The tables to the right give price-demand and price-supply data for the sale of soybeans at a grain market, where x is the number of bushels of soybeans (in thousands of bushels) and p is the price per bushel (in dollars). Use quadratic regression to model the price-demand data and linear regression to model the price-supply data. Complete parts (A) and (B) below. (A) Find the equilibrium quantity and equilibrium price. The equilibrium quantity is thousand bushels. (Round to three decimal places as needed.) Price-Demand p=D(x) 6.67 6.54 6.47 6.42 6.41 X 0 10 20 30 40 Price-Supply p= S(x) 6.34 6.35 6.40 6.43 6.48 X 0 10 20 30 40

The tables to the right give price-demand and price-supply data for the sale of soybeans at a grain market, where x is the number of bushels of soybeans (in thousands of bushels) and p is the price per bushel (in dollars). Use quadratic regression to model the price-demand data and linear regression to model the price-supply data. Complete parts (A) and (B) below. (A) Find the equilibrium quantity and equilibrium price. The equilibrium quantity is thousand bushels. (Round to three decimal places as needed.) Price-Demand p=D(x) 6.67 6.54 6.47 6.42 6.41 X 0 10 20 30 40 Price-Supply p= S(x) 6.34 6.35 6.40 6.43 6.48 X 0 10 20 30 40

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 3E

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The tables to the right give price-demand and price-supply data for the sale of soybeans at a grain market, where x is the number of bushels of soybeans (in thousands of bushels) and p is the price per bushel (in dollars). Use quadratic regression to model the price-demand data and linear regression to model the price-supply data. Complete parts (A) and (B) below. (A) Find the equilibrium quantity and equilibrium price. The equilibrium quantity is thousand bushels. (Round to three decimal places as needed.) Price-Demand p = D(x) 6.59 6.48 6.38 6.27 6.24 (Round to the nearest dollar as needed.) X 0 10 20 30 40 The equilibrium price is $ per bushel. (Round to the nearest cent as needed.) (B) Use a numerical integration routine to find the consumers' surplus and producers' surplus at the equilibrium price level. The consumers' surplus is $ (Round to the nearest dollar as needed.) The producers' surplus is $ Price-Supply p = S(x) 6.40 6.47 6.53 6.54 6.61 X 0 10 8888 20 30 40
The tables to the right give price-demand and price-supply data for the sale of soybeans at a grain market, where x is the number of bushels of soybeans (in thousands of bushels) and p is the price per bushel (in dollars). Use quadratic regression to model the price-demand data and linear regression to model the price-supply data. Complete parts (A) and (B) below. (A) Find the equilibrium quantity and equilibrium price. The equilibrium quantity is thousand bushels. (Round to three decimal places as needed.) (...) Price-Demand p=D(x) 6.67 6.56 6.47 6.36 6.34 X 0 10 20 30 40 Price-Supply p= S(x) 6.46 6.54 6.60 6.61 6.68 X 0 10 20 30 40
The Ipod Touch has been out for several years now and a lot of data has been collected. There is a functional relationship between the Price of an IPod Touch and Weekly Demand. Below is a table of data that have been collected Price P ($) Weekly Demand S (1,000s) 150 207 170 208 190 192 210 190 230 185 250 169 A.. Find the linear model that best fits this data using regression and enter the model below (for entry round the slope value to nearest 0.01 and constant parameter to nearest 1) T(p) = Now answer these two questions: B.. What does the model predict will be the weekly demand if the price of an ipod touch is $191 ? (nearest 100) C.. According to the model at what should the price be set in order to have a weekly demand of 194,800 ipod Touches? $ (nearest $1) Note: In the "real" world Apple sold about 20 million Ipod Touch's from Sept. 2007-Sept. 2009
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Refrigerator prices are affected by characteristics such as whether or not the refrigerator is on sale, whether or not it is listed as a Sub- Zero brand, the number of doors (one door or two doors), and the placement of the freezer compartment (top, side, or bottom). The table below shows the regression output from a regression model using the natural log of price as the dependent variable. The model was developed by the Bureau of Labor Statistics. Variable Coefficient Standard Error t Statistic 5.4841 Intercept 0.13081 41.92 Sale price -0.0733 0.0234 -3.13 Sub-Zero brand 1.1196 0.1462 7.66 0.06956 0.005351 13.00 Total capacity (in cubic feet) Two-door, freezer on bottom Two-door, side freezer 0.04657 0.08085 0.58 Two-door, freezer on top 0.03596 -9.55 Base -0.3432 -0.7096 -0.8820 One door with freezer 0.1310 -5.42 -5.92 One door, no freezer 0.1491 (a) Write the regression model, being careful to exclude the base indicator variable. (Negative amounts should be indicated by a minus…
Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shells
4. The following regression is fitted using variables identified that could be related to tuition charges ($) of a university. TUITION = a+ B ACCEPT + y MSAT + 1 VSAT Where ACCEPT = the percentage of applicants that was accepted by the university, MSAT = Median Math SAT score for the freshman class and VSAT = Median English SAT score for the freshman class. The data was processed using MNITAB and the following is an extract of the output obtained: Predictor Coef StDev Constant -26780 6115 ACCEPT 116.00 37.17 MSAT -4.21 14.12 VSAT 70.85 15.77 т P -4.38 0.000 0.003 -0.30 4.49 0.767 ** S = 2685 R-Sq 69.6% R-Sq (adj) = 67.7% Analysis of Variance Source DF SS MS Regression 3 Residual Error 49 Total 52 808139371 353193051 1161332421 269379790 7208021 F 37.37 Р 0.000 a) Write out the regression equation. b) State the dependent and independent variable(s) c) Fill in the blanks identified by ** and ****. d) Is significant, at the 10% level of significance? [1] [2] [6] [4] e) State one…
What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?
You are the owner of a restaurant located in a beach resort in Hawaii and want to use regression analysis to estimate the demand for your fresh seafood dinners. You have collected data on the daily quantity of seafood dinners sold over the last summer season. In order to correctly specify your regression equation, which of the following variables should be considered? Select one: A. the prices charged for souvenirs in local stores B. the prices charged for scuba diving excursions at the resort C. the wages paid to your chef and servers D. the daily number of vacationers at the resort
A manufacturer is developing a facility plan to provide production capacity for its factory. The amount of capacity required in the future depends on the number of products demanded by its customers. The data below reflect past sales of its products: Year Annual Sales (number of products) Year Annual Sales (number of products) 1 490 5 461 2 487 6 475 3 492 7 472 4 478 8 458 Use simple linear regression to forecast annual demand for the products for each of the next three (3) years, by using the tabular method to: derive the values for the intercept and slope derive the linear equation plot the linear regression line develop a forecast for the firm’s annual sales for each of the next three years
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(2)What would the consequence be for a regression model if theerrors were not homoscedastic?