Using sales and unemployment data, answer the question "which elements best explain why department sales differ?" To do this, compute the regression models - Regress total sales for each department on unemployment. Interpret the output for each variable and discuss the important business insights you can derive from the model results SUMMARY OUTPUT Regression Statistics Multiple R 0.026 R Square 0.001 Adjusted R Square 0.001 Standard Error 22,704 Observations 421,570 ANOVA Significance F df SS MS F Regression 1 145.455,471,781 145,455,471,781 282.189 0.000 Residual 421,568 217,298,931,238,867 515.454,046 Total 421,569 217,444,386,710,648 Standard Error P- value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Coefficients t Stat Intercept 18,490.703 153.423 120.521 18,189.998 18,791.408 18,189.998 18,791.408 Unemploymen t (315.245) 18.766 (16.79) 0.000 (352.027) (278.464) (352.027) (278.464) Using sales and unemployment data, answer the question "which elements best explain why department sales differ?" To do this, compute the regression models - Regress total sales for each department on unemployment. Interpret the output for each variable and discuss the important business insights you can derive from the model results SUMMARY OUTPUT Regression Statistics Multiple R 0.026 R Square 0.001 Adjusted R Square 0.001 Standard Error 22,704 Observations 421,570 ANOVA Significance F df SS MS F Regression 1 145.455,471,781 145,455,471,781 282.189 0.000 Residual 421,568 217,298,931,238,867 515.454,046 Total 421,569 217,444,386,710,648 Standard Error P- value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Coefficients t Stat Intercept 18,490.703 153.423 120.521 18,189.998 18,791.408 18,189.998 18,791.408 Unemploymen t (315.245) 18.766 (16.79) 0.000 (352.027) (278.464) (352.027) (278.464)