Thesis advisor: Christopher Maxwell / In this paper I present an econometric analysis of selection bias in the NBA Draft from 1995-2003. Employing an interval regression maximum likelihood control model that predicts the eventual value of players based on their Draft position, I pinpoint which groups of players consistently over-perform or under-perform relative to their Draft position. Using this analysis I detect bias pertaining to four different groups of players. There was a bias against high school players, especially those taken in the lottery (the top of the first round of the Draft), which may point to risk-averse tendencies of NBA teams. There was also a bias in favor of centers taken in the lottery, who were consistently drafted too high. Black players were selected too low in the first round, and too high in the second round. The final effect deals with foreign entrants to the Draft. From 1995-2001 foreigners were drafted too low; in 2002 and 2003, when more foreigners were selected, they were drafted too high. My paper details the nature of these biases and analyzes their potential causes. / Thesis (BA) — Boston College, 2008. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Economics. / Discipline: Economics Honors Program. / Discipline: College Honors Program.

This diploma thesis deals with the development of individual financial indicators of a selected company using the methods of financial analysis, interval regression analysis and time series analysis. Based on the results of these analyzes there is evaluated the financial situation of the company and created a forecast of the evolution of the selected indicators for the next two years. The selected company is described by an analysis of the internal and external surroundings. Possible risks are identified by a risk analysis and proposals are made to reduce the value of individual risks to an acceptable level.

The thesis unifies the most important author's results in the field of algorithms concerning zonotopes and their applications in optimization and statistics. The computational-geometric results consist of a new compact output-sensitive algorithm for enumerating vertices of a zonotope, which outperforms the rival algorithm with the same complexity-theoretic properties both theoretically and empirically, and a polynomial algorithm for arbitrarily precise approximation of a zonotope with the Löwner-John ellipsoid. In the application area, the thesis presents a result, which connects linear regression model with interval outputs with the zonotope matters. The usage of presented geometric algorithms for solving a nonconvex optimisation problem is also discussed.

This diploma thesis aims at analyzing accounting and financial indicators using time series, regression analysis and interval regression analysis of selected company VIDEN plus, a.s. The thesis analyzes the development trends of individual indicators, which are able to reveal the state of the company. Based on the data obtained, the issue of company risks arising from the analyzes and their correction is then addressed.

This master`s thesis deals with the use of statistical and economic methods of analysis for evaluation of the financial situation of Heineken Czech Republic a.s. Company's economic indicators are subjected to regression analysis, interval regression analysis, and time series analysis. The proposal part contains a detailed evaluation of the results of the analyzes, evaluation of the financial risk factors and recommendations for improving the financial situation of the company.

The diploma thesis is focusing on the review of financial indicators of a selected company through the use of statistical methods. Time series analysis, interval regression analysis, and regression analysis were used to analyse financial indicators, and based on that a prediction of the development in the next two years was created. The analysis also addresses potential risks for the company and possible ways to lower them. Based on the information obtained from the analysis a complete evaluation is created and recommendations are made to improve the company’s situation.

This diploma thesis deals with the analysis of financial indicators using time series, regression analysis and interval regression analysis of a selected company. The diploma thesis describes selected financial indicators, time series, regression analysis and interval regression analysis. Furthermore, the calculations of financial indicators for the selected company and the characteristics of the time series are performed. Individual financial indicators are subjected to regression analysis and interval regression analysis. Based on the performed analyzes, the company's risk factors are determined and recommendations for the improvement of the current situation in the company are proposed.

First, basic aspects of interval analysis, roles of intervals and their applications are addressed. Then, various classes of interval matrices are described and their relations are depicted. This material forms a prelude to the unifying theme of the rest of the work - solving interval linear systems. Several methods for enclosing the solution set of square and overdetermined interval linear systems are covered and compared. For square systems the new shaving method is introduced, for overdetermined systems the new subsquares approach is introduced. Detecting unsolvability and solvability of such systems is discussed and several polynomial conditions are compared. Two strongest condi- tions are proved to be equivalent under certain assumption. Solving of interval linear systems is used to approach other problems in the rest of the work. Computing enclosures of determinants of interval matrices is addressed. NP- hardness of both relative and absolute approximation is proved. New method based on solving square interval linear systems and Cramer's rule is designed. Various classes of matrices with polynomially computable bounds on determinant are characterized. Solving of interval linear systems is also used to compute the least squares linear and nonlinear interval regression. It is then applied to real...