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Essential Statistics for Non-STEM Data Analysts

You're reading from   Essential Statistics for Non-STEM Data Analysts Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781838984847
Length 392 pages
Edition 1st Edition
Languages
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Author (1):
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Rongpeng Li Rongpeng Li
Author Profile Icon Rongpeng Li
Rongpeng Li
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Getting Started with Statistics for Data Science
2. Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing FREE CHAPTER 3. Chapter 2: Essential Statistics for Data Assessment 4. Chapter 3: Visualization with Statistical Graphs 5. Section 2: Essentials of Statistical Analysis
6. Chapter 4: Sampling and Inferential Statistics 7. Chapter 5: Common Probability Distributions 8. Chapter 6: Parametric Estimation 9. Chapter 7: Statistical Hypothesis Testing 10. Section 3: Statistics for Machine Learning
11. Chapter 8: Statistics for Regression 12. Chapter 9: Statistics for Classification 13. Chapter 10: Statistics for Tree-Based Methods 14. Chapter 11: Statistics for Ensemble Methods 15. Section 4: Appendix
16. Chapter 12: A Collection of Best Practices 17. Chapter 13: Exercises and Projects 18. Other Books You May Enjoy

Classifying numerical and categorical variables

Descriptive statistics are all about variables. You must know what you are describing to define corresponding descriptive statistics.

A variable is also referred to as a feature or attribute in other literature. They all mean the same thing: a single column in a tabulated dataset.

In this section, you will examine the two most important variable types, numerical and categorical, and learn to distinguish between them. Categorical variables are discrete and usually represent a classification property of entry. Numerical variables are continuous and descriptive quantitatively. Descriptive statistics that can be applied to one kind of variable may not be applied to another one, hence distinguishing between them precedes analytics.

Distinguishing between numerical and categorical variables

In order to understand the differences between the two types of variables with the help of an example, I will be using the population estimates...

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