Language:
English
Pages:
1 online resource (1 volume)
,
illustrations
Keywords:
Mathematical statistics
;
Sampling (Statistics)
;
Electronic books
;
Electronic books ; local
Abstract:
Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you'll learn about A-B testing and hypothesis , or significance tests -critical aspects of experimental design for data science. What you'll learn-and how you can apply it You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you'll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you'll also learn how to conduct various hypothesis , or significance tests , enabling you to avoid misinterpreting randomness. This lesson is for you because You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: n/a
Note:
"From Practical statistics for data scientists by Peter Bruce and Andrew Bruce"--Cover. - Date of publication from resource description page. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 16, 2017)
URL:
https://learning.oreilly.com/library/view/-/9781491978351/?ar
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