Prediction of airline fares within the United States including Alaska & Hawaii is required for transportation mode choice modeling in impact analysis of new modes such as NASA's Small Airplane Transportation System (SATS). Developing an aggregate cost model i.e. a 'generic fare model' of the disaggregated airline fares is required to measure the cost of air travel. In this thesis, the ratio of average fare to distance i.e. fare per mile and average fare is used as a measure of this cost model. The thesis initially determines the Fare Class categories to be used for Coach and Business class for the analysis .The thesis then develops a series of 'generic fare models' using round trip distance traveled as an independent variable. The thesis also develops a set of models to estimate average fare for any origin and destination pair in the US. The factors considered by these models are: the round trip distance traveled between the origin (o) and destination (d), the type of fare class chosen by the traveler (first, business class and unrestricted coach class and restricted coach class), the type of airport (large hub, medium hub, small hub, or non hub), whether or not the route is served by a low cost airline and the airline market concentration between the o-d pair. The models suggest that competition at the destination airport is more critical than the competition at origin airport for coach class fares and vice a versa for business class fares. Models suggested in this thesis predict air fares with R-square values of 0.3 to 0.75. / Master of Science

Sewer pipelines are deteriorating due to aging and sub optimal material selections, leading to the infiltration of clean ground and rainfall water into the pipes. It is estimated that a significant portion (up to 40-50%) of the water entering wastewater treatment plants is actually clean infiltrated water. This infiltration not only contributes to unnecessary energy consumption but also poses the risk of flooding the sewer network and treatment plants. Finding these broken pipes is utmost importance but is not straight forward due to the pipes being located a few meters below ground. There exist methods of pinpointing where these leaks occur, but they are often time consuming and expensive. This thesis seeks to address the following question; Can the estimation of infiltration be accomplished solely through the temperature data obtained from discrete pump stations, or is the inclusion of precipitation data essential for achieving accurate results? Two machine learning algorithms are investigated to solve the regression problem of estimating the amount of rainfall derived infiltration. The first model is a classical linear regression model. The second model is a Convolutional neural network (CNN). Both of these models are trained on the same data set. The temperatures recorded at the stations are reliable and can be trusted. However, the data labeling process involves utilizing calculated flows to the stations during both dry and wet weather periods. This means that the labels of the data cannot be trusted to be the actual ground truth, and there exists an uncertainty in the data set. Both models manage to capture large temperature drops which indicates infiltration has occurred. The linear regression model seems to be too sensitive towards small temperature drops and predicts infiltration when there is none. The CNN model on the other hand seems to be able to capture only large temperature drops when infiltration occurs. However, both models are trained with data from only one station, this means that the models are biased towards the average temperature of that particular station, other stations may have a higher or lower average temperature. When testing the models on a different station with lower average temperature the models predict infiltration when there is none.

Wind energy is rapidly expanding worldwide, and it is common practice to maximize production by selecting sites with higher wind potential. To perform critical operations such as wind flow modeling, wind turbine micro placement, annual energy yield calculation, and cost of energy estimation, a thorough understanding of a site's wind resource is required. The present study examines data from three independent wind measurement systems to see how measured data depends on the choice of the measurement system and how this might forecast the wind resource and, consequently, the energy output of a potential wind farm.  The present analysis uses three measurement units, one meteorological mast (met mast), and ground-based AQ510 Sound Detection And Ranging (SoDAR) & SoDAR and ZX 300 Light Detection And Ranging (LiDAR) devices to capture wind data for nearly a year. This study describes the operating concept of remote sensing devices such as AQ510 SoDAR and ZX 300 LiDAR, the linear regression relationship between wind speed measured on the Met Mast versus SoDAR, Met Mast versus LiDAR, and SoDAR versus LiDAR. Additionally, an understanding of stratification for this potential wind farm’s site is explored for specific days during spring, summer, and winter.  The results of the intercomparison study among Met Mast, SoDAR & LiDAR show quite a good relationship between the different measurement systems, being the correlation coefficient between the mast and the LiDAR measurements being slightly larger than between the mast and the SoDAR measurements. Comparison during the stability and instability regimes show a larger difference in some cases. Python and MS Excel are used to build data filtering procedures, the Richardson number, and comparison computations.

Arctic warming is assumed to be four times the global warming. A published study by Ivanov et al. (2019) shows that the annual average temperature of Franz Joseph Land (the world’s northernmost island region, a Russian territory) has increased by 5.2 °C from 2000-2017. This result supported the idea of determining whether Svalbard (Norwegian territory) is experiencing similar warming. Svalbard has historically been an attractive research center for examining climate change in the Arctic. Due to easier accessibility, the vast majority of weather stations have been located on the western part of the main island, Spitsbergen, which does not provide a representative picture of the entire archipelago. Therefore, this project has focused on eastern Spitsbergen. Data from six stations have been processed to analyze the temperature changes based on linear regression (the same method as at Franz Joseph Land). As eastern Spitsbergen has never been a priority, only short datasets are available, with the longest one dating from 2009. Because of this, no statistically significant result could be elucidated. Instead, data from Longyearbyen, which is located southwest were implemented, allowing analysis over the same period as Franz Joseph Land (2000-2017). This result suggested a temperature increase of 5.6 °C for the same period, with a statistical significance of P = 0.13, as well as that the winters are extra vulnerable to warming. The stations from eastern Spitsbergen’s local variability were also examined, which showed that the local climate varies although the stations are relatively close. Among others, Pyramiden seemed to be most affected by the lapse rate feedback, meaning a significant strong warming at the surface.

