Excel Link:https://docs.google.com/spreadsheets/d/1DTPXKxA-pbKAvG1XdBRLjMAn9WNCRIPY/edit?usp=sharing&ouid=111846680843393654433&rtpof=true&sd=true

An antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P10_18.xlIsx contains data on these three variables for several recently auctioned comparable items. Let X1 represent the age of item. Let X2 represent the number of bidders. Then, the estimated multiple regression equation is = -1336.72 + 12.74X1 + 85.82X2, R-square = 0.893. Suppose that the antique collector believes that the rate of increase of the auction price with the age of the item will be driven upward by a large number of bidders. Revise the multiple regression equation by adding an interaction term involving the age of the item and the number of bidders a. Estimate your revised equation. Let X3 represent the interaction (age of item, number of bidders). Round your answers to three decimal places, if necessary. If your answer is negative number, enter "minus" sign. Y= -1336.72 Q + 12.74 Q X1 + 85.82 Q X2 + 0x3 b. Interpret each of the estimated coefficients in your revised model. Round your answers to two decimal places, if necessary. As the age of the item increases by 1 year, the w typically @ by 12.74 0 @ and @ by the product of 0 @ and the current @, while holding the & constant. As the number of bidders increases by 1, the @ typically @ by 0 @ and by the product of Q and the current &, while holding the & constant. c. Does this revised model fit the given data better than the original multiple regression model? @, the revised model yields a R-square and thus fits the given data & than the original model. The interaction term appears & add significantly to the overall explanatory power of the model