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The Statistics and Calculus with Python Workshop

You're reading from   The Statistics and Calculus with Python Workshop A comprehensive introduction to mathematics in Python for artificial intelligence applications

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209763
Length 740 pages
Edition 1st Edition
Languages
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Authors (6):
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Ajinkya Sudhir Kolhe Ajinkya Sudhir Kolhe
Author Profile Icon Ajinkya Sudhir Kolhe
Ajinkya Sudhir Kolhe
Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Marios Tsatsos Marios Tsatsos
Author Profile Icon Marios Tsatsos
Marios Tsatsos
Alexander Joseph Sarver Alexander Joseph Sarver
Author Profile Icon Alexander Joseph Sarver
Alexander Joseph Sarver
Peter Farrell Peter Farrell
Author Profile Icon Peter Farrell
Peter Farrell
Alvaro Fuentes Alvaro Fuentes
Author Profile Icon Alvaro Fuentes
Alvaro Fuentes
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Table of Contents (14) Chapters Close

Preface
1. Fundamentals of Python 2. Python's Main Tools for Statistics FREE CHAPTER 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 Appendix

Descriptive Statistics

As mentioned before, descriptive statistics and inferential statistics are the two main categories in the field of statistics. With descriptive statistics, our goal is to compute specific quantities that can convey important information about—or in other words, describe—our data.

From within descriptive statistics, there are two main subcategories: central tendency statistics and dispersion statistics. The actual terms are suggestive of their respective meaning: central tendency statistics are responsible for describing the center of the distribution of the given data, while dispersion statistics convey information about the spread or range of the data away from its center.

One of the clearest examples of this distinction is from the familiar normal distribution, whose statistics include a mean and a standard deviation. The mean, which is calculated to be the average of all the values from the probability distribution, is suitable for estimating...

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