Approved for public release; distribution is unlimited. / This paper documents the development of a statistical analysis package for the TRS-80 microcoraputer. The package is comprised of six interactive programs which are generally divided into topical areas. The major emphasis is on exploratory data analysis and statistical inference, however, probability and inverse probability distributions are also included. The programming language is TRS-80 Level II BASIC enhanced by the input/output commands available through the ESF-80 (Exatron Stringy Floppy) mass storage subsystem. With the modification of these few commands, the package is compatible with most floppy disk operating systems designed for the TRS-80 Model I or Model III microcomputers. This statistical analysis capability implemented on a relatively inexpensive system provides a useful tool to the student or the trained analyst without ready access to a mainframe computer system. / Major, United States Marine Corps

This master‘s thesis deals with impacts on prices of sites which are meant by planning for residential housing in the landscape park Moravian Karst. In the research section of the thesis, there are defined fundamental concepts that concern themselves with the given issues and there are portrayed impacts on prices of sites according to the accessible literature. In the analytical section of the thesis, there is conducted an analysis of the real estate market of the given segment. Furthermore, there are examined and statistically evaluated impacts on prices of sites based on a multiple linear regression modelled in a statistical tool gretl. Impacts are evaluated both within a scope of the whole area and within particular municipalities. In the discussion section of the thesis, the manifested questions are answered, the stated hypotheses are tested and the obtained results are compared to the findings that were described in the research section.

In the iron ore mining fraternity, in order to achieve the desired quality in the froth flotation processing plant, stakeholders rely on conventional laboratory test technique which usually takes more than two hours to ascertain the two variables of interest. Such a substantial dead time makes it difficult to put the inherent stochastic nature of the plant system in steady-state. Thus, the present study aims to evaluate the feasibility of using machine learning algorithms to predict the percentage of silica concentrate (SiO2) in the froth flotation processing plant in real-time. The predictive model has been constructed using iron ore mining froth flotation system dataset obtain from Kaggle. Different feature selection methods including Random Forest and backward elimination technique were applied to the dataset to extract significant features. The selected features were then used in Multiple Linear Regression, Random Forest and Artificial Neural Network models and the prediction accuracy of all the models have been evaluated and compared with each other. The results show that Artificial Neural Network has the ability to generalize better and predictions were off by 0.38% mean square error (mse) on average, which is significant considering that the SiO2 range from 0.77%- 5.53% -( mse 1.1%) . These results have been obtained within real-time processing of 12s in the worst case scenario on an Inter i7 hardware. The experimental results also suggest that reagents variables have the most significant influence in SiO2 prediction and less important variable is the Flotation Column.02.air.Flow. The experiments results have also indicated a promising prospect for both the Multiple Linear Regression and Random Forest models in the field of SiO2 prediction in iron ore mining froth flotation system in general. Meanwhile, this study provides management, metallurgists and operators with a better choice for SiO2 prediction in real-time per the accuracy demand as opposed to the long dead time laboratory test analysis causing incessant loss of iron ore discharged to tailings.

The loss of 6 million U.S. manufacturing jobs since 2000 has severely affected communities that have lost a vital source of employment. Voluntary employee turnover has compounded the problem. The purpose of this correlational study was to examine the relationship between employee turnover and destructive leadership behaviors of managers in small and medium enterprise (SME) manufacturing businesses in Warren County, New Jersey. The constructive-destructive leadership model formed the theoretical framework for the study. A random sample of 96 SME manufacturing firm employees completed the destructive leadership scale (DLS), multifactor leadership questionnaire (MLQ), and the turnover intention scale (TIS-6) via an online survey. Multiple linear regression analyses and Pearson-product correlation coefficients were used to predict employee turnover. Tyrannical leadership and laissez-faire leadership were the only significant contributors to the regression model. Implications for social change include providing business managers with information needed to maintain or increase employee retention levels, which may improve employee morale, increase job satisfaction, and enhance customer satisfaction in the communities served.