The purpose of this bachelor thesis is to investigate how different variables impact voice and data traffic for a telecom operator that operates in an undisclosed Sub-Saharan African country. The data has been provided by said company. The models, generated by using multivariate linear regression analysis, have a high explanatory power, as evidenced by high coefficients of determination. However, it is important to recognize the persistence of certain systematic issues, which are most likely due to the absence of key explanatory variables. Addressing these limitations in future research efforts will lead to a more comprehensive understanding of the subject and more robust findings to determine which factors drive voice and data traffic. In the report, the telecommunication sites are segmented based on generated income. Two segmentation models were created to categorize sites based on their data and voice revenue quartiles. A color matrix was used to depict the results. The hypothesis that nearby sites are more likely to perform similarly was tested using a quartile-based scoring method. The regression analysis uncovered significant variables and revealed information about the relationship between various factors and data and voice traffic. The regression residuals were analyzed using qualitative cluster analysis, which revealed distinct clustering patterns. Overall, the study provides useful insights into data and voice traffic segmentation and performance analysis in the analyzed region. / Syftet med detta kandidatarbete är att undersöka hur olika variabler påverkar röst- och datatrafik för en telekom-operatör som är verksam i ett Subsahariskt afrikanskt land. Studien använder sig av linjär regressionsanalys för att utveckla modeller som visar med en bra förklaringsgrad. Förklaringsgraden visas genom höga determinationskoefficienter. Men, trots ett bra resultat är det viktigt att ta hänsyn till systematiska problem hos modellerna. problemen beror troligtvis på att viktiga förklarande variabler saknas i datan. Framtida forskningsinsatse bör därför sträva efter att åtgärda dessa begränsningar, och på så sätt uppnå en mer omfattande förståelse av ämnet och mer korrekt resultat. I rapporten segmenteras telekommunikationsmasterna baserat på genererad inkomst. Två segmenteringsmodeller har utvecklats för att kategorisera masterna enligt deras kvartiler för data- och röstintäkter. Resultaten visas visuellt med hjälp av en färgmatris. Dessutom prövades hypotesen att närliggande master uppvisar liknande prestanda med hjälp av en kvartilsbaserad poängmetod. Regressionsanalysen identifierar signifikanta variabler och ger insikter i relationen mellan olika faktorer mellan data- och rösttrafik. Vidare upptäcks, via kvalitativ klusteranalys av regressionsresterna, tydliga klustringsmönster i resultatet. Sammantaget ger denna studie värdefulla insikter i data- och rösttrafiksegmentering samt prestandaanalys i den analyserade regionen.

Knowledge of battery health is very important. It provides insight into the capacity of a given system and allows the operators to plan ahead more efficiently. But measuring state of health (SoH) of a battery is difficult, and takes time. More importantly, the battery needs to be taken out of operation to be analysed correctly. This paper aims to evaluate a proposed linear regression method for predicting battery health, based on easily acquired operational data. The main predictor being voltage deviation, a characteristic of battery voltages during charge/discharge cycles. Using this method, the only time a battery would need to be extracted is to gather training data. Then, the model could be used for similar batteries to predict their SoH. Meaning those systems would never need to be halted, increasing productivity. The results of this paper is that the data used was not suitable for linear regression. There were problems with heteroskedasticity and non-normality of the residuals, but mainly the estimated parameter for the relationship between voltage deviation and SoH ran contrary to established theory. Which could not be overlooked. Therefore, the estimated models should not be used to predict SoH. To accomplish the goal of accurate SoH prediction, more research should be conducted and a better sample used.

Agrivoltaic system combines solar energy and agriculture which is an effective way to utilize the lands full potential. Crops can be grown between vertical panels or under tilted panels among other designs. Combining solar panels and agriculture leads to optimization of space. This degree project evaluates a comparison of several parameters measured both in a vertical agrivoltaic system and a reference control plot located in Kärrbo Prästgård, Västerås, Sweden. Specifically, the correlation between the ground albedo and normalized difference vegetation index (NDVI) under the two treatments are investigated. Correlations between the albedo and the NDVI against different weather parameters are also explored. Linear regression models are developed separately for the albedo and NDVI with the most correlated parameters. In addition, because the albedo in the reference is not the same as the albedo in the agrivoltaic system, a linear regression model linking the albedo of the agrivoltaic system, and the albedo of the reference system is further developed. With this latter model, power production from the vertical agrivoltaic system is simulated under different albedo considerations: using measured albedo from the agrivoltaic system, using predicted albedo from the linear regression model, and using measured albedo from the reference system. These power estimations are then compared to the real power production from the agrivoltaic system. The study employs MATLAB for data analysis, models development and power production simulations.   The study compared the correlation between ground albedo and NDVI in an agrivoltaic system and a control plot. The albedo model revealed that the reference system could explain 87% of the albedo variance in the agrivoltaic system, but the NDVI model showed that the reference system could only account for 39.6% of the variation in the agrivoltaic system. Furthermore, for the power production comparison, using the actual measured albedo in the agrivoltaic showed the most accurate power, employing predicted albedo through the linear regression model showed the second highest and using the albedo measured in the reference showed the least accurate.

