January 2018
Beginner to intermediate
422 pages
9h 47m
English
Content preview from Regression Analysis with R
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Data Preparation Using R Tools
Real world datasets are very varied: variables can be textual, numerical, or categorical and observations can be missing, false, or wrong (outliers). To perform a proper data analysis, we will understand how to correctly parse a dataset, clean it, and create an output matrix optimally built for regression. To extract knowledge, it is essential that the reader is able to create an observation matrix, using different techniques of data analysis and cleaning.
In the previous chapters, we analyzed how to perform a single and multiple regression analysis while how to carry out a multiple and multinomial logistic regression. But in all cases analyzed, to get the correct indication from the models, the data must be ...
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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.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.