1. 1
    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 …
  2. 2
    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 …
  3. 3
    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…
  4. 4
    We’re almost there! We have one small problem with our equation. Consider this very small dataset: [-5, 5] The mean of this dataset is 0, so when we find the difference between each point and the…
  5. 5
    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. * …
  6. 6
    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…
  1. 1
    When beginning to work with a dataset, one of the first pieces of information you might want to investigate is the spread — is the data close together or far apart? One of the tools in our st…
  2. 2
    Variance is a tricky statistic to use because its units are different from both the mean and the data itself. For example, the mean of our NBA dataset is 77.98 inches. Because of this, we can say s…
  3. 3
    There is a NumPy function dedicated to finding the standard deviation of a dataset — we can cut out the step of first finding the variance. The NumPy function std() takes a dataset as a param…
  4. 4
    Now that we’re able to compute the standard deviation of a dataset, what can we do with it? Now that our units match, our measure of spread is easier to interpret. By finding the number of standa…
  5. 5
    In the last exercise you saw that Lebron James was 0.55 standard deviations above the mean of NBA player heights. He’s taller than average, but compared to the other NBA players, he’s not absurdly …

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