Calculate a quantile for a given binomial probability distribution.
> **Note:** This is depreciated. The Binomial Distribution Quantile Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handle binomial distributions. This service is a part of the Binomial distribution suite of services that allows the user to generate a binomial distribution sequence of any length, calculate quantiles out of given probability (this specific service) and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service (see description below). The Binomial Distribution Suite is based on R functions qbinom, rbinom and pbinom that are included in R stats package. ##Consumption of Web Service## This service accepts 4 arguments of a normal distribution and calculates the associated quantile. The following arguments should be provided to it within the request: - p - a single aggregated probability of multiple trials, - size - the number of trials, - prob - the probability of success in a trial, - side - L for the lower side of the distribution, U for the upper side of the distribution. The output of the service is the calculated quantile that is associated with the given probability. ##Example Application There are multiple ways of consuming the service in an automated fashion. One example apps is here: [Quantile Calculator](http://microsoftazuremachinelearning.azurewebsites.net/BinomialDistributionQuantileCalculator). *While these web services could be consumed by users - potentially directly on the marketplace, through a mobile app, website, or even on a local computer for example, the purpose of the web service is also to serve as an example of how Azure ML can be used to create web services on top of R code. With just a few lines of R code and clicks of a button within the Azure ML Studio, an experiment can be created with R code and published as a web service. The web service can then be published to the Azure Marketplace and consumed by users and devices across the world with no infrastructure set-up by the author of the web service.*