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

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781838984847

Length 392 pages

Edition 1st Edition

Languages

Python

Concepts

Data Science

Author (1):

Rongpeng Li

View More author details

Table of Contents (19) Chapters

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

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Learning about joint and conditional distribution

We have covered basic examples from discrete probability distributions and continuous probability distributions. Note that all of them describe the distribution of a single experiment outcome. How about the probability of the simultaneous occurrence of two events/outcomes? The proper mathematical language is joint distribution.

Suppose random variables X and Y denote the height and weight of a person. The following probability records the probability that X = x and Y = y simultaneously, which is called a joint distribution. A joint distribution is usually represented as shown in the following equation:

For a population, we may have P(X = 170cm, Y = 75kg) = 0.25. You may ask the question: What is the probability of a person being 170 cm while weighing 75 kg? So, you see that there is a condition that we already know this person weighs 75 kg. The expression for a conditional distribution is a ratio as follows...