Exercise

# Variance and standard deviation

Variance and standard deviation are two of the most common ways to measure the spread of a variable, and you'll practice calculating these in this exercise. Spread is important since it can help inform expectations. For example, if a salesperson sells a mean of 20 products a day, but has a standard deviation of 10 products, there will probably be days where they sell 40 products, but also days where they only sell one or two. Information like this is important, especially when making predictions.

Both `dplyr`

and `ggplot2`

are loaded, and `food_consumption`

is available.

Instructions

**100 XP**

- Calculate the variance and standard deviation of
`co2_emission`

for each`food_category`

by grouping by and summarizing variance as`var_co2`

and standard deviation as`sd_co2`

. - Create a histogram of
`co2_emission`

for each`food_category`

using`facet_wrap()`

.