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Regression Analysis with R

You're reading from  Regression Analysis with R

Product type Book
Published in Jan 2018
Publisher Packt
ISBN-13 9781788627306
Pages 422 pages
Edition 1st Edition
Languages
Author (1):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Toc

Table of Contents (15) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Getting Started with Regression 2. Basic Concepts – Simple Linear Regression 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 1. Other Books You May Enjoy Index

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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