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#### Overview of this book

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
Preface
1. Fundamentals of Python
Free Chapter
2. Python's Main Tools for Statistics
3. Python's Statistical Toolbox
4. Functions and Algebra with Python
5. More Mathematics with Python
6. Matrices and Markov Chains with Python
7. Doing Basic Statistics with Python
8. Foundational Probability Concepts and Their Applications
9. Intermediate Statistics with Python
10. Foundational Calculus with Python
11. More Calculus with Python
12. Intermediate Calculus with Python

# Continuous Random Variables

In this section, we'll continue working with random variables. Here, we'll discuss continuous random variables. We will learn the key distinction between continuous and discrete probability distributions. In addition, we will introduce the mother of all distributions: the famous normal distribution. We will learn how to work with this distribution using `scipy.stats` and review its most important characteristics.

## Defining Continuous Random Variables

There are certain random quantities that, in principle, can take any real value in an interval. Some examples are as follows:

• The price of the IBM stocks one week from now
• The number of calories ingested by a person in a day
• The closing exchange rate between the British pound and the Euro
• The height of a randomly chosen male from a specific group

Because of their nature, these variables are known as continuous random variables. As with discrete random variables, there...