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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

Understanding mean, median, and mode

Mean, median, and mode describe the central tendency in some way. Mean and median are only applicable to numerical variables whereas mode is applicable to both categorical and numerical variables. In this section, we will be focusing on mean, median, and mode for numerical variables as their numerical interactions usually convey interesting observations.

Mean

Mean, or arithmetical mean, measures the weighted center of a variable. Let's use n to denote the total number of entries and as the index. The mean reads as follows:

Mean is influenced by the value of every entry in the population.

Let me give an example. In the following code, I will generate 1,000 random numbers from 0 to 1 uniformly, plot them, and calculate their mean:

import random
random.seed(2019)
plt.figure(figsize=(8,6))
rvs = [random.random() for _ in range(1000)]
plt.hist(rvs, bins=50)
plt.title("Histogram of Uniformly Distributed...
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