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Hands-On Exploratory Data Analysis with Python

You're reading from   Hands-On Exploratory Data Analysis with Python Perform EDA techniques to understand, summarize, and investigate your data

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789537253
Length 352 pages
Edition 1st Edition
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Authors (2):
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Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
Usman Ahmed Usman Ahmed
Author Profile Icon Usman Ahmed
Usman Ahmed
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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Fundamentals of EDA
2. Exploratory Data Analysis Fundamentals FREE CHAPTER 3. Visual Aids for EDA 4. EDA with Personal Email 5. Data Transformation 6. Section 2: Descriptive Statistics
7. Descriptive Statistics 8. Grouping Datasets 9. Correlation 10. Time Series Analysis 11. Section 3: Model Development and Evaluation
12. Hypothesis Testing and Regression 13. Model Development and Evaluation 14. EDA on Wine Quality Data Analysis 15. Other Books You May Enjoy Appendix

Understanding regression

We use correlation in statistical terms to denote the association between two quantitative variables. Note that we have used the term quantitative variables. This should be meaningful to you. If not, we suggest you pause here and go through Chapter 1, Exploratory Data Analysis Fundamentals.

When it comes to quantitative variables and correlation, we also assume that the relationship is linear, that is, one variable increases or decreases by a fixed amount when there is an increase or decrease in another variable. To determine a similar relationship, there is the other method that's often used in these situations, regression, which includes determining the best straight line for the relationship. A simple equation, called the regression equation, can represent the relation:

Let's examine this formula:

  • Y = The dependent variable (the variable...
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