Agnes, a General Manager in XXX Company, estimated a multiplicative demand function of the form: using a cross-section data collected in the company sales on 30th June, 2019. The estimation results are as follows: Constant Price(P) Income (I) Price of other Good (Po) Estimated coefficient 0.022 -0.223 1.354 0.133 Standard Error 0.012 0.056 0.502 0.814 t-statistic (1.19) (-3.98) -2.69 -0.13 Number of Observations, n=210; R-squared= 0.7516 Critical Students t=1.96 at 5% Level of Significance Write down the estimated demand equation Interpret the coefficients and value Describe any three managerial decisions that can be applied by the manager from the estimated demand function

Agnes, a General Manager in XXX Company, estimated a multiplicative demand function of the form: using a cross-section data collected in the company sales on 30th June, 2019. The estimation results are as follows: Constant Price(P) Income (I) Price of other Good (Po) Estimated coefficient 0.022 -0.223 1.354 0.133 Standard Error 0.012 0.056 0.502 0.814 t-statistic (1.19) (-3.98) -2.69 -0.13 Number of Observations, n=210; R-squared= 0.7516 Critical Students t=1.96 at 5% Level of Significance Write down the estimated demand equation Interpret the coefficients and value Describe any three managerial decisions that can be applied by the manager from the estimated demand function

