Book Image

Modern Python Cookbook - Second Edition

By : Steven F. Lott
Book Image

Modern Python Cookbook - Second Edition

By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
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Computing regression parameters

Once we've determined that two variables have some kind of relationship, the next step is to determine a way to estimate the dependent variable from the value of the independent variable. With most real-world data, there are a number of small factors that will lead to random variation around a central trend. We'll be estimating a relationship that minimizes these errors, striving for a close fit.

In the simplest cases, the relationship between variables is linear. When we plot the data points, they will tend to cluster around a line. In other cases, we can adjust one of the variables by computing a logarithm or raising it to a power to create a linear model. In more extreme cases, a polynomial is required. The process of linear regression estimates a line that will fit the data with the fewest errors.

In this recipe, we'll show how to compute the linear regression parameters between two variables. This will be based on the...