Zscores

Recall the concept of Z scores from the lectures. Z scores are standardized scores how far a parameter is removed from its mean. A Z score with value 2 means that an observation is 2 standard deviations away from its population mean. Also recall that the formula for the Z score is the following:

\(Zi = \frac{(xi - \mu)} {\sigma}\)

In this formula, \(xi\) refers to the observation for which you want to calculate the z score, while \(\mu\) refers to the population mean of the phenomen and \(\sigma\) refers to the population standard deviation.

To illustrate the concept of the Z score, let's go back to our scandinavia_data dataset. In this population of male hipsters from Scandinavia, the average beard length is 25. The standard deviation in the population is 3.47. Suppose we had a hipster with a beard length of 32mm, this would be unusual for this population and thus would have a rather high Z score.

Calculate the Z score of this hipster with a beard of 32 millimeter and store it in a variable called z_score
print the variable z_score to the console

Exercise

Zscores

Instructions