Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas.

The Mississippi River Valley Alluvial Aquifer (MRVAA) is one of the most productive agricultural regions in the United States. The main objectives of this research are to identify long term trends and change points in hydrological variables (streamflow and rainfall), to assess the relationship between hydrological variables, and to evaluate the influence of global climate indices on hydrological variables. Non-parametric tests, MMK and Pettitt’s tests were used to analyze trend and change points. PCC and Streamflow elasticity analysis were used to analyze the relationship between streamflow and rainfall and the sensitivity of streamflow to rainfall changes. PCC and MLR analysis were used to evaluate the relationship between climate indices and hydrological variables and the combined effect of climate indices with hydrological variables. The results of the trend analysis indicated spatial variability within the aquifer, increase in streamflow and rainfall in the Northern region of the aquifer, while a decrease was observed in the southern region of the aquifer. Change point analysis of annual maximum, annual mean streamflow and annual precipitation revealed that statistically decreasing shifts occurred in 2001, 1998 and 1995, respectively. Results of PCC analysis indicated that streamflow and rainfall has a strong positive relationship between them with PCC values more than 0.6 in most of the locations within the basin. Results of the streamflow elasticity for the locations ranged from 0.987 to 2.33 for the various locations in the basin. Results of the PCC analysis for monthly maximum and mean streamflow showed significant maximum positive correlation coefficient for Nino 3.4. Monthly maximum rainfall showed a maximum significant positive correlation coefficient for PNA and Nino3.4 and the monthly mean rainfall showed a maximum significant positive correlation coefficient of 0.18 for Nino3.4. Results of the MLR analysis showed a maximum significant positive correlation coefficient of 0.31 for monthly maximum and mean streamflow of 0.21 and 0.23 for monthly maximum and mean rainfall, respectively. Overall, results from this research will help in understanding the impacts of global climate indices on rainfall and subsequently on streamflow discharge, so as to mitigate and manage water resource availability in the MRVAA underlying the LMRB.

Road traffic signs provide regulatory, warning, guidance, and other important information to road users to prevent hazards and road accidents. Therefore, the traffic signs must be detectable, legible, and visible both in day and nighttime to fulfill their purpose. The nighttime visibility is critical to safe driving on the roads at night. The state of the art gives clear evidence that the retroreflectivity improves the nighttime visibility (detectability and legibility) of the road traffic signs and that the nighttime visibility can be improved by using an adequate level of retroreflectivity. Furthermore, nighttime visibility can be affected by human, sign, vehicle, environmental, and design factors.  The retroreflectivity and colors of the road signs deteriorate over time and thus the visibility worsens, therefore, maintaining the road signs is one of the important issues to improve the safety on the roads.  Thus, it is important to judge whether the retroreflectivity and colors of the road sign are within the accepted levels for visibility and the status of the signs are accepted or not and need to be replaced.  This thesis aims to use machine learning algorithms to predict the status of road signs in Sweden. To achieve this aim, three classifiers were invoked: Artificial Neural Network (ANN), Support Vector Machines (SVM), and Random Forest (RF). The data which was collected in Sweden by The Road and Transport Research Institute (VTI) was used to build the prediction models. High accuracy was achieved using the three algorithms (ANN, SVM, and RF) of 0.84.3, 0.93, and 0.98, respectively. Scaling the data was found to improve the accuracy of the prediction for all three models and better accuracy is achieved when the data was scaled using standardization compared with normalization. Additionally using principal component analysis (PCA) has a different impact on the accuracy of the prediction for each algorithm. Another aim was to build prediction models to predict the retroreflectivity performance of the in-use road signs without the need to use instruments to measure the retroreflectivity or color. Experiments using linear and logarithmic regression models were conducted in this thesis to predict the retroreflectivity performance. Two datasets were used, VTI data and another data which was collected in Denmark by voluntary Nordic research cooperation (NMF group). The age of the road traffic sign, the chromaticity coordinate X for colors, and the class of retroreflectivity were found significant to the retroreflectivity in both datasets.  The logarithmic regression models were able to predict the retroreflectivity with higher accuracy than linear models. Two suggested logarithmic regression models provided high accuracy for predicting the retroreflectivity (R2 of 0.50 on VTI data and 0.95 on NMF data) by using color, age, class, GPS position, and direction as predictors. Nearly the same accuracy (R2 of 0.57 on VTI data and 0.95 on NMF data) was achieved by using all parameters in the data as predictors (including chromaticity coordinates X, Y for colors). As a conclusion, omitting chromaticity coordinates X, Y for colors from the logarithmic regression models does not affect the accuracy of the prediction.

