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

Variance

Standard Deviation

Standard Deviation Units

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

1
Variance
Finding the mean, median, and mode of a dataset is a good way to start getting an understanding of the general shape of your data However, those three descriptive statistics only tell part of the …
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2
Distance From Mean
Now 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 …
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3
Average Distances
We 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…
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4
Square the Differences
We’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…
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5
Variance in R
Well 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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6
Review
Great work! In this lesson you’ve learned about variance and how to calculate it. In the example used in this lesson, the importance of variance was highlighted by showing data from test scores in…
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