This chapter presents inference methods for estimating system parameters with confidence, distribution fitting strategies, and methods for data fitting such as regression and interpolation. It presents the parametric method to estimate the confidence interval of the mean. The chapter introduces the semi-parametric bootstrap method. It presents two methods for checking if probability distributions are suitable for representing a set of measured data. Many different metrics may quantify errors. In regression models, one of the most adopted is the Root Mean Square Error. The strategy adopted for linear regression may also be broadly adopted as a general setting to estimate parameters of more complex curve fitting. Regression techniques are one of the most popular statistical techniques used for prediction.