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.

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.

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

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
A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (X1), the living area of the house in square feet (X2), and the number of bedrooms (X3). The following regression model was chosen using a data set of house statistics: y=88,399554791.3333x231,471.1372x3 The first house from the data set had the following values: Selling price $324,000 Age - 22 years Square Feet 2.000 Bedrooms 3 The residual for this house is 23,558 -41,480 10,216 -16,095 27
RQ7. A teacher is trying to predict student test grades (Q). She believes test grades are a function of incoming GPA, hours studying, and hours spent on social media (a distraction). She runs a regression and it produces these coefficients: Variable Coefficient Intercept GPA Hours Studying Social Media 70.0 3.5 2.4 -4.0 For a given student Julian, his GPA is 2.0, he studies 4 hours for the exam, and he spends 6 hours on Facebook. Predict his exam score (round to the nearest whole number).
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.
Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.
46) The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the trade-off between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B₁totwrk + ß₂educ + ß3age +u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (i) If adults trade off sleep for work, what is the sign of f₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75, the estimated equation is sleep = 6,241.15 + 0.211totwrk + 9.22educ + 1.67age n = 211, R² = 0.981 If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?
The Weibracht Corporation designs and manufactures custom beer steins for some of the numerous brew pubs in western North Carolina. Each stein costs Weibracht $8 to produce. The brew pubs purchase the steins from Weibracht for resale to their customers and require a 51% margin. Weibracht’s marketing director has conducted a regression analysis using historical data, resulting in the following regression line: q = -12 * p + 326, where q = slope * retail price + MWB What is the profit maximizing price that Weibracht should charge the brew pubs for these custom steins?
A. B. Consider data on births to women in the United States. Two variables of interest are the dependent variable, infant birth weight in ounces (bwght), and an explanatory variable, average number of cigarettes the mother smoked per day during pregnancy (cigs). The following simple regression was estimated using data on n = 1,388 births: bwght = 119.772 (0.572) n = 1,388, 0.514 cigs (0.091) R² = 0.0227, where standard errors are shown in parenthesis. What percent of the variation in birth weight is explained by cigs? What is the predicted birth weight when cigs = 0? What about when cigs = 20 (one pack per day)? Comment on the difference.
In regression analysis, a common metric used in assessing the quality of the model being used to fit the data is known as the R-squared coefficient. Explain the R-squared coefficient. What is the difference between the R-squared and adjusted R-squared coefficients?
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
18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?
Consider the following sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted r2. Linear Quadratic Cubic Intercept 9.52 9.86 9.91 2.62 2.71 1.80 x2 NA -0.31 -0.33 x3 NA NA 0.26 R2 0.800 0.824 0.883 Adjusted R2 0.797 0.821 0.882 a. Predict y for x = 2 and 5 with each of the estimated models. (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places.) Linear ŷ Quadratic ŷ Cubic ý x = 2 x = 5