Perhaps the most important step in examining the relationship between two variables is to create a scatterplot. A scatterplot is simply a graph which plots an individuals' score on one variable (e.g. arm strength) against their score on a second variable (e.g. supervisory ratings).

Scatterplots are used to examine any general trends in the relationship between two variables. If scores on one variable tend to increase with correspondingly high scores of the second variable, a positive relationship is said to exist. If high scores on one variable are associated with low scores on the other, a negative relationship exists.

The extent to which the dots in a scatterplot cluster together in the form of a line indicates the strength of the relationship. Scatterplots with dots that are spread apart represent a weak relationship.

Below are scatterplots for arm and grip strength against supervisor ratings and work simulations.

Interpretation:

Which of the following statements can we conclude is NOT true?
The scatterplots above indicate that strength scores (arm and grip) tend to be positively related with ratings and work simulation.
Individuals with lower strength scores tended to receive lower ratings and perform worse on the simulations than stronger individuals.
The scatterplots indicate that arm and grip scores were more strongly related to work simulation scores than supervisory ratings.
Grip scores and work simulation scores appear to have a lower correlation coefficient than grip scores and ratings.