Summary  
This chapter explains how to implement code that calculates covariance and correlation coefficients to quantify the linear relationship between numerical data sets.  

General domain of usage  
Data analysis

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

Calculate Covariance and Correlation

Solution

Awesome!

Calculate Covariance and Correlation

Solution

Awesome!

Calculate Covariance and Correlation

Solution