El principal indicador para evaluar la calidad del suelo como subrasante en el diseño de pavimentos es la capacidad de soporte CBR. En muchos casos, no es posible su obtención mediante ensayos, al menos en la frecuencia requerida, y son muy costosos. Por ello, la necesidad de cuantificar este parámetro mediante modelos matemáticos que utilicen propiedades fácilmente determinables y permitan evaluar rápidamente la eficacia de una solución de estabilización. En el presente trabajo de investigación tiene como propósito desarrollar herramientas prácticas para la predicción del valor de CBR del suelo expansivo post estabilización con ceniza de cáscara de arroz (CCA) y cal. Se plantea obtener modelos matemáticos basados en la regresión lineal múltiple haciendo uso de sus propiedades índice (%F, IP) y de compactación (OCH, MDS), los cuales se generaron mediante la aplicación del software SPSS Statistics, cuya ecuación resultante fue: 〖CBR〗_f=46.116-0.526 %F+0.034 IP+0.218 OCH+5.06 MDS Esta ecuación presenta una correlación muy alta con R = 0.975 y un ajuste de bondad excelente de R2 = 0.95. Esto quiere decir que la variable de respuesta CBR es explicada en un 95% por las variables predictoras %F, IP, OCH y MDS. El modelo de regresión propuesto se aplicó a un tramo de la carretera PE-8B en la región San Martín donde se observó que el valor de CBR se incrementa en promedio 272% al estabilizarse con los agentes de estudio sugeridos. / The main indicator to evaluate the quality of the soil as a subgrade in pavement design is the California Bearing Ratio (CBR). In many cases, it is not possible to obtain them by testing, at least at the required frequency, and they are very expensive. Therefore, the need to quantify this parameter through mathematical models that use easily determinable properties and will evaluate the effectiveness of a proposed stabilization solution. The purpose of this research work is to develop practical tools for the prediction of the CBR in expansive soil post stabilization with rice husk ash and lime. It is proposed to obtain mathematical models based on multiple linear regression using their index (% F, IP) and compaction (OCH, MDS) properties, which were generated by applying the SPSS Statistics software, whose resulting equation was: 〖CBR〗_f=46.116-0.526 %F+0.034 IP+0.218 OCH+5.06 MDS, which presents a very high correlation with R = 0.975 and an excellent goodness fit of R2 = 0.95. This means that the CBR response variable is 95% explained by the predictor variables %F, IP, OCH and MDS. The proposed regression model was applied to a section of the PE-8B highway in the San Martín region where it was found that the CBR value was found on average 272% when stabilized with the suggested study materials. / Tesis

This thesis examines which factors drive overall net inflows to a Swedish niche bank. It further investigates whether these factors are the same or different from the factors that drive net inflows to mutual funds as well as shares. To find the key factors, and to what degree they drive the different net inflows, three separate multiple linear regressions were performed. The data analysed was taken from the period January 2018 to February 2022, and was provided by Avanza Bank. The data for the driving factors were gathered from different sources online. 21 regressors were used for this analysis. The thesis conclusion in brief was that for total net inflows the main contributor was the number of customers, which positively impacted the net inflows. The two subcategories: mutual fund and stock inflows were more volatile and the number of customers proved not as important in these cases. Some seasonal patterns were recognized, e.g. January was always a significant month for total net inflows. Therefore, performing a time series analysis would be recommended to draw further conclusions. Other possible avenues for future research is to gain a deeper understanding of this applied area of mathematics and to gather more data both in terms of the analysed time period and number of regressors. / Detta arbete ämnar undersöka vilka faktorer som generellt driver nettoflöden till en svensk nischbank. Arbetet ämnar även att vidare undersöka om dessa eller andra faktorer är mest drivande för nettoflöden till kategorierna fonder och aktier. För att hitta dessa nyckelfaktorer, samt till vilken grad de driver nettoflöden, utfördes tre stycken regressionsanalyser. Datan som analyserades avsåg tidsperioden januari 2018 till februari 2022 och sammanställdes av Avanza Bank. Samtliga potentiella nyckelfaktorer för nettoinflödet samlades in från diverse källor online, totalt användes 21 stycken regressorer för analysen. Arbetets slutsats i korthet var att det för det totala nettoflöden är bankens totala antal kunder som är den största drivande faktorn, vilket har ett positivt samband med den beroende variabeln. För de två sub-kategorierna, nettoflöden till fonder och aktier var det svårare att bygga en modell och antal kunder visade sig inte ha en stor påverkan för dessa. Ett säsongsmönster kunde observeras i datan, exempelvis var januari alltid en signifikant månad för stora nettoflöden. Med detta som bakgrund kunde ett tidsserieanalys rekommenderas för att kunna dra bättre slutsatser inom ämnet. Andra möjliga alternativ för framtida forskning innefattar en djupare analys inom detta område av tillämpad matematik samt insamling av mer data både i fråga om den studerade tidsperioden samt antalet använda regressorer.

