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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R 2. Statistical Methods with R FREE CHAPTER 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Summary

In this chapter, we saw how to obtain useful statistical information from sample populations and probability distributions. You should now be able to summarize your data and obtain useful statistical information such as the mean, median, variance, and so on. You should also be able to plot and obtain useful statistics from theoretical probability distributions, fit your sample data to theoretical probability distributions, perform hypothesis testing using parametric and non-parametric statistical tests, and plot and test whether time series data is stationary using statistical tests.

Now that you have a foundation of how to apply statistical methods to your sample datasets, we will be moving on to linear models in the next chapter, where you will find out how to study the relationship between variables.

You have been reading a chapter from
Mastering Scientific Computing with R
Published in: Jan 2015
Publisher:
ISBN-13: 9781783555253
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