Application of fuzzy linear regression analysis and logistic regression analysis in forecasting process of enterprises

In recent years, in increasingly competitive environment and as results of global economy, one of the most important tool for enterprises has become forecasting the future and determining their strategies in this way in order that enterprises maintain their life and create a difference. In this sense, for minimizing the risks, enterprises tend towards using of forecasting methods and statistical analysis that take part in many applications.The relationship between dependent and independent variables that based on cause and effect relation, is expressed as a regression and oftenly used in statistical analysis. As for Fuzzy Linear Regression Analysis is being an alternative method to the classical regression analysis by using a problem solving tecnique which takes into account the fuzziness of system structure and include both qualitative and quantitative variables to the model in decision process. Another regression tecnique Logistic Regresson Analysis is used for the situation that the outcome variable is binomial or multinomial categorical variable, and take discrete values like 0 and 1. By the effects of explanatory variables on dependent variable are obtained as a probability, it provides to determine these factors as a probability.In this thesis study, Fuzzy Regression Analysis and Logistic Regression Analysis is examined theoretically as a forecasting method and within this scope an application carried out for forecasting the sector portions of the banks with both analyses methods by setting models and consequently the obtained results are interpreted.