Book Image

Statistical Application Development with R and Python - Second Edition

Book Image

Statistical Application Development with R and Python - Second Edition

Overview of this book

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
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
Index

Confidence intervals


The MLE is a point estimate and as such, on its own, it is almost of no practical use. It would be more appropriate to give coverage of parameter points, which is most likely to contain the true unknown parameter. A general practice is to specify the coverage of the points through an interval and then consider specific intervals that have a specified probability. A formal definition is in order.

A confidence interval for a population parameter is an interval that is predicted to contain the parameter with a certain probability.

The common choice is to obtain either 95 percent or 99 percent confidence intervals. It is common to specify the coverage of the confidence through a significance level , more about this in the next section, which is a small number closer to 0. The 95 percent and 99 percent confidence intervals then correspond to percent intervals with respective equal to 0.05 and 0.01. In general, a percent confidence interval says that if the experiment is...