With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually. Crack rating is an important parameter used by FDOT for making maintenance decisions and budget appropriation. By establishing a crack rating threshold below which traveler comfort is not assured, authorities can screen the pavement sections which are in need of Maintenance and Rehabilitation (M&R). Hence, accurate and reliable prediction of crack thresholds is essential to optimize the rehabilitation budget and manpower. Transition Probability Matrices (TPM) can be utilized to accurately predict the deterioration of crack ratings leading to the threshold. Such TPMs are usually developed by historical data or expert or experienced maintenance engineers' opinion. When historical data are used to develop TPMs, deterioration trends have been used vii indiscriminately, i.e. with no discrimination made between pavements that degrade at different rates. However, a more discriminatory method is used in this thesis to develop TPMs based on classifying pavements first into two groups. They are pavements with relatively high traffic and, pavements with a history of excessive degradation due to delayed rehabilitation. The new approach uses a multiple non-linear regression process to separately optimize TPMs for the two groups selected by prior screening of the database. The developed TPMs are shown to have minimal prediction errors with respect to crack ratings in the database that were not used in the TPM formation. It is concluded that the above two groups are statistically different from each other with respect to the rate of cracking. The observed significant differences in the deterioration trends would provide a valuable tool for the authorities in making critical network-level decisions. The same methodology can be applied in other transportation agencies based on the corresponding databases.

Job dissatisfaction among bank employees may adversely influence the financial performance of banks due to employee turnover, decreased productivity, poor service quality, decreased customer satisfaction, and negative employee attitudes in the workplace. The purpose of this correlational study was to examine how work on the present job, pay, opportunities for promotion, supervision, and coworker relationships predict job satisfaction among bank employees in Nigeria. The population of the study was 167 bank employees in 3 commercial banks in Nigeria. The 2-factor theory (TFT) served as the theoretical foundation in this study. Data collection was through a survey instrument called the job descriptive index. The results of the multiple linear regression analysis showed that the regression model significantly predicted job satisfaction, F (5, 95) = 10.806, p < .05, R2 = .363. Both supervision and coworker relationships were statistically significant predictors of job satisfaction among bank employees in Nigeria, while there were no statistically significant relationships between the predictors' work on the present job, pay, and opportunities for promotion, and the dependent variable, job satisfaction. The implications of this study for positive social change include the potential to provide senior bank executives with an understanding of factors that relate to job satisfaction among bank employees, including creating a desirable work environment, improving the quality of supervision in the organization, increasing job satisfaction, and making the organization more desirable for employees.

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects the model. We will also make a comparison between the Bayesian approach and the traditional frequentist approach to data analyses.

The dissertation deals with the accounting aspects of assessing the financial health of a company with a focus on agriculture. The main objective of this study is to assess individual methods designed to evaluate the financial health of a company, to determine their sensitivity to risk data in accounting. The study is focused on the field of agriculture mainly as a result of knowledge about the difficult process of compiling and using agricultural accounting. Agriculture fall within the primary sector of the economy, is very important for landscaping and a lot of subsidies flow from the budget of state and the European Union. Due to the specifics and stated problematic areas, which cannot be fully captured by legislation, incomplete or distorted information is transmitted, being also transferred to the methods of the financial health assessment of the company. Attention is also paid to the influence of legislative changes on the values in accounting as well as creative accounting. Following the findings from the theoretical basis, the application part analyses the impact of different accounting solutions on the financial statements. A paired t-test, used for the analysis, was preceded by data normality testing using the histogram and Shapiro-Wilk test. According to these tests, statistically significant differences were found com-paring the current method of accounting used for investment subsidies and leases with the IFRS accounting, between the accounting of changes in inventories and capitalization before and after 1 January 2016, and in land valuation using historical cost and market price. All these areas influence the values of all the analysed methods of financial health assessment. Only the CH-index showed no statistically significant difference in land valuation and accounting solution of inventory activation and changes. Furthermore, the reliability and controllability of the selected methods used for the evaluation of financial health in the field of agriculture is assessed. According to the results, none of the evaluated models can be used in its original variant, but it is possible to use them to compare the company with similar enterprises or over time thanks to the proven dependence of partial indicators and even the whole models on the productivity. Another type of analysis is designed to determine the indicators that have a statistically significant impact on the actual financial situation of businesses. The method of generalized linear models - multinomial linear regression - is used for this test. To determine whether an enterprise is at risk or not, it would be possible to use the stock / income and short-term liabilities / income indicators, and the cash flow / assets indicator to determine the type of threat.

