriser i Sverige.

The possible effects of the General Data Protections Regulations (GDPR) have been widely discussed among policymakers, stakeholders and ordinary people who are the objective for data collection. The purpose of GDPR is to protect people’s integrity and increase transparency for how personal data is used. Up until May 25th, 2018 personal data could be sampled and used without consent from users. Many argue that the introduction of GDPR is good, others are reluctant and argue that GDPR may harm data-driven companies. The report aims to answer how GDPR affects sales at the flight search engine Flygresor.se. By examining how and to what extent these regulations impact revenue, it is hoped for that these findings will lead to a deeper understanding of how these regulations affect businesses. Multiple linear regression analysis was used as the framework to answer the research question. Numerous models were constructed based on data provided by Flygresor.se. The models mostly included categorical variables representing time indicators such as month, weekday, etc. After carefully performing data modifications, variable selections and model evaluation tests three final models were obtained. After performing statistical inference tests and multicollinearity diagnostics on the models it could be concluded that an effect from GDPR could not be statistically proven. However, this does not mean that an actual effect of GDPR did not occur, only that it could not be isolated and proven. Thus, the extent of the effect of GDPR is statistically inconclusive. / De möjliga följderna av införandet av General Data Protections Regulations (GDPR) har varit väl omdiskuterat bland beslutsfattare, intressenter och människor som är målet för datainsamlingen. Syftet med GDPR är att skydda människors integritet samt öka insynen för hur personlig data används. Fram tills den 25 maj 2018 har det varit möjligt att samla in och använda personuppgifter utan samtyckte från användare. Många menar att införandet av GDPR är nödvändigt medans andra är mer kritiska och menar att GDPR kan skada lönsamheten för data beroende verksamheter. Denna rapport syftar till att svara på huruvida GDPR har påverkat försäljningen på flygsökmotorn Flygresor.se. Genom att undersöka om och i vilken utsträckning dessa regler påverkat intäkterna, är förhoppningen att dessa resultat kan leda till en djupare förståelse för hur GDPR påverkar företag. Multipel linjär regressionsanalys användes som ramverk för att svara på frågeställningen. Flera modeller utformades baserat på data som tillhandahölls av Flygresor.se. Modellerna var främst baserade på kategoriska variabler som representerade tidsaspekter så som månad, veckodag etc. Efter ett grundligt genomförande av data modifieringar, variabelselektion och modellutvärdering kunde tre modeller konstateras. Efter att ha genomfört signifikanstester och korrelationstester på modellerna kunde det fastställas att en effekt från GDPR inte kunde statistiskt säkerställas. Dock betyder detta inte att GDPR inte har haft en faktisk effekt, utan att en effekt inte kunde isoleras och bevisas.

I en företagstransaktion anlitas vanligen en investmentbank för att bistå med värdering av bolaget samt agera rådgivare. Investmentbanker agerar som en slag företagsmäklare som är antingen på köp- eller säljsidan av transaktionen. När bolagsvärdet presenteras i en pitch till säljarna brukar de använda sig av flera metoder för att beräkna värdet av företaget. För att öka precisionen i värdering av ett nordiskt industribolag ställdes frågan om multipel regressionsanalys kunde användas som ett komplement i en bolagsvärdering och hur den stod sig gentemot en klassisk värderingsmetod som Precedent Transactions Analysis. Dessa frågor kom att analyseras och besvarades genom att skapa en regressionsmodell som byggde på data hämtad från företagens finansiella rapporter. Den insamlade datan byggde på 132 transaktioner av nordiska industribolag under perioden 2009-2019. Regressionsmodellen kom sedan att jämföras mot en PTA-värdering som byggde på tidigare företagstransaktioner av bolag med liknande finansiell och affärsmässig bakgrund som bolaget i fråga skulle värderas. Denna studie visar på att regressionsanalys kan användas som en komplement till de olika värderingsmetoderna men bör ej användas för att värdera nordiska industribolag med avhandlingens val av variabler och skall inte ersätta någon av de klassiska värderingsmetoderna. / Prior to a company being sold or acquired they usually contact an investment bank to support with the valuation of the company, execute the sale and act as advisors for the actors that wish to buy or sell. Investment banks acts as a kind of company broker which is either on the buy or the sell side. When the company value is presented, they usually utilize several methods to calculate the value of the company. During the last decade the frequency of transactions on the Nordic industry market have increased significantly. To increase the precision in the valuation of a Nordic industrial company, the question was asked if multiple regression analysis could be used as a valuation method? Also, how did it compare itself against a classical valuation method like Precedent Transaction Analysis? These questions came to be analyzed and answered by creating a regression modell built of data gathered from financial reports. The regression model then came to be compared to the PTA-valuation which built on previous company transactions with companies that were alike in financial background. This study shows that regression analysis could be used as a complement to the different valuation methods. However the model should not be used to evaluate Nordic industrial companies with the choice of variables in the thesis, since the reliability of the model is unpredictable. Regression analysis as a stand-alone valuation method should be taken with great caution and not replace neither of the classical valuation methods.

