If there is a strong correlation between two variables, it's easy to jump to the conclusion that one causes y and causes X; or that a third factor, Z (or even a whole set of other factors)
Correlation occurs when we can observe a trend between the response/dependent variable (y) and the explanatory/independent variable (x).
When comparing two sets of data, we may observe and use correlation to determine whether or not the data presented if significant or not and whether or not it supports our hypothesis. We may want to see if this is just a fluke and whether or not the trend causes causation.
An example of this would be if we thought that the weight of female mice determines how many kids they have in a month.. Let's say that mice that weigh up to one ounce have 6 baby mice per month.
Let's say that the x variable is the weight which is between 0.25 oz and 1.25 oz and the y-variable is the amount of kids between each month. If another laboratory conducts the same study with the same type of mice with the same weights as us, we would need to determine if there is correlation and if there is causation and try to use this information to determine if both sets of data are significant to our hypothesis.
Perhaps the easiest way to do this is to draw the needed shape in the box, then locate it among the answer choices. Use the identifying features such as dot location and direction of arrow (or tail).
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Left-to-right, this is what you're trying to match:
