A popular U.S. automobile manufacturer has 10,000 dealerships located throughout the country. The automobile manufacturer has multiple brands within its portfolio: a value brand that caters to younger clientele, a moderate brand that caters to middle class customers and finally, a premium brand which is marketed to wealthy clientele. The company's leadership, located at corporate headquarters, is very interested in the relationship between the median salary of potential customers and the company's revenue. Specifically, the company is concerned that if potential customers' salaries continue to not increase in the future, the company's revenue will remain stagnant, which will in turn steer away potential investors and shareholders. The company's research department recently collected data for analysis in order to support leadership's upcoming discussion with shareholders and investors about the company's future revenue forecast. Sales figures from a random sample of 1000 dealerships were collected. The research division also conducted statistical analysis, using data provided by the Bureau of Labor and Statistics, to calculate the median salary of people living in the vicinity of these 1,000 dealerships. The Dealership Number, State, Median Salary, Annual Sales, Number of Vehicles Sold, Square Footage and Quality Award Winner data were collected for these 1000 dealerships.

Simple linear regression results: Dependent Variable: Annual Sales Independent Variable: Median Salary Annual Sales = 251182.44 + 1495.2028 Median Salary Sample size: 1000 R (correlation coefficient) = 0.85053014 R-sq = 0.72340153 Estimate of error standard deviation: 11416570 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 251182.44 1169455 #0 998 0.2147859 0.83 Slope 1495.2028 29.266447 #0 998 51.089316