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Regression Analysis with R

You're reading from   Regression Analysis with R Design and develop statistical nodes to identify unique relationships within data at scale

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788627306
Length 422 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Regression 2. Basic Concepts – Simple Linear Regression FREE CHAPTER 3. More Than Just One Predictor – MLR 4. When the Response Falls into Two Categories – Logistic Regression 5. Data Preparation Using R Tools 6. Avoiding Overfitting Problems - Achieving Generalization 7. Going Further with Regression Models 8. Beyond Linearity – When Curving Is Much Better 9. Regression Analysis in Practice 10. Other Books You May Enjoy

Finding outliers in data


Outliers are the values that, compared to others, are particularly extreme (a value clearly distant from the other available observations.). Outliers are a problem because they tend to distort data analysis results, in particular in descriptive statistics and correlations. These should be identified in the data cleaning phase, but can also be dealt in the next step of data analysis. Outliers can be univariate when they have an extreme value for a single variable, or multivariate when they have an unusual combination of values on a number of variables.

Outliers are the extreme values of a distribution that are characterized by being extremely high or extremely low compared to the rest of the distribution, and thus representing isolated cases with respect to the rest of the distribution.

There are different methods to detect the outliers, we will use the Tukey's method which uses the interquartile range (IQR) range approach. This method is not dependent on distribution...

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