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CompTIA Data+: DAO-001 Certification Guide

You're reading from   CompTIA Data+: DAO-001 Certification Guide Complete coverage of the new CompTIA Data+ (DAO-001) exam to help you pass on the first attempt

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804616086
Length 370 pages
Edition 1st Edition
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Author (1):
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Cameron Dodd Cameron Dodd
Author Profile Icon Cameron Dodd
Cameron Dodd
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Preparing Data
2. Chapter 1: Introduction to CompTIA Data+ FREE CHAPTER 3. Chapter 2: Data Structures, Types, and Formats 4. Chapter 3: Collecting Data 5. Chapter 4: Cleaning and Processing Data 6. Chapter 5: Data Wrangling and Manipulation 7. Part 2: Analyzing Data
8. Chapter 6: Types of Analytics 9. Chapter 7: Measures of Central Tendency and Dispersion 10. Chapter 8: Common Techniques in Descriptive Statistics 11. Chapter 9: Hypothesis Testing 12. Chapter 10: Introduction to Inferential Statistics 13. Part 3: Reporting Data
14. Chapter 11: Types of Reports 15. Chapter 12: Reporting Process 16. Chapter 13: Common Visualizations 17. Chapter 14: Data Governance 18. Chapter 15: Data Quality and Management 19. Part 4: Mock Exams
20. Chapter 16: Practice Exam One 21. Chapter 17: Practice Exam Two 22. Index 23. Other Books You May Enjoy

Understanding hypothesis testing

In this section, we will cover what hypothesis testing is and why it is an important skill to have for a data analyst. Hypothesis testing is, in a nutshell, trying to guess whether or not your hypothesis is true. If someone asks you a specific business question, this is how you give a confident yes or no answer.

There are several different types of hypothesis tests, as you can imagine, but, for the purpose of this chapter, we are going to focus on one of the most common ones. The most common form of hypothesis testing is comparing two things and saying whether or not they are the same. It is as simple as that. Sometimes, you are comparing two completely separate things. Sometimes, you are comparing a “before” and “after” group. Often, you are comparing something altered to something unaltered. Which product sells better, A or B? Are our sales this year higher than last year? Would we sell the product to more women if we...

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