Learn R: Variance and Standard Deviation
In this module, you will learn how to quantify the spread of the dataset by calculating the variance and standard deviation in R.
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Key Concepts
Review core concepts you need to learn to master this subject
Interpretation of Variance
Interpretation of Variance
A larger variance means the data is more spread out and values tend to be far away from the mean. A variance of 0 means all values in the dataset are the same.
Variance in R
Lesson 1 of 2
- 2Now that you have learned the importance of describing the spread of a dataset, let’s figure out how to mathematically compute this number. How would you attempt to capture the spread of the data …
- 3We now have five different values that describe how far away each point is from the mean. That seems to be a good start in describing the spread of the data. But the whole point of calculating vari…
- 4We’re almost there! We have one small problem with our equation. Consider this very small dataset: c(-5, 5) The mean of this dataset is 0, so when we find the difference between each point and th…
- 5Well done! You’ve calculated the variance of a data set. The full equation for the variance is as follows: \sigma^2 = \frac{\sum_{i=1}^{N}{(X_i -\mu)^2}}{N} Let’s dissect this equation a bit. * …
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Stress-test your knowledge with quizzes that help commit syntax to memory
Learn R: Variance and Standard Deviation
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