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Statistical Application Development with R and Python - Second Edition

You're reading from Statistical Application Development with R and Python - Second Edition

Product type Book

Published in Aug 2017

Publisher

ISBN-13 9781788621199

Pages 432 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (19) Chapters

Statistical Application Development with R and Python - Second Edition

Credits

About the Author

Acknowledgment

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Data Characteristics

2. Import/Export Data

3. Data Visualization

4. Exploratory Analysis

5. Statistical Inference

6. Linear Regression Analysis

7. Logistic Regression Model

8. Regression Models with Regularization

9. Classification and Regression Trees

10. CART and Beyond

Index

The simple linear regression model

In Example 4.6.1. Resistant line for the IO-CPU time of Chapter 4, Exploratory Analysis, we built a resistant line for CPU_Time as a function of the No_of_IO processes. The results were satisfactory in the sense that the fitted line was very close to covering all the data points (refer to the Resistant line for CPU_Time figure of Chapter 4, Exploratory Analysis). However, we need more statistical validation of the estimated values of the slope and intercept terms. Here, we take a different approach and state the linear regression model in more technical details.

The simple linear regression model is given by , where X is the covariate/independent variable, Y is the regressand/dependent variable, and ε is the unobservable error term. The parameters of the linear model are specified by and . Here, is the intercept term and corresponds to the value of Y when x = 0. The slope term, , reflects the change in the Y value for a unit change in X. It is also common...

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