1. If the mean values of Pc, Qc, M, and PA 250, 5,000, 25,000, and 300, calculate the priceelasticity, cross-price elasticity, and income elasticity of demand for cement. Explain thesefigures.2. Write the resulting regression equation Given: The ATV Company produces a specialty cement used in the construction of roads. ATV is aprice-setting firm and estimates the demand for its cement using a demand function in the linear form:Q=f( Pc, M, PA)where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M= country's taxrevenues per capita, and PR = the price of asphalt per yard. The manager of ATV obtained the followingresults in her attempt to estimate the demand for cement in the succeeding months. The results arepresented below:%3DDEPENDENTR- SQUAREF-RATIOQcP-VALUE ON FVARIABLEOBSERVATIONS640.809384.8720.0001Р-T-VARIABLEPARAMETER ESTIMATE STANDARD ERRORVALUERATIOINTERCEPT8.204.012.040.0461Pc-3.541.64-2.160.0357M0.642870.193.380.0014PA0.78540.382.070.0439

Regression Equation: Dependent Variable: Qc Independent Variable: Pc, M, Pa This is a multiple…

1. If the mean values of Pc, Qc, M, and PA 250, 5,000, 25,000, and 300, calculate the price elasticity, cross-price elasticity, and income elasticity of demand for cement. Explain these figures. 2. Write the resulting regression equation

1. If the mean values of Pc, Qc, M, and PA 250, 5,000, 25,000, and 300, calculate the price elasticity, cross-price elasticity, and income elasticity of demand for cement. Explain these figures. 2. Write the resulting regression equation

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
Hide student question Time Left : 01:58:26 Student question Analyze the model significance using the regression statistics: R-square, t-statistics, and use the standard error to determine the range within which demand for the product will fall with a 95% confidence interval. Calculate and interpret the following elasticities: Own price, cross price, income, and advertising and explain what these results suggest about the effects of changes in each of these variables on the demand for Maa mustard oil? Based on the elasticities you have estimated in question 8, what will be the impact of a price increase by the company on the total revenue of Maa mustard oil (assuming other variables remain constant)? What is the revenue maximizing price for Maa oil if the competitors price does not increase in October 2015? Solve pls
An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
Numbers 5 adn 6 need to be answered. The function (8.6) needed in 5b has been included in a separate picture. Please show all work. Thank you.
General Cereals is using a regression model to estimate the demand for Tweetie Sweeties, a whistle-shaped, sugar-coated breakfast cereal for children. The following (multiplicative exponential) demand function is being used: QD= 6,280 P 1.35) 42.05 N 2.70 where QD quantity demanded, in 10-oz boxes P = price per box, in dollars A = advertising expenditures on daytime television, in dollars N = proportion of the population under 12 years old, in percent What is the point price elasticity of demand for Tweetie Sweeties? O 2.70 O 2.05 -0.66 -1.35 What is the advertising elasticity of demand? -1.35 O 2.70 O 0.76 O 2.05 According to the estimated model, a percent increase in the proportion of the population under 12 years old by percent. the quantity demanded
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General Cereals is using a regression model to estimate the demand for Tweetie Sweeties, a whistle-shaped, sugar-coated breakfast cereal for children. The following (multiplicative exponential) demand function is being used: QD = 6,280 P(-2.15) A1.75N2.70 where QD = quantity demanded, in 10-oz boxes P = price per box, in dollars A = advertising expenditures on daytime television, in dollars N = proportion of the population under 12 years old, in percent What is the point price elasticity of demand for Tweetie Sweeties? 1.75 -1.23 2.70 -2.15 What is the advertising elasticity of demand? 0.65 1.75 -2.15 2.70 According to the estimated model, a percent increase in the proportion of the population under 12 years old by percent. the quantity demanded
Other Companies (Part 2) You directed your research department to do some research on the demand for Tesla sedans. They selected BMW i3 Sedans and Chevy Bolts as comparative offerings. Using regression analysis, the research department comes up the following estimate for yearly demand. Qx = -40,000 -1*Px +0.02*M +2*PB +2*PC Where: Px = $70,000, M = $150,000, PB = $65,000, PC = $40,000 a. Is the own price elasticity of demand for Tesla sedans at the point defined above elastic or inelastic? If Mr. Musk decides to raise his prices, what will happen to his revenue. b. PB and PC represent the price for i3 sedans and Bolt sedans respectively. Are these items compliments or substitutes when compared to Teslas? Give evidence to support your answer. c. Is the demand for Tesla sedans elastic or inelastic to price changes of BMW i3 and Bolt at the price points given in the problem? Interpret the result you find and explain what it means.
Determine and discuss the impact of diesel prices on fuel revenues and the impact of gasoline prices on fuel revenues. What is the relationship between meal prices and meals? Define, measure, and discuss the price elasticities. HINT: Find price elasticity of demand by multiplying the slope of the regression line from Excel by the mean real price then divide by the mean quantity demanded. Please do fast ASAP
5. Confounding variables and multiple regressions Suppose you are interested in studying the effect of square footage on the price of houses. You propose the following regression of housing prices on square footage: price - Bo + P sqft + u where price the price of the house. sqft = the square footage of the house residual Failing to account for the number of bathrooms in a house, which may influence housing prices and may be positively correlated with square footage, could lead to estimate of the effect . This issue of confounding variables would likely bias the estimate of