Investing in public real estate stocks can diversify a stock portfolio due to the nature of these companies. The industry is generally less sensitive to economic downturns and spikes in inflation are offset by increased real estate property and rent prices. Nevertheless, measures of the wider economy could be used as predictors of the real estate stock market.  This thesis attempts to model the Swedish real estate stock market with the index SX35PI (Stockholm Real Estate PI) using the fundamental economic factors and repo rate. Data was collected and formatted to a monthly interval for the period February 2012 to December 2021. This resulted in an exponential multiple regression model that used all the regressors that explained 95.7% of the variation in SX35PI, and an alternative autoregressive forecasting model that explained 82.3% of the variation in SX35PI. / Investeringar i fastighetsbolag kan diversifiera en aktieportfölj tack vare dessa bolags karaktär. Denna industri är nämligen mindre känslig för ekonomiska nedgångar och minskad efterfråga samt plötsliga ökningar i inflationen som vägs upp av ökningar i fastighetspriser och hyror. Aktiemarknaden för fastighetsaktier kan modelleras med makroekonomiska mått. Denna rapport försöker modellera aktiemarknaden för svenska fastighetsbolag med fundamentala ekonomiska mått samt reporäntan. Data samlades och transformerades för att få datapunkter varje månad under februari 2012 till december 2021. Resultatet blev en exponentiell multipel regressionsmodell som använde alla förklarande variabler vilka förklarade 95.7% av variationen i SX35PI, och en alternativ autoregressiv modell som förklarade 82.3% av variationen i SX35PI.

