# Blame It on the Rain

### Resources for this lesson:

In this lesson, you will distinguish between correlation and causation.

Now that you have explored tools to analyze the fit of a model for a given data set, you will need to consider how you analyze the results of a correlated set of data.  If two variables show a correlation with one another (either positive or negative), does that mean that one variable causes the other to act in a certain way?  Let’s explore this question further.

Justyce and Allyson work part-time after school as student assistants at the private preschool in their neighborhood.  The school currently has an enrollment of 104 students.  Since the school is accredited through the State of Maryland, the school is required to report all child injuries (which include minor bumps, bruises and cuts).  The school director has asked them to help compile the monthly “Oops Reports” totals.

While sorting the data, Justyce notices a trend.

## Real-Life Scenarios

> Text version for Real-Life Scenario

As the girls compiled the data, they organized the data, as seen in the table below:

Month

Number of Rainy Days

Number of Injuries

March

10

50

April

14

55

May

9

48

June

7

40

July

4

36

August

5

37

Justyce concludes that rainy weather causes an increase in student injuries.