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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

Summary

This chapter was an intense one. Congratulations on finishing it!

First, we covered the concept of the hypothesis, including the basic concepts of hypotheses, such as the null hypothesis, the alternative hypothesis, and the P-value. I spent quite a bit of time going over example content to ensure that you understood the concept of the P-value and significance levels correctly.

Next, we looked at the paradigm of hypothesis testing and used corresponding library functions to do testing on various scenarios. We also covered the ANOVA test and testing on time series.

Toward the end, we briefly covered A/B testing. We demonstrated the idea with a classic click rate example and also pointed out some common mistakes.

One additional takeaway for this chapter is that in many cases, new knowledge is needed to understand how a task is done in unfamiliar fields. For example, if you were not familiar with time series before reading this chapter, now you should know how to use...

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