Final answer:
To determine the association type in a scatterplot, look at the direction of the points: rising points suggest a positive association; falling points indicate a negative association. A straight line pattern shows a linear association, making the variables suitable for linear regression. A curved pattern, however, suggests a non-linear association and may require different modeling methods.
Step-by-step explanation:
To interpret the scatterplot and determine what type of association the scatterplot shows, you would look at the general direction of the points on the graph. If the points tend to rise from left to right, this indicates a positive association—as one variable increases, so does the other. On the contrary, if the points tend to fall from left to right, this signifies a negative association—as one variable increases, the other decreases. A linear association means the data points form a pattern that looks like a straight line, either going upwards (positive) or downwards (negative). If the points do not follow a straight line but still show a specific pattern, such as a curve, this is a non-linear association.
If the scatterplot shows a straight line pattern, indicating a direct relationship between the two variables, then these variables would be good candidates for linear regression. Linear regression is a statistical method used to model the relationship between two variables by fitting a linear equation to the observed data. However, if the scatterplot indicates that a curve would be more appropriate, statisticians might prefer other methods to model the relationship.