This thesis is a case study in collaboration with the company Getswish AB. GetswishAB provides the mobile application and payment service Swish with the purpose ofdelivering smooth money transfers for individuals and companies in Sweden. About80 percent of the Swedish population are connected to Swish, and the majority seethe service as an apparent part of everyday life. This work studies a small part of alltransactions that take place daily between individuals and companies. Specifically, thispaper examines which factors affect the Swish transaction amount (TA) to companieswithin five different industries. The five industries studied are: Sports, leisure,and entertainment activities; Restaurant, catering, and bar activities; Retail trade,except for motor vehicles and motorcycles; Trade and repair of motor vehicles andmotorcycles; and Telecommunications. In combination with descriptive analysis andseasonality studies, a multiple linear regression model is used to evaluate patternsin the amount transferred to companies within the various industries. The responsevariable is the daily aggregated TA and the seven responding regressors examined are:i) The number of employees of the company, ii) The revenue of the company, iii) Thedate for registration to Swish service for companies, iv) The age of the customers, v) Thegender of the customers, vi) The number of transactions, and vii) The transaction date.The estimated parameters for each regressor are studied to evaluate correlations withthe TA. This thesis states that it is possible to construct a model from the regressorsanalyzed, which can predict the amount with an explanation degree of above 85% forfour of the five industries. The model constructed for the motor vehicle industry nevergives satisfactory results and must be further investigated to conclude. / Detta examensarbete är en fallstudie i samarbete med företaget GetSwish AB.GetSwish AB tillhandahåller mobilapplikationen och betaltjänsten Swish, vars syfteär att leverera smidig pengaöverföring för privatpersoner och företag i Sverige. Idagär cirka 80 procent av Sveriges befolkning anslutna till Swish och majoriteten sertjänsten som en självklar del av vardagen. Detta arbete kommer dock endast fokuserapå en liten del av alla transaktioner som dagligen sker mellan privatpersoner ochföretag. Specifikt undersöker denna rapport vilka faktorer som påverkar Swishstransaktionsbelopp till företag inom fem olika branscher. De fem branschernasom studeras är: Sport-, fritids- och nöjesverksamhet; Restaurang-, catering ochbarverksamhet; Detaljhandel utom med motorfordon och motorcyklar; Handelsamt reparation av motorfordon och motorcyklar; och Telekommunikation. Ikombination med en deskriptiv analys och säsongsstudier skapades en multipel linjärregressionsmodell för att utvärdera mönster i transaktionsbeloppet från kund tillföretag inom de olika branscherna. Responsvariablen är det dagliga aggregeradebeloppet och de förklarande variablerna som undersöktes var: antalet anställda,omsättning, datum för registrering till Swish för företag, kundernas ålder och könsamt antal transaktioner och transaktionsdatum. De skattade parametrarna förvarje regressor studerades för att utvärdera magnitud samt positiva eller negativakorrelationer med beloppet. Denna rapport visar att det är möjligt att konstrueraen modell från de analyserade regressorerna som kan förutsäga beloppet med enförklaringsgrad på över 85% för fyra av de fem branscherna och kan användas föratt förutspå beloppen på de dagliga transaktionerna. Modellen som konstruerats förfordonsindustrin gav aldrig tillfredsställande resultat och bör undersökas vidare innanslutsatser dras.

In general, insurance companies and especially their clients face long and complicated claims processes where payments rarely, and almost reluctantly, are made the same day. A part of this slow moving procedure is the fact that in some cases the insurer has to value the personal property themselves, which can be a tedious process. In conjunction with the insurance company Hedvig, this project address this issue by examining a pricing model for a specific personal property; smartphones - one of the most common occurring claim types in the insurance context. Using multiple linear regression with data provided by PriceRunner, 10 key characteristics out of 91 where found to have significant explanatory power in predicting the market price of a smartphone. The model successfully simulates this market price with an explained variance of 90%. Furthermore this thesis illustrates an intuitive example regarding pricing models for personal property of other sorts, identifying limiting key components to be data availability and product complexity. / I dagsläget står försäkringsbolag och deras kunder allt för ofta inför långa och komplicerade försäkringsärenden, där utbetalningar i regel aldrig sker samma dag. En del i denna långsamma och utdragna utbetalningsprocess är det faktum att försäkringsbolaget på egen hand måste uppskatta egendomens värde, vilket kan vara en mycket komplicerad process. I samarbete med försäkringsbolaget Hedvig undersöker denna rapport en värderingsmodell för ett av de vanligaste försäkringsärendena gällande personlig egendom, nämligen smartphones. Genom att använda multipel linjär regression med data försedd av PriceRunner har 10 av 91 nyckelfaktorer identifierats ha signifikant förklaringsgrad vid modellering av marknadsvärdet av en smartphone. Den framtagna modellen simulerar framgångsrikt marknadsvärdet med en 90-procentig förklaringsgrad av variansen. Vidare illustrerar denna rapport intuitiva riktlinjer för värderingsmodellering till andra typer av personlig egendom, samtidigt som den identifierar begränsande nyckelaspekter som exempelvis tillgången på data och egendomens inneboende komplexitet.

