To avoid software development project failure, accurate estimation of software development effort is necessary at the beginning of a software project. This paper proposes to adjust the kurtosis and the skewness of project feature variables for better fitting of software estimation models. The proposed method conducts logarithmic transformation of variables, then conducts the kurtosis and skewness transformation to make the variable distribution closer to the normal distribution. To empirically evaluate the effectiveness of the proposed method, we employed three industry data sets and linear regression models with three-fold cross validation. The result of the evaluation showed that the models with the proposed method were better in both the goodness of fit and the estimation accuracy in terms of MMRE compared to log-log regression.