In classical regression models, the functional relationship between the dependent (response) variable and independent (explanatory) variables is evaluated and also the best fit model for the relationship is determined. The deviation between the observes values and estimated values are supposed to be due to random errors. The deviations are sometimes due to the impressive observed data or the indefiniteness of the system The uncertainty is assumed not due to randomness but fuzziness is unusually refers to fussy regression analysis This book is mainly concerned with the concepts of fuzzy sets and fuzzy logic in some regression analysis problems it is hoped that the book will be benefitted to not only the post graduate students master degree M. Phil but also researchers working in this interesting technique which is again applicable to many real life situation contents; Review of Previous Works; Fuzzy Sets and Fuzzy Logic; An Overview; Basic Purpose of Regression; A quadratic Programming Approach to Constrained Linear Regression; Linear Regression with Gauzy Parameters by Interval Approach; The Estimation of Consumer Price Index Number by Interval Regression Analysis Quadratic Programming Approch to Interval Regression Analysis Application to Consumer Price Index Analysis