In order to make estimates or future projections based on your data, you first need to perform distribution fitting and select the model describing the random process you are dealing with. While calculating probabilities can be performed using the Cumulative Distribution Function (CDF) of the best fitting distribution, the estimates are made using the Quantile Function. The CDF indicates the probability that the random variate X takes on a value less than or equal to x:

F(x) = P(X≤x),

and the Quantile Function, also known as the Inverse CDF, is defined for continuous distributions in the following way:

x = F^-1(p),

where p=F(x), and F(x) is the CDF of the same distribution. To make an estimate or a projection means to calculate x for a given probability value p. For some continuous distributions, the analytical expression for the Inverse CDF can be easily derived from the CDF, however, for many models, including the Normal, Lognormal, Beta, and Gamma distributions, the Inverse CDF is not available in closed form, and should be evaluated using either iterative numerical methods or approximation formulas.