Theory of regression methods is based on the crispness of the observations and the parameters of interest. But there can be many different situations in which the above mentioned concepts are imprecise. On the other hand, the theory of fuzzy sets is a well established tool for formulation and analysis of imprecise and subjective concepts. In these times we must use the fuzzy regression. In this paper, at first we use a well-known signed distance, then with using this signed distance we estimate the crisp regression coefficients based on fuzzy data using least square method. Finally, we exhibit confidence interval and hypothesis testing for these coefficients based on bootstrap theory and numerical examples are also provided to illustrate the approach. In case of the confidence interval and hypothesis testing problem, bootstrap techniques (Efron and Tibshirani, [10]) have empirically been shown to be efficient and powerful.