B BUS 310 Assume that, using the regression analysis tool in Excel to estimate the demand curve for daily ski lift tickets at a particular mountain resort, you obtained the following summary output: SUMMARY OUTPUT Regression Statistics Multiple R 0.743862 R Square 0.553331 Adjusted R Squa 0.547376 Standard Error 54.77761 Observations 77 ANOVA df SS MS F Significance F Regression 1 278783.68 278783.7 92.91 9.1512E-15 Residual 75 225044 3000.587 Total 76 503827.68 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 863.347 24.174367 35.71332 8E-49 815.189192 911.50481 799.4546 927.23944 X Variable 1 -3.487667 0.3618298 -9.63897 9E-15 -4.20846879 -2.766865 -4.443977 -2.531357 1. Based on this data, if you were to charge $70 for a lift ticket, how many would you expect to sell? Explain how you obtained the answer. Round to the nearest integer. 2. If the price of lift tickets would decrease by $10, what would be the anticipated change in the quantity demanded? Indicate if the quantity would increase or decrease, and by how much. Explain how you reached that conclusion. 3. What percentage of the variation in the number of tickets demanded is explained by the + price? Explain how you obtained that number. List at least one other factor (besides the price) that might influence the number of lift tickets demanded on a particular day