Estimating Fuzzy Linear Regression Model for Air Pollution Predictions in Baghdad City - Alsoltany, Suhaila Najma; Alnaqash, Iftikhar Abdulhamed (Al-Nahrain Journal of Science, 2018)
Regression analysis is one f the basic tools of scientific investigation of functional relationship between dependent and independent variables, For many years linear regression models has been used in almost every field of science. The purpose of regression analysis is to explain the variation of dependent variables in terms of the variation of explanatory variables, residuals are assumed to be due to random errors, however the residuals are sometimes due to the indefiniteness of the model structure or imprecise observations, the uncertainty in this type of regression model becomes fuzziness, not random.The aim of this paper is to study and applied the method of estimation fuzzy linear regression parameters using fuzzy data collecting from (145) sample in three stations (Andalus square, jadiriya, alawi) in Bagdad city every day, In order to measurements the concentrations of airborne stuck which represents the response variable, and also the most important air pollutants, namely, (lead, zinc, copper, iron, nickel, chromium, cadmium) as independents variables the main result identify the best techniques to estimate the fuzzy linear regression parameters for this data and calculates the expected value of the concentrations of airborne stuck in Bagdad city for the next years.