: In fuzzy linear regression that was introduced by Tanaka in 1982 , some of the strict assumptions of the statistical model are relaxed. In the general fuzzy regression model the input data (for explanatory variables x) and the output data (for dependent variable y) are fuzzy, the relationship between the input and output data is given by a fuzzy function and the distribution of the data is possibilistic. They need not have statistical properties. So the fuzzy regression analysis should be applied to many real life problems in which the strict assumptions of align=left Outliers are sometimes occurred because of big errors during the collection, recording or transferring data. Sometimes they are correct observations that show inadequacy of the model. When an outlier is detected, it should be investigated. We should not automatically omit it and continue the analysis. If outliers are serious observations, they prove inadequacy of the model. Usually they provide valuable keys for analyzer to make better model. It is important for analyzer to detect outliers and investigate their effect on different features of analysis. One of the drawbacks of Tanaka’s model is that it is sensitive to outliers. This sensitivity caused that predicted intervals become wide which is not desired. Over the last years, some methods are presented to remove this problem. One method is to introduce a new variable and construct a fuzzy linear programming problem with fuzzy intervals and obtain reasonable estimate intervals. In this way, estimates are not affected by outliers and effect of outliers will be omitted. This means that all data influence on estimated interval not just outliers. Another method is to add some additional constraints to the main problem’s constraints and detect outliers and modify the constraints of outliers. In this way also, effect of outliers will be omitted. However there are some drawbacks in these methods. For example they have to already determine some values for parameters. To overcome the drawbacks, we use an omission approach that investigate the value changes in the objective function when each observation is omitted. So, a method for detecting outliers is presented that by eliminating every observation, its effect on objective function in linear programming problem is investigated and the outlier is detected. In addition, we use box plot to define the cutoffs for detecting outliers. A certain diagnostic measure is used to see the effect of one observation on the objective function. Then the concentration is on the biggest one. Therefore, a box plot is used to determine whether the biggest measure is an outlier or not.