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Statistical Application Development with R and Python

You're reading from   Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Data Characteristics FREE CHAPTER 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

Summary


In this chapter, we have introduced "statistical inference", which is a common usage term that consists of three parts: estimation, confidence intervals, and hypotheses testing. We began the chapter with the importance of likelihood and to obtain the MLE in many of the standard probability distributions using inbuilt modules. Later, simply to maintain the order of concepts, we defined functions exclusively for obtaining the confidence intervals. Finally, the chapter considered important families of tests that are useful across many important stochastic experiments.

In the next chapter, we will introduce the linear regression model, which more formally constitutes the applied face of the subject. We saw that the code development and application in Python is easier too, as we defined new tests as required and did not depend on an existing setup on the web.

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