Environmental sustainability in buildings is an important part of preserving the environment and reducing climate change. The increasing amount of physical infrastructure systems in Malawi has not been accompanied by policy-makers clearly understanding perceptions and attitudinal behaviors of building designers to promote environmental sustainability. Some building designers in Malawi might not be practicing sustainability innovations adequately, requiring more research to understand their perceptions and behaviors. The purpose of this mixed methods sequential and explanatory study was to explore how building designers' behaviors relate to the implementation of sustainability innovations in Malawi. Ajzen's theory of planned behavior explaining how attitudinal behaviors relate to individual's actions, served as the conceptual framework. The central research question investigated perceptions and attitudinal behaviors building designers hold about sustainability, and how these behaviors connect with practicing sustainability innovations. Data collection used a Likert scale questionnaire to capture behavior items. A sample of 99 individuals working in building organizations completed the questionnaire. Multiple linear regression analysis showed attitude behavior influenced practicing sustainability more than the subjective and perceived control behaviors. Interviews with 24 participants supported the analytical finding. Government and policy-makers were the target audience. Knowledge about behaviors toward sustainability innovations enables government and policy-makers strategize and change stakeholders' mindset to increase sustainability practices thereby impacting societal change in the construction communities.

<p>The Swedish Transport Administration has, and manages, a database containing information of the status of road condition on all paved and governmental operated Swedish roads. The purpose of the database is to support the Pavement Management System (PMS). The PMS is used to identify sections of roads where there is a need for treatment, how to allocate resources and to get a general picture of the state of the road network condition. All major treatments should be reported which has not always been done.</p><p>The road condition is measured using a number of indicators on e.g. the roads unevenness. Rut depth is an indicator of the roads transverse unevenness. When a treatment has been done the condition drastically changes, which is also reflected by these indicators.</p><p>The purpose of this master thesis is to; by using existing indicators make predictions to find points in time when a road has been treated.</p><p>We have created a SAS-program based on simple linear regression to analyze rut depth changes over time. The function of the program is to find levels changes in the rut depth trend. A drastic negative change means that a treatment has been made.</p><p>The proportion of roads with an alleged date for the latest treatment earlier than the programs latest detected date was 37 percent. It turned out that there are differences in the proportions of possible treatments found by the software and actually reported roads between different regions. The regions North and Central have the highest proportion of differences. There are also differences between the road groups with various amount of traffic. The differences between the regions do not depend entirely on the fact that the proportion of heavily trafficked roads is greater for some regions.</p>

In this thesis, we consider the problem of estimating the regression function in location-scale regression models. This model assumes that the random vector (X,Y) satisfies Y = m(X) + s(X)e, where m(.) is an unknown location function (e.g. conditional mean, median, truncated mean,...), s(.) is an unknown scale function, and e is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed. In the first part of the thesis, we assume that m(x) = E(Y|X=x) follows a polynomial model. A new estimation procedure for the unknown regression parameters is proposed, which extends the classical least squares procedure to censored data. The proposed method is inspired by the method of Buckley and James (1979), but is, unlike the latter method, a non-iterative procedure due to nonparametric preliminary estimation. The asymptotic normality of the estimators is established. Simulations are carried out for both methods and they show that the proposed estimators have usually smaller variance and smaller mean squared error than the Buckley-James estimators. For the second part, suppose that m(.)=E(Y|.) belongs to some parametric class of regression functions. A new estimation procedure for the true, unknown vector of parameters is proposed, that extends the classical least squares procedure for nonlinear regression to the case where the response is subject to censoring. The proposed technique uses new `synthetic' data points that are constructed by using a nonparametric relation between Y and X. The consistency and asymptotic normality of the proposed estimator are established, and the estimator is compared via simulations with an estimator proposed by Stute in 1999. In the third part, we study the nonparametric estimation of the regression function m(.). It is well known that the completely nonparametric estimator of the conditional distribution F(.|x) of Y given X=x suffers from inconsistency problems in the right tail (Beran, 1981), and hence the location function m(x) cannot be estimated consistently in a completely nonparametric way, whenever m(x) involves the right tail of F(.|x) (like e.g. for the conditional mean). We propose two alternative estimators of m(x), that do not share the above inconsistency problems. The idea is to make use of the assumed location-scale model, in order to improve the estimation of F(.|x), especially in the right tail. We obtain the asymptotic properties of the two proposed estimators of m(x). Simulations show that the proposed estimators outperform the completely nonparametric estimator in many cases.

