You are working at Amazon and are analyzing what factors lead to a product having a higher rating. You run a regression to estimate the following equation: Rating = Bo + 1 (Price) + B2(Discuont %) + By(Number of Ratings) + e Variable Descriptions: Rating - The average rating of the product from 1-5 stars Price - The price of the product measured in dollars Discount % - The percentage of the discount being offered (0-100%) Number of Ratings - The number of times the product has been rated SUMMARY OUTPUT Regression Statistics Multiple R 0.232660421 R Square 0.054130872 Adjusted R Square 0.052118384 Standard Error 0.284360709 Observations 1414 ANOVA df SS MS F Significance F Regression 3 6.524875939 2.174958646 26.89749453 6.45167E-17 Residual 1410 114.0140279 0.080861013 Total 1413 120.5389038 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 4.148693265 0.019919207 208.2760232 0 4.109618795 4.187767735 Price 2.6632E-06 7.2937E-07 3.651366196 0.000270388 1.23243E-06 4.09397E-06 Discount (%) -0.002017497 0.000351389 -5.741499829 1.14749E-08 -0.002706798 -0.001328197 Number of Ratings 2.2418-06 4.29244E-07 5.22081778 2.04799E-07 1.39898E-06 3.08303E-06 What would be the predicted rating of a product that costs $80, has a 10% discount, and 20,000 ratings? 4.17 stars 3 stars 4.19 stars 45015 stars