Business Analytics: 1.4.2 Correlation 1.4.2 Correlation As we have seen, scatter plots are useful for visualizing the relationship between two variables. It is also helpful to have a measure that quantifies how strong the relationship is. We will use the correlation coefficient to measure the strength of the linear relationship between two variables. The correlation coefficient measures the extent to which the data points on the scatter plot create a line, on a scale from -1 to +1. Move the slider to see how a scatter plot changes as the correlation changes. X -1 0Even when the correlation coefficient is 0, a relationship between two variables might exist- just not a linear one. The relationship may appear more like a curve, for example, as shown below. Never rely solely on the correlation coefficient. Always consult a visual representation of the data such as a scatter plot to see patterns and gain other insights into the situation the data describe. Correlation Coefficient - - - L -1 0\fv Access image details Weight versus Height of Red Sox Players scatter plot. The Yaxis rises from 0 to 300 pounds. The X- axis runs from 64 to 80 inches. There are 10 data points in the middle of the graph, rising diagonally from (68 inches, 160 pounds) to (76 inches, 250 pounds). Most of the data points are clustered between (72 inches, 190 pounds) and (75 inches, 210 pounds). Height (inches) Weight (pounds) 155 200 200 210 200 What do you estimate to be the correlation coefcient between the height and weight of the ten Red Sox players