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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages

Table of Contents (24) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. RefresheR 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 1. Other Books You May Enjoy Index

Populations, samples, and estimation


One of the core ideas of statistics is that we can use a subset of a group, study it, and then make inferences or conclusions about that much larger group.

For example, let's say we wanted to find the average (mean) weight of all the people in Germany. One way do to this is to visit all the 81 million people in Germany, record their weights, and then find the average. However, it is a far more sane endeavor to take down the weights of only a few hundred Germans, and use these to deduce the average weight of all Germans. In this case, the few hundred people we do measure is the sample, and the entirety of the people in Germany is called the population.

Now, there are Germans of all shapes and sizes: some heavier, some lighter. If we only pick a few Germans to weigh, we run the risk of, by chance, choosing a group of primarily underweight Germans or overweight ones. We might then come to an inaccurate conclusion about the weight of all Germans. However, as...

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