The growing interest in savings on the financial markets implicates that the competition is expanding and managers of Swedish equity funds need to create shareholder value, independent of the macroeconomic situation. The Swedish financial market experienced a rapid rebound during the first quarter of 2019, following the plunge in the preceding quarter. This thesis utilizes multiple linear regression to analyze Swedish equity funds during the first quarter of 2019. The aim is to identify variables affecting fund performance in a market rebound in order to formulate a performance maximizing strategy. Based on the results of the performance influencing variables, the strategy is to underweight small cap stocks, overweight the energy and technology sector, underweight the communication services sector and staying neutral to overweighted in remaining sectors. Furthermore, the strategy proposes an overweighted exposure to North American stocks and an underweight to Western European stocks. The overexposure to North America should be larger in absolute value compared to the underexposure to Western Europe. The strategy is ambiguous since data from only one market rebound is analyzed. Therefore, the strategy is not significantly proven to be adaptable in any market rebound. The model analysis is based on modern macroeconomic and financial theories. In addition, the discussion problematizes the neoclassical view on economics based on the notion that a combination of rationality and irrationality is prevalent among investors. Further research is essential either to support or reject the performance affecting variables and the allocation strategy specified in the thesis. / Det växande intresset att investera på de finansiella marknaderna implicerar att konkurrensen hårdnar bland fondförvaltare. Fondförvaltare för svenska aktiefonder måste därmed skapa andelsägarvärde, oberoende av det makroekonomiska läget. Den finansiella marknaden återhämtade sig snabbt under det första kvartalet 2019 efter den branta nedgången under det föregående kvartalet. Studien avser att identifiera de bidragande faktorerna till avkastning för svenska aktiefonder under denna återhämtning. Multipel linjär regression används för detta ändamål samt för att formulera en avkastningsmaximerande strategi. Strategin föreslår att förvaltare för svenska aktiefonder bör undervikta småbolag, övervikta aktier inom energi och teknik samt undervikta aktier i kommunikationssektorn. Strategin är vidare att vara neutral till överviktad i övriga sektorer. Dessutom är strategin att övervikta nordamerikanska aktier och att undervikta västeuropeiska aktier. Övervikten i Nordamerika ska vara större i absoluta termer än undervikten i Västeuropa. Strategin är tvetydig då den bygger på data från enbart en marknadsåterhämtning. Därmed är den framtagna strategin inte bevisad att vara applicerbar på vilken marknadsåterhämtning som helst. Analysen är baserad på modern makroekonomisk och finansiell teori. Diskussionen problematiserar den neoklassiska synen på ekonomi baserat på uppfattningen att investerare är både irrationella och rationella i sina investeringsbeslut. Fortsatt forskning är essentiell för att antingen stärka eller förkasta dragna slutsatser i denna studie.

This project aims to analyze Nordic companies active in the sector of Information and Communications Technology (ICT), and does this in two parts. Part I entails analyzing public companies to construct a valuation model aimed at predicting the enterprise value of private companies. Part II deals with analyzing private companies to determine if there are opportunities providing excess returns as compared to investments in public companies. In part I, a multiple regression approach is utilized to identify suitable valuation models. In doing so, it is revealed that 1-factor models provide best statistical results in terms of significance and prediction error. In descending order, in terms of prediction accuracy, these are (1) total assets, (2) turnover, (3) EBITDA, and (4) cash flow. Part II uses model (1) and finds that Nordic ICT private equity does provide opportunities for positive alphas, and that it is possible to construct portfolio strategies that increase this alpha. However, with regards to previous research, it seems as though the returns offered by the private equity market analyzed does not adequately compensate investors for the additional risks related to investing in private equity. / Det här projektet analyserar nordiska bolag aktiva inom Informations- och Kommunikationsteknologi (ICT) i två delar. Del I behandlar analys av publika bolag för att konstruera en värderingsmodell avsedd att förutsäga privata bolags enterprise value. Del II analyserar privata bolag för att undersöka huruvida det finns möjligheter att uppnå överavkastning jämfört med investeringar i publika bolag. I del I utnyttjas multipel regressionsanalys för att identifiera tillämpliga värderingsmodeller. I den processen påvisas att modeller med enbart en faktor ger bäst statistiska resultat i fråga om signifikans och förutsägelsefel. I fallande ordning, med avseende på precision i förutsägelser, är dessa modeller (1) totala tillgångar, (2) omsättning, (3) EBITDA, och (4) kassaflöde. Del II använder modell (1) och finner att den nordiska marknaden för privata ICT-bolag erbjuder möjligheter för överavkastning jämfört med motsvarande publika marknad, samt att det är möjligt att konstruera portföljstrategier som ökar avkastningen ytterligare. Dock, med hänsyn till tidigare forskning, verkar det som att de möjligheter för avkastning som går att finna på marknaden av privata bolag som undersökts inte kompenserar investerare tillräckligt för de ytterligare risker som är relaterade till investeringar i privata bolag.