Linear regression is used to predict the value of one variable from another variable. Since It Is based on correlation, It cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction. For example, given this data on literacy and undernourishment, we can create a scatter plot which shows that there seems to be a relationship between the variables. Literacy and Undernourishment Y - -0 5539% + 55.621 1 0.8341 20 The graph implies that as literacy (X) increases, the percentage of people who are undernourished (y) decreases. We can calculate a best fit line equation and use this to predict that the undernourishment rate we would expect in a country with a percentage literacy rate of 87% would be y = (-015539) 87)+55.621 or about 7.43 percent. What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe including why and how it would be used. This serves as your Initial post to the discussion (if you choose topic #2) and is due by 11:59 pm EST on Saturday