In the previous chapters, we have become familiar with instantiating arrays, working on data frames and matrices, solving equations, creating special functions, and performing calculus using functions in SciPy. Together with all this, in a wide variety of tasks, we have assessed the probability of certain events happening or performing statistical analysis on top of data.

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.

Furthermore, probability is the extent to which an event is likely to occur, which is measured using the ratio of the favorable cases to the whole number of cases possible.

This chapter shows you how to work with the statistical and probability tools available in SciPy.

There are multiple applications in which probability and statistics play a vital role. Some of those applications are as follows:

• Predicting the likelihood of an event happening
• Forecasting the value of a certain variable over time
• Image compression algorithms
• Business analytics, which deals with collecting, analyzing, and deriving insights from data to support the formulation of strategies
• Statistical quality control to identify outliers from a given output/outcome
• Signal processing
• Operational research

While these applications are some of the major applications for probability and statistics, there are multiple other applications where probability and statistics form the backbone of the analysis.