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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth: A Case Study Appendix

Statistical Significance Testing

Often, an analyst is interested in comparing the statistical properties of two groups, or perhaps just one group before and after a change. Of course, the difference between these two groups may just be due to chance.

An example of where this comes up is in marketing A/B tests. Companies often test two different types of landing pages for a product and measure how many clicks it will receive on each of the landing pages. For example, if you make the image of a product two times larger, will this make people more likely to click it? You may find that 10% of the visitors for variation A of the landing page clicked on the product, and 11% for variation B. So, does that mean variation B is 10% better than A or is this just a result of day-to-day variance? You need a method based on statistics to determine just that.

Statistical significance testing is the method of determining whether the data that you have supports a certain hypothesis. To build...

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