Most American universities and colleges require students to provide faculty evaluation at end of each academic term, as a way of measuring faculty teaching performance. Although some analysts think that this kind of evaluation does not necessarily provide a good measurement of teaching effectiveness, there is a growing agreement in the academic world about its reliability. This study attempts to find any strong statistical evidence supporting faculty evaluation by students as a measure of faculty teaching effectiveness. Emphasis will be on analyzing relationships between instructor ratings by students and corresponding students’ grades. Various statistical methods are applied to analyze a sample of real data and derive conclusions. Methods considered include multivariate statistical analysis, principal component analysis, Pearson's correlation coefficient, Spearman's and Kendall’s rank correlation coefficients, linear and logistic regression analysis.

Objective of this Master's thesis is to evaluate different ways of measuring financial performance of enterprise. Economic Value Added is compared with older, traditional indicators of profitability. Emphasis lies on general applicability. In theoretical part, history of entrepreneurial goals is in addition to the actual analysis of tested indicators outlined. EVA, profitability ratios are assessed using a sample of 100 Czech manufacturing companies. In practical part, outputs of thesis apart from actual testing are stated.

The work occupy by inverse analysis based on artificial neural network. This identification algorithm enable correct determine parameters of applied material model on creation of numerical model of construction so it's possible that the results of computerized simulation correspond with experiments. It look's like suitable approach especially in cases with complicated problems and complex models with many material parameters.