Final answer:
The interpretation of the correlation coefficient (r) is made clearer by using scatter plots, which allows for a visual representation of data and the calculation of the least-squares regression line to establish the strength and direction of a linear relationship.
Step-by-step explanation:
The interpretation of r, which refers to the correlation coefficient, becomes more intuitive when we use scatter plots to visualize the data. A scatter plot helps us see the relationship between the independent and dependent variables, indicating whether a linear relationship exists. To assess the nature of the association between the variables more quantitatively, we calculate the least-squares regression line, often written in the form ý = a + bx, and draw it on the scatter plot for a visual representation of the linear relationship.
Moreover, finding the correlation coefficient is essential to determine the strength and direction of the linear relationship between the two variables. If the value of r is significant, it implies a strong linear relationship. Lastly, by observing the pattern of the points in relation to the best-fit line, we can interpret whether a linear or a non-linear model is more appropriate for the data set.