Team 20 | MANAGIRAL ECONOMICS PROJECT 1 | Estimation of the Demand for Combo 1 meals | | Corey Siragusa 106549438 | Yujing Zhang 108672624 | Gary Zhao 108693441 | 11/7/2012 |
a) Using the data in Table 1, specify a linear functional form for the demand for Combination 1 meals, and run a regression to estimate the demand for Combo 1 meals.
According to the passage, we know that the Quantity of meals sold by Combination (Q) is related to the average price charged (P) and the dollar amount spent on newspaper ads for each week in 1998(A). The price will influence the quantity of demand with inverse relation, and ads may lead to change of demand with positive relation.
Household income and population in the suburb did…show more content… We can have strong confident to conclude that P and A variable has some correlation with Q.
However, the Adjusted R-square equals to 0.2337, which is not every large. That is to say, P and A are able to explain only 23.37% changes of quantity of demand.
d) Discuss how the estimation of demand might be improved.
From Part C, we find that the adjusted R-square is not very high. So we should increase the adjusted R-square by increasing number of variables. We believe that the estimation of demand might be improved, if we can collect more information about competitors, including their price, advertising, product improvement, etc. And the population of tourists can be consideration, even though the permanent population didn’t change obviously.
e) Using your estimated demand equation, calculate an own-price elasticity and an advertising elasticity. Compute the elasticity values at the sample mean values of the data in Table 1.
Discuss, in quantitative terms, the meaning of each elasticity.
When Mean of P=4, Mean of A=10009, then Qd= 100626.05-16392.65P +1.58A=50869.67
The own-price elasticity is -1.289, which means that if the price goes up $1, the quantity will go down 1.289, and the revenue will drop.
The advertising elasticity is 0.3109, which means that if the investment on