Aerodynamic parameter estimation from closed loop data has been developed as another research area since control and stability augmentation systems have been mandatory for aircrafts. This thesis focuses on aerodynamic parameter estimation of an air to ground missile from closed loop data using separate surface excitations. A design procedure is proposed for designing separate surface excitations. The effect of excitations signals to the system is also analyzed by examining autopilot disturbance rejection performance. Aerodynamic parameters are estimated using two different estimation techniques which are ordinary least squares and complex linear regression. The results are compared with each other and with the aerodynamic database. An application of the studied techniques to a real system is also given to validate that they are directly applicable to real life.

Aerodynamic parameter estimation from closed loop data has been developed as another research area since control and stability augmentation systems have been mandatory for aircrafts. This thesis focuses on aerodynamic parameter estimation of an air to ground missile from closed loop data using separate surface excitations. A design procedure is proposed for designing separate surface excitations. The effect of excitations signals to the system is also analyzed by examining autopilot disturbance rejection performance. Aerodynamic parameters are estimated using two different estimation techniques which are ordinary least squares and complex linear regression. The results are compared with each other and with the aerodynamic database. An application of the studied techniques to a real system is also given to validate that they are directly applicable to real life.

In test score equating the focus is on the problem of finding the relationship between the scales of different test forms. This can be done only if data are collected in such a way that the effect of differences in ability between groups taking different test forms can be separated from the effect of differences in test form difficulty. In standard equating procedures this problem has been solved by using common examinees or common items. With common examinees, as in the equivalent groups design, the single group design, and the counterbalanced design, the examinees taking the test forms are either exactly the same, i.e., each examinee takes both test forms, or random samples from the same population. Common items (anchor items) are usually used when the samples taking the different test forms are assumed to come from different populations. The thesis consists of four papers and the main theme in three of these papers is the use of covariates, i.e., background variables correlated with the test scores, in observed score equating. We show how covariates can be used to adjust for systematic differences between samples in a non-equivalent groups design when there are no anchor items. We also show how covariates can be used to decrease the equating error in an equivalent groups design or in a non-equivalent groups design. The first paper, Paper I, is the only paper where the focus is on something else than the incorporation of covariates in equating. The paper is an introduction to test score equating, and the author's thoughts on the foundation of test score equating. There are a number of different definitions of test score equating in the literature. Some of these definitions are presented and the similarities and differences between them are discussed. An attempt is also made to clarify the connection between the definitions and the most commonly used equating functions. In Paper II a model is proposed for observed score linear equating with background variables. The idea presented in the paper is to adjust for systematic differences in ability between groups in a non-equivalent groups design by using information from background variables correlated with the observed test scores. It is assumed that conditional on the background variables the two samples can be seen as random samples from the same population. The background variables are used to explain the systematic differences in ability between the populations. The proposed model consists of a linear regression model connecting the observed scores with the background variables and a linear equating function connecting observed scores on one test forms to observed scores on the other test form. Maximum likelihood estimators of the model parameters are derived, using an assumption of normally distributed test scores, and data from two administrations of the Swedish Scholastic Assessment Test are used to illustrate the use of the model. In Paper III we use the model presented in Paper II with two different data collection designs: the non-equivalent groups design (with and without anchor items) and the equivalent groups design. Simulated data are used to examine the effect - in terms of bias, variance and mean squared error - on the estimators, of including covariates. With the equivalent groups design the results show that using covariates can increase the accuracy of the equating. With the non-equivalent groups design the results show that using an anchor test together with covariates is the most efficient way of reducing the mean squared error of the estimators. Furthermore, with no anchor test, the background variables can be used to adjust for the systematic differences between the populations and produce unbiased estimators of the equating relationship, provided that the “right” variables are used, i.e., the variables explaining those differences. In Paper IV we explore the idea of using covariates as a substitute for an anchor test with a non-equivalent groups design in the framework of Kernel Equating. Kernel Equating can be seen as a method including five different steps: presmoothing, estimation of score probabilities, continuization, equating, and calculating the standard error of equating. For each of these steps we give the theoretical results when observations on covariates are used as a substitute for scores on an anchor test. It is shown that we can use the method developed for Post-Stratification Equating in the non-equivalent groups with anchor test design, but with observations on the covariates instead of scores on an anchor test. The method is illustrated using data from the Swedish Scholastic Assessment Test.