November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Summary
In this chapter, we introduced a statistical regression, noted the most common regression approaches, and provided some advice on selecting the correction approach for a particular statistical project. In addition, we mentioned how to identify opportunities for using statistical regression and discussed data summarization, exploring relationships, and testing for significance of the difference.
Finally, we wrapped up with a working example of linear regression modeling for r predicting project profitability.
The next chapter will introduce the developer to the idea of statistical regularization for improving data models in an effort to help comprehend what statistical regularization is and why it is important as well as feel comfortable ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.