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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
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 (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Performing a student's t-test


A one sample t-test enables us to test whether two means are significantly different; a two sample t-test allows us to test whether the means of two independent groups are different. Using t-tests, we can find how significant the difference is and if the difference has happened by chance. In this recipe, we will discuss how to conduct one sample t-test and two sample t-tests using R.

Getting ready

Ensure that mtcars has already been loaded into a DataFrame within an R session. As the t.test function originates from the stats package, make sure the library, stats, is loaded.

How to do it...

Perform the following steps:

  1. First, we visualize the attribute, mpg, against am using a boxplot:
        > boxplot(mtcars$mpg, mtcars$mpg[mtcars$am==0], ylab = "mpg",
        names=c("overall","automobile"))
        > abline(h=mean(mtcars$mpg),lwd=2, col="red")
        > abline(h=mean(mtcars$mpg[mtcars$am==0]),lwd=2, col="blue")

The boxplot of mpg of the overall population...

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