Den psykiska ohälsan har ökat bland befolkningen i Sverige vilket förutom ett personligt lidande ger stora samhällsekonomiska konsekvenser. Orsaken till denna ökning har inget definitivt svar men kan potentiellt förklaras av makro- och socioekonomiska faktorer. Denna rapport undersöker därför om det finns ett samband mellan psykisk ohälsa och makro- och socioekonomiska faktorer. Det sker även en analys av hur dessa faktorer kan förklara ökningen av psykiska ohälsa. För att ta reda på om ett samband existerar utförs en multipel linjär regressionsanalys där den beroende variabeln definieras som svåra besvär av oro, ängslan och ångest och de förklarande variablerna utgörs av förgymnasial-, gymnasial- och eftergymnasial utbildning, BNP per capita, hushållens disponibla inkomst och arbetslöshet. Analysen delas upp i grupperna kvinnor, män och totala befolkningen där data från åren 2002-2017 används. Analysen visar på ett visst samband mellan de olika regressionsvariablerna och makro- och socioekonomiska faktorer. Totala befolkningens psykiska ohälsa har framförallt ett samband med förgymnasial utbildning. De signifikanta variablerna för kvinnors psykiska ohälsa är gymnasial utbildning, BNP per capita och disponibel inkomst. För modellen för mäns psykiska ohälsa är arbetslöshet och disponibel inkomst mest signifikanta. Modellerna har approximativt uppfyllda antaganden och multikollinearitet närvarande vilket bidrar till en bristande tillförlitlighet. Vidare forskning krävs för ytterligare validering av sambanden samt för en djupare förståelse av makro- och socioekonomiska faktorers påverkan och möjliga orsakssamband. / The mental health has increased in Sweden, which besides the personal suffering affects both the society and economy. The reason behind the increase does not have any definite explanation but the answer may, at least partly, be found in macroeconomic and socioeconomic factors. This report will therefore investigate if there exists a relationship between mental health problems and macroeconomic and socioeconomic factors. An analysis of how these factors may explain the increase of mental health problems is also performed. To see if a relationship exists, a multivariable regression analysis is performed, where the dependent variable is defined as severe problems with anxiety and worry. The regression variables are education level, GDP per capita, the households disposable income and unemployment. The analysis is performed on the groups; women, men and total population and the data is collected over the years 2002 to 2017. The analysis indicates a certain relationship between the different macro and socioeconomic variables and mental health problems. For the total population, education level is the most significant. For women, education level, GDP per capita and the households disposable income are most important. For men, unemployment and disposable income are the strongest correlated variables. The models approximately fulfills the assumptions for the least square method and have multicollinearity present, which in total makes them less reliable. Further research to validate these relationships and to contribute to explanations of potential causality is needed.

This report investigates what the main price drivers are for commercial real estate rentals in Stockholm and Gothenburg. The mathematical method applied in this thesis is multiple linear regression and statistical analysis. The models are built from data provided by Datscha, a commercial market information provider. The data sets contains 922 observations across 9 different metrics from 2019. The response variable used to explain the price drivers is taxated monthly rental. The most significant driving variables common to all three final models where market value, location, and taxated value. These results align with current macroeconomic theory; revenue streams stand in direct proportion to underlying asset, i.e market value. Furthermore, location stands out as significant due to its attractiveness to all interacting entities. The models constructed had satisfying predictabilty, with R2-values ranging from 0.725 − 0.896. / Denna rapport undersöker de signifikanta faktorer som driver prisnivå vid uthyrning av kommersiella fastigheter i Stockholm och Göteborg. De matematiska metoder som tillämpas är multipel regressionsanalys samt statistisk analys. Modeller bygger på data från Datscha, en kommersiell leverantör av fastighetsrelaterad marknadsinformation. Slutgiltigt dataset har 922 observationer över 9 variabler, alla från år 2019. Den responsvariabel som används för att förklara prisdrivarna är taxerad månatlig hyra. De mest signifikanta drivande variablerna som är gemensamma för samtliga tre slutmodeller är marknadsvärde, plats samt taxerat värde. Rapportens resultat ligger i linje med kontemporär makroekonomisk teori; intäktsflöden står i direkt proportion till den underliggande tillgången, dvs. här fastighetens marknadsvärde. Vidare är variabeln plats signifikant givet fördel med närhet för samtliga inblandade entiteter. De konstruerade modellerna innehar satisfierande prediktabilitet, med R2-värden mellan 0.725 − 0.896.