Allt eftersom klimatkrisen fortskrider ökar engagemanget kring hållbarhet inom företag. Växthusgaser är ett av de största problemen och matsvinn har därför fått mycket uppmärksamhet sedan det utnämndes till den tredje största bidragaren till de globala utsläppen. För att minska sitt bidrag rabatterar många matbutiker produkter med kort bästföredatum, vilket kommit att kräva en förståelse för hur priskänslig efterfrågan på denna typ av produkt är. Prisoptimering görs vanligtvis med så kallade Generalized Linear Models men då efterfrågan är ett komplext koncept har maskininl ärningsmetoder börjat utmana de traditionella modellerna. En sådan metod är Random Forest Regression, och syftet med uppsatsen är att utreda ifall modellen är bättre på att estimera efterfrågan baserat på rabattnivå än en klassisk linjär modell. Vidare utreds det ifall ett tydligt linjärt samband existerar mellan rabattnivå och efterfrågan, samt ifall detta beror av produkttyp. Resultaten visar på att Random Forest tar bättre hänsyn till det komplexa samband som visade sig finnas, och i detta specifika fall presterar bättre. Vidare visade resultaten att det sammantaget inte finns något linjärt samband, men att vissa produktkategorier uppvisar svag linjäritet. / As the climate crisis continues to evolve many companies focus their development on becoming more sustainable. With greenhouse gases being highlighted as the main problem, food waste has obtained a great deal of attention after being named the third largest contributor to global emissions. One way retailers have attempted to improve is through offering close-to-date produce at discount, hence decreasing levels of food being thrown away. To minimize waste the level of discount must be optimized, and as the products can be seen as flawed the known price-to-demand relation of the products may be insufficient. The optimization process historically involves generalized linear regression models, however demand is a complex concept influenced by many factors. This report investigates whether a Machine Learning model, Random Forest Regression, is better at estimating the demand of close-to-date products at different discount levels than a basic linear regression model. The discussion also includes an analysis on whether discounts always increase the will to buy and whether this depends on product type. The results show that Random Forest to a greater extent considers the many factors influencing demand and is superior as a predictor in this case. Furthermore it was concluded that there is generally not a clear linear relation however this does depend on product type as certain categories showed some linearity.

Soft robotic sensors (SRSs) are a class of pliable, passive sensors which vary by some electrical characteristic in response to changes in geometry. The properties of SRSs make them excellent candidates for use in wearable motion analysis technology. Wearable technology is a fast-growing industry, and the improvement of existing human motion analysis tools is needed. Prior research has proven the viability of SRSs as a tool for capturing motion of the foot-ankle complex; this work covers extensive effort to improve and ruggedize a lab tool utilizing this technology. The improved lab tool is validated against a camera-based motion capture system to show either improvement or equivalence to the previous prototype while introducing enhanced data throughput, reliability, battery life, and durability.

The role of motivation and its relationship with desired outcomes has been studied in a variety of contexts as evidenced in the literature. Motivation, its origin, type, and its effect, has been theorized to range from non-existent to the main driving force behind all behavior. Self-determination theory, a more recent motivational theory, posits that motivation is a driving force of behavior; however, the amount of control one has to perform freely a given task determines whether this motivation is internally (autonomously) generated or externally (controlled) generated. The idea of motivation affecting outcomes is clearly evidenced in research geared toward finding the role of motivation on satisfaction of a given job, task, or assignment. This research reviewed studies that focused on motivation and its role on job satisfaction. A theoretical thread was postulated that intrinsic motivation is as good as, if not better in most instances, than extrinsic motivation in determining job satisfaction. Also, job satisfaction leads to greater lengths of tenure in a given job. Both of these statements were affirmed from a review of the literature. However, one question remains: what type of intrinsic motivation factors best correlate to job satisfaction (and its potential effect of improving tenure)? Therefore, the overall objective of this study is to determine whether various forms of intrinsic motivation correlate with an employee’s satisfaction with their job or career. The study was conducted using a survey method that incorporated the participation of 172 participants from two very similar psychiatric hospitals in the southeastern United States. Multiple linear regression was used to determine if any relationship existed between three intrinsic motivation factors (autonomy, competence, and relatedness) and job satisfaction. The results of this study suggest that positive relationships do exist between that of autonomy and relatedness intrinsic motivation factors and job satisfaction scores. The combined predictor factors (autonomy, competence, and relatedness) yielded an R2 = .145, indicating that almost 15% of the total job satisfaction scores can be explained by these three variables. Additional, exploratory regression analyses were conducted using experimental statements and selected demographic information. Conclusions and recommendations for future research are also given.