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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 3. Chapter 3: Calculus and Differential Equations 4. Chapter 4: Working with Randomness and Probability 5. Chapter 5: Working with Trees and Networks 6. Chapter 6: Working with Data and Statistics 7. Chapter 7: Using Regression and Forecasting 8. Chapter 8: Geometric Problems 9. Chapter 9: Finding Optimal Solutions 10. Chapter 10: Improving Your Productivity 11. Index 12. Other Books You May Enjoy

Using Regression and Forecasting

One of the most important tasks that a statistician or data scientist has is to generate a systematic understanding of the relationship between two sets of data. This can mean a continuous relationship between two sets of data, where one value depends directly on the value of another variable. Alternatively, it can mean a categorical relationship, where one value is categorized according to another. The tool for working with these kinds of problems is regression. In its most basic form, regression involves fitting a straight line through a scatter plot of the two sets of data and performing some analysis to see how well this line fits the data. Of course, we often need something more sophisticated to model more complex relationships that exist in the real world.

Forecasting typically refers to learning trends in time series data with the aim of predicting values in the future. Time series data is data that evolves over a period of time, and usually...

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