MBA 600 Managerial Economics

MBA 600 Managerial Economics

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MBA 600 Managerial Economics

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MBA 600 Managerial Economics

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Course Code: MBA600
University: University Of Dubai is not sponsored or endorsed by this college or university

Country: United Arab Emirates

1.Should you use the ordinary least squares (OLS) method or the two-stage least-squares method (2OLS) method for estimating the demand? Explain briefly.2.Using statistical software, estimate the parameters of the empirical demand function specified in part a.  Write your estimated demand equation.  
3.Evaluate your regression results by examining signs of parameters, p-values (or t-ratios), and the R2.
4.Discuss how the estimation of demand might be improved. 
From the various residual plots highlighted above, it is evident that the distribution of residuals is in a random pattern with no specific pattern being observed in either of the cases. The presence of random arrangement is indicative of the independence of the error or residual terms of the independent variables (i.e. A & P). As a result, a one-step OLS would be appropriate for this particular regression analysis.
Based on the regression output obtained from Excel, it is apparent that the intercept is 50,312.71 while the corresponding slope coefficients for P & A are -4098.14 and 0.39 respectively. 
D = 50312.71 – 4098.14P + 0.39A
Price (P) – The slope coefficient of price is negative which is expected as price and demand are inversely related. As the price of the meal would increase, the demand should decrease which is the case here.  Further, the T statistic associated with the slope is -3.21 with an associated p value of zero. This highlights the fact that the slope coefficient of price variable cannot be assumed to be zero and hence is significant.
Advertisement (A) – The slope coefficient of price is positive which is expected as price and advertising spend are directly related. As the advertising spending on the meal would increase, the demand should also increase from the customer which is the case here.  Further, the T statistic associated with the slope is 2.61 with an associated p value of 0.01. This highlights the fact that the slope coefficient of advertisement variable cannot be assumed to be zero and hence is significant.
The intercept value of 50312.71 indicates the demand when P and A both would be zero.
The R2 value is 0.2638 which implies that 26.38% of the variation in demand of meal is jointly explained by changes in the price and advertising spend.
It is apparent that the R2 value for the given model is quite less even though both the independent variables are significant. As a result, in order to enhance the explanatory and predictive power of the given regression model, it is essential to insert additional independent variables which would contribute to explaining the demand for meals. This is essential so as to enhance the overall utility of the model.
Mean price = $ 7.01, Mean weekly advertising spend = 20,017.88
D = 50312.71 – 4098.14P + 0.39A
Hence, D = 50312.71 -4098.14*7.01 + 0.39*20017.88 = 29,459.20
New price = 7.01*1.01= $ 7.08
Hence, D = 50312.71 -4098.14*7.08 + 0.39*20017.88 = 29,171.78
Change in demand = [(29171.78-29459.20)/29459.2]*100 = -0.98%
Thus, price elasticity of demand = -0.98/1 = -0.98
It is evident that the demand is slightly inelastic as the elasticity coefficient is lesser than 1 but only marginally.
New advertising spend = 20017.88*1.01 = $20,218.06
Hence, D = 50312.71 -4098.14*7.01 + 0.39*20218.06 = 29,538.09
Change in demand = [(29538.09.78-29459.20)/29459.2]*100 = 0.267%
Thus, advertisement elasticity of demand = 0.267/1 = 0.267
It is evident that the demand is highly inelastic with regards to advertisement spending as the elasticity coefficient is lesser than 1. This implies to cause a 1% increase in sales, the advertisement expenditure would be greater than 3.5%.

The regression equation is indicated below.

D = 50312.71 – 4098.14P + 0.39A
In the given case, P = $ 8.5, A= $ 36,000
Substituting the above inputs, we get
D = 50312.71 – 4098.14*8.5 + 0.39*36000 = 29,666 approximately
Hence, the estimated weekly demand of Combination 1 meal would be 29.666.

The regression equation is indicated below.

D = 50312.71 – 4098.14P + 0.39A
It is known that A = $ 36,000
Hence, D = 50312.71 – 4098.14P + 0.39*36000
D = 50312.71 – 4098.14P + 14186.88
4098.18P = 64499.59 – D
Therefore, the inverse demand function can be written as follows.
P = 15.74 – D/4098.18
Putting D= 55,000
P = 15.74 – (55000/4098.18) = $2.32
Hence, the requisite price for Combination 1 meal should be fixed as $ 2.32.

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