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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Introduction


To handle the uncertainty of real-world events, we can use probability to measure the likelihood of whether an event will occur. By definition, probability is quantified with a number between 0 and 1; the higher the probability (closer to 1), the more certain we are that an event will occur.

As statistical inference is used to deduce the properties of a given population, knowing the probability distribution of a given population becomes essential. For example, if you find that the data selected for prediction does not follow the exact assumed probability distribution in experiment design, the results should be refuted. In other words, probability provides justification for statistical inference.

In this chapter, we focus on the topic of probability distribution and simulation. We first discuss how to generate random samples, before covering how to use R to generate samples from various distributions such as normal, uniform, Poisson, chi-squared, and Student's t-distribution. We...

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