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Despite much research indicating that acquisitions are unsatisfactory in generating value, in terms of stock market return, their continued and growing existence highlights that acquisitions play an essential role in the corporate landscape, and will only continue doing so moving forward.  This continuous undertaking in acquisitions despite a lacking performance inspired a thesis that is focused on viewing acquisitions through an operational perspective. The aim of the thesis is to answer the question: "What impacts the difference in operational efficiency between the group of companies, that is formed after an acquisition, and the separate companies, that operate before an acquisition?" A multiple linear regression model analysis is performed with explanatory regressors that are mainly based on the target-acquirer relation. The final model's relatively low explanatory power combined with certain model assumption violations and a challenging sample of observations harms the reliability of the significant regressors in the final model. The significant regressors are the acquirer's average annual EBIT margin growth rate before the acquisition and the cross-border regressor. The two regressors are in line with acquisition and financial accounting theory. The conclusion, however, is that analyzing acquisitions in this regard is questionable and could very well not return informative results that can be applied to future acquisitions to improve the unsatisfactory returns. / Trots att tidigare studier indikerar att företagsförvärv generellt är ofördelaktiga, speciellt ur ett aktieägarperspektiv, fortsätter de att ske.  Med hänsyn till avsaknaden av operationella perspektiv på företagsförvärv genomförs en studie med detta syfte. Den huvudsakliga frågan som besvaras i rapporten är: "vad påverkar skillnaden i den operationella effektiviteten mellan koncernen, som uppstår efter företagsförvärvet, och de separata företagen, som verkar innan företagsförvärvet?" För att besvara frågan genomförs en regressionsanalys med förklarande variabler som främst baseras på relationen mellan det uppköpta och köpande bolaget. Den slutgiltiga modellen ger ett relativt lågt förklaringsvärde, följer inte modellantagandena till fullo och är baserad på ett utmanande dataset. Detta sänker de signifikanta variablernas trovärdighet i den slutgiltiga modellen. De två signifikanta variablerna är dels den genomsnittliga årliga tillväxten i EBIT-marginal för det köpande bolaget innan förvärvet och dels en variabel som visar på om företagsförvärvet är gränsöverskridande. Resultatet överensstämmer med teorin som finns tillgänglig inom företagsförvärv och redovisning. Slutsatsen av studien är att operationella analyser av företagsförvärv kan ifrågasättas och att de generellt sett kan vara oanvändbara för att förbättra utfallen av företagsförvärv.

Public health is an essential urban issue attracting the increasing attention of scholars and decision-makers while urban planning is an important mechanism to promote health since mounting evidence has shown the planning elements, such as urban green spaces (UGS), could have a positive influence. In previous literature, the studies concerning the impacts of UGSs can be divided into two aspects: the first is to reveal the relationships between UGSs and health using statistical models based on self-reported data, while the second is to take their relationships as presumption and visualize the spatial distributions of UGSs in a geographical information system (GIS) to indicate the existence of health inequalities. I attempt to combine the two pathways, using GIS-based analyses to quantify and visualize UGSs, and subsequently employing the quantified indicators of UGSs to calculate their associations with public health by multiple linear regression (MLR) analysis in SPSS. The study describes a detailed methodology that could be taken as a generic approach to analyze other urban elements and services. A case study is conducted in Linköping central urban area with base areas selected as the spatial unit of analysis. As a consequence, although the elaborate relationships between different indicators of UGSs and public health are not directly provided by the regression models, the correlations between them are indicated to be weak and subtle and require larger samples to reveal. Potential improvements, including the application of panel data and other kinds of regression models, are also summarized for further research.

There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject.There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject. / Det finns en märkbar trend av växande turism världen över. Turismen in i Sverige får allt mer ekonomisk och social uppmärksamhet. Syftet med detta arbete är att finna vilka makroekonomiska variabler som bidrar till de årliga intäkterna av internationell turism i Sverige. För analysen används multipel linjär regression över tidsperioden 1978-2017. Det slutgiltiga resultatet visar att BNP är den dominanta makroekonomiska faktorn som är drivande i de årliga intäkterna av internationell turism i Sverige, detta oavsett tidsperiod. Valutakursen NOK/SEK verkar vara signifikant i det långa loppet, från 1978-2017, men inte under de kortare tidsperioderna. Valutakursen USD/SEK och arbetslösheten är båda icke signifikanta variabler för internationell turism konsumtion i Sverige över alla tidsperioder. Devalveringen av den svenska kronan år 1992, förändrade inte relationen mellan de sistnämnda variablerna och y -variabeln. Dock kan dessa resultat vara ostabila på grund av de begränsade antalet observationer som använts i analysen och därför rekommenderar vi andra regressions modeller till detta ämne, såsom "panel data regression".