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 2E

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Agnes, a General Manager in XXX Company, estimated a multiplicative demand function of the form: using a cross-section data collected in the company sales on 30th June, 2019. The estimation results are as follows: (SEE IMAGE) Write down the estimated demand equation Interpret the coefficients and R2 value Describe any three managerial decisions that can be applied by the manager from the estimated demand function
The ATV Company produces a specialty cement used in the construction of roads. ATV is a price-setting firm and estimates the demand for its cement using a demand function in the linear form: Q = f (P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard. The manager of ATV obtained the following results in her attempt to estimate the demand for cement in the succeeding months. The results are presented below: DEPENDENT VARIABLE Qc R- SQUARE F-RATIO P-VALUE ON F OBSERVATIONS 64 0.8093 84.872 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54…
The ATV Company produces a specialty cement used in the construction of roads. ATV is a price-setting firm and estimates the demand for its cement using a demand function in the linear form: Q = f (P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard. The manager of ATV obtained the following results in her attempt to estimate the demand for cement in the succeeding months. The results are presented below: DEPENDENT VARIABLE Qc R- SQUARE F-RATIO P-VALUE ON F OBSERVATIONS 64 0.8093 84.872 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54…
The ATV Company produces a specialty cement used in the construction of roads. ATV is a price-setting firm and estimates the demand for its cement using a demand function in the linear form: Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard. The manager of ATV obtained the following results in her attempt to estimate the demand for cement in the succeeding months. The results are presented below: DEPENDENT VARIABLE Qc R- SQUARE F-RATIO P-VALUE ON F OBSERVATIONS 64 0.8093 84.872 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54…
The ATV Company produces a specialty cement used in the construction of roads. ATV is a price-setting firm and estimates the demand for its cement using a demand function in the linear form: Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard. The manager of ATV obtained the following results in her attempt to estimate the demand for cement in the succeeding months. The results are presented below: DEPENDENT VARIABLE Qc R- SQUARE F-RATIO P-VALUE ON F OBSERVATIONS 64 0.8093 84.872 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54…
using picture attached Over the range from $20 to $18, Qd goes from 12 to 17. Using this range of prices and quantities, you should calculate the coefficient of price elasticity of demand. In the box labeled H1, the coefficient of price elasticity of demand is: 3.28 5 3.01 2.97 In box H2, you would interpret the coefficient calculated in the previous question. Therefore, you would characterize this range as: Elastic Unit Elastic Inelastic None of the Above Using the dataset above, if prices increased from $8 to $10, then Total Revenue will: Increase Decrease None of the above
Recently, Verizon Wireless ran a pricing trial in order to estimate the elasticity of demand for its services. The manager selected three states that were representative of its entire service area and increased prices by 5 percent to customers in those areas. One week later, the number of customers enrolled in Verizon’s cellular plans declined 4 percent in those states, while enrollments in states where prices were not increased remained flat. The manager used this information to estimate the own price elasticity of demand and, based on her findings, immediately increased prices in all market areas by 5 percent in an attempt to boost the company’s 2012 annual revenues. One year later, the manager was perplexed because Verizon’s 2012 annual revenues were 10 percent lower than those in 2011—the price increase apparently led to a reduction in the company’s revenues. Did the manager make an error? Explain
The REDY Company produces a specialty cement used in the construction of roads. REDY is a price-setting firm and estimates the demand for its cement using a demand function in the linear form: Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard. The manager of REDY obtained the following results in her attempt to estimate the demand for cement in the succeeding months. The results are presented below: DEPENDENT VARIABLE Qc R- SQUARE F-RATIO P-VALUE ON F OBSERVATIONS 64 0.8093 84.872 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M…
The Camera Shop sells two popular models of digital SLR cameras. The sales of these products are not independent of each other, but rather if the price of one increase, the sales of the other will increase. In economics, these two camera models are called substitutable products. The store wishes to establish a pricing policy to maximize revenue from these products. A study of price and sales data shows the following relationships between the quantity sold (N) and prices (P) of each model:
Using regression analysis on data from a field experiment, the demand curve for a product is estimated to be QXd = 1,200 − 3PX − 0.1PZ where Pz = $300. a. What is the own price elasticity of demand when Px = $140? Is demand elastic or inelastic at this price? b. What is the own price elasticity of demand when Px = $240? Is demand elastic or inelastic at this price? c. What is the cross-price elasticity of demand between good X and good Z when Px = $140?
Recently, Pacific Cellular ran a pricing trial in order to estimate the elasticity of demand for its services. The manager selected three states that were representative of its entire service area and increased prices by 5 percent to customers in those areas. One week later, the number of customers enrolled in Pacific’s cellular plans declined 4 percent in those states, while enrollments in states where prices were not increased remained flat. The manager used this information to estimate the own price elasticity of demand and, based on her findings, immediately increased prices in all market areas by 5 percent in an attempt to boost the company’s 2012 annual revenues. One year later, the manager was perplexed because Pacific Cellular’s 2012 annual revenues were 10 percent lower than those in 2011 - the price increase apparently led to a reduction in the company’s revenues. Did the manager make an error? Explain.
The multivariate demand function below will be needed for questions 12-18. Setting: Grapple, Inc. is a leading seller of laptop personal computers. However, they want to become a leading tablet seller, too. Your marketing department, aided by your economics staff, has estimated a function to help you in the quest for market leader in tablets. The variables are defined after the function. Qg = 10000 - 25Pg + 20Ph + 30Pr - 15dv - 35Psc - 10Pmm + 0.05Ag + 0.03A -25C + 0.1Y Qg = the number of Grapple tablet computers demanded per week. Pg = the price of each new Grapple tablet (in $). Ph = the price of each Hewpaq tablet (in $). Pr = the price of each Ronova tablet. Pdv = the price to equip a tablet with Holographic digital video (in $, this is an upgrade option that enables three-dimensional graphics on a tablet. Two-dimensional graphics is standard equipment). Psc = the price of various screen sizes (in $, a 8 inch is standard, but upgrade options are 10…

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	Constant	Price(P)	Income (I)	Price of other Good (Po)
Estimated coefficient	0.022	-0.223	1.354	0.133
Standard Error	0.012	0.056	0.502	0.814
t-statistic	(1.19)	(-3.98)	-2.69	-0.13
*Number of Observations, n=210; R-squared= 0.7516*
*Critical Students t=1.96 at 5% Level of Significance*