Chapter 3. Statistics and Linear Algebra

Statistics and linear algebra are branches of mathematics that are especially useful for data analysis. That's why we will focus on them in this chapter. Statistics is needed to make inferences from raw data. For instance, we can compute that the data for a variable has a certain arithmetic mean and standard deviation. From these numbers, we can then infer a range and the expected value for this variable. Then, we can run statistical tests to check how likely it is that we made the right conclusion.

Linear algebra concerns itself with systems of linear equations. These are easy to solve with NumPy and SciPy using the linalg package. Linear algebra is useful, for instance, to fit data to a model. We shall introduce other NumPy and SciPy packages in this chapter for random number generation and masked arrays.

In this chapter, we will cover the following topics:

• Descriptive statistics
• The linalg package
• Polynomials
• Matrices as specialized ndarray subclasses
• Random numbers
• Continuous and discrete distributions
• Masked arrays