Market multiples are a common and simple tool for estimation of corporate value. It can express temporal dynamics and differences in markets, industries and firms. Despite their practical usefulness, some critical problems remains which continue to be debated. This thesis investigates if there exists characteristics for explaining market capitalization by market multiples within the life science industry in Sweden. The approach follows well known theory of multiple linear regression analysis. The results indicated only a linear relationship between the market cap and the R\&D expenditures of a company. This does not mean that the other explanatory variables does not have effect on market cap only that there is no linear relationship that could be statistically proven. / Värderingsmultiplar är ett vanligt och enkelt verktyg för att approximera företags värde. Det kan beskriva temporär dynamik och skillnader hos marknader, industrier och bolag. Trots dess praktiska användbarhet finns en del kritiska problem som fortfarande debatteras. Denna uppsats undersöker om det existerar några egenskaper för att förklara marknadsvärdet med hjälp av värderingsmultiplar inom life science industrin i Sverige. Tillvägagångssättet följer välkänd teori om multipel linjär regressions analys. Resultaten visade att det endast finns ett samband mellan marknadsvärdet och utgifter för forskning och utveckling för ett bolag. Detta innebär inte att de andra variablerna inte har någon effekt på marknadsvärdet, utan att det inte finns ett linjärt samband som kan bevisas på ett statistiskt vis.

WiFi can provide network coverage for users on land at anytime and anywhere, but on the sea, the wireless communication scenes change dramatically due to the signals are non-existence. Although some techniques (e.g. satellite, undersea fiber, microwave communication) have been used in marine communication, they are either too expensive with very small bandwidth, or too limited in its coverage range. We propose to develop a marine wireless mesh network which is formed by low cost buoyed wireless base stations to provide broadband connectivity for users on the sea. Ocean wave simulation and prediction are key technologies in developing marine mesh network, because marine environments are dramatically different from terrestrial environment. The ocean waves have characteristics of rhythmic oscillations and the line of sight between two communication nodes is often blocked by them. Therefore, we have to develop a new wave-state-aware networking protocol which is suitable for marine environments. Ocean wave simulation technology can simulate this kind of dynamic environments and provide a test platform for the development of marine mesh network. Ocean wave prediction technology can improve the throughput of marine wireless network. Thus, they are indispensable technologies in developing marine mesh network. In this thesis, we designed an ocean wave measurement method, two ocean wave prediction methods, and an ocean wave simulation method. Firstly, we designed an accelerometer-based ocean wave measurement method. It can measure the real time wave height accurately. Secondly, we designed an Elman-neural-network-based ocean wave prediction method for nonlinear waves. It has a higher prediction accuracy than other neural network methods in nonlinear wave prediction. Thirdly, we designed a multiple-linear-regression-based ocean wave prediction method for linear waves. It has a higher prediction accuracy and less time consumption than other methods in linear wave prediction. Finally, we implemented and improved a spectrum-based ocean wave simulation method which is originally proposed by Tessendorf. It can present the movement of ocean waves realistically and in real time. To sum up, above four methods provide an effective test platform and technical support for the development of our marine mesh network. / Master of Science / With the development of wireless communication technology, WiFi has been an indispensable resource for daily work and pleasure. However, in the marine environments, WiFi is not exist. Thus, passengers and workers on the sea are eager for it. We propose to develop a marine wireless mesh network which is formed by low cost buoyed wireless base stations to provide WiFi for users on the sea. Marine environments are dramatically different from terrestrial environments. The ocean waves have characteristics of rhythmic oscillations and the link between two buoys is often blocked. Therefore, the signals are also intermittent. We decided to develop a new wave-state-aware networking protocol to eliminate the harmful effect of this kind of rhythmic oscillations. Ocean wave simulation and prediction are key technologies in developing networking protocol, in which ocean wave simulation technology can simulate the marine environments and provide a test platform for developing networking protocol. Ocean wave prediction technology can improve the network throughput. Thus, they are indispensable technologies in developing marine mesh network. In this thesis, we mainly research three problems that related to ocean waves, they are ocean wave measurement, ocean wave prediction and ocean wave simulation. Ocean wave measurement can tell us the current wave height of a buoy, ocean wave prediction can tell us the future height of a buoy, after we know these information, we can decide whether allow the buoy to send signal. It can not only save energy, but also improve the success rate of communication. Ocean wave simulation can provide us a dynamic environment to test whether our networking protocol works well. To sum up, these methods provide an effective test platform and technical support for the development of our marine mesh network.