The Results In the second example, we looked at the relationship between sales dollars and inventory dollars. Taking the same sample twelve-month sample of numbers from 2005 provided the data for the second equation. We plotted the values on a graph, with sales on the x-axis and inventory on the y-axis. If there were a perfect linear relationship between inventory dollars and sales dollars, then all 12 points on the graph would fit on a straight line. However, as expected, this is not the case for the second graph. A similar linear relationship between sales dollars and inventory dollars was found compared to the first graph of inventory and sales dollars. As the dollar amount of inventory rises, so did the inventory dollars. The results show a positive correlation between the two because the cluster of points on the graph slopes upward. If the inventory dollars declined and the sales dollars continued to rise, we would no longer have the favorable linear relationship. The regression analyses provided equations of the lines that fit through the cluster of points with the minimal amount of deviations from the line. The deviations of the points from the line are considered error points. Once we determined the regression equations, we can use the information to determine the sales dollars given the inventory dollars or the inventory dollars given the sales dollars for 2005. State the Limitations When performing a regression analysis, valuable data is collected and analyzed for predicting the future with that relationships do exist between two variables or subjects. The ability to understand causes and predict outcomes of events is critical in any business. We intuitively have the ability to detect that variables are related. The concepts of regression and correlation, however, provide us with a means to establish concrete evidence of such relationships. From known data, we determine how the values of variables have a linear relationship and then used that...