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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR FREE CHAPTER 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 18. Other Books You May Enjoy

Estimating means


In the example that is going to span this entire chapter, we are going to be examining how we would estimate the mean height of all US women using only samples. Specifically, we will be estimating the population parameters using samples means as an estimator.

I am going to use the vector all.us.women to represent the population. For simplicity's sake, let's say there are only 10,000 US women:

 > # setting seed will make random number generation reproducible 
 > set.seed(1) 
 > all.us.women <- rnorm(10000, mean=65, sd=3.5) 

We have just created a vector of 10,000 normally distributed random variables with the same parameters as our population of interest using the rnorm function. Of course, at this point, we can just call mean on this vector and call it a day—but that's cheating! We are going to see that we can get really really close to the population mean without actually using the entire population.

Now, let's take a random sample of ten from this population using...

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