Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fieldsthat have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery.Percent Recovery (%)20 40 80100Jobs Created (Millions)661218Find the regression line.j(r) =Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 50%.million jobs

Solve using linear regression equation ŷ = a + bxwhereb = N∑X2−(∑X)2N∑XY−∑X∑Ya = N∑Y−b(∑X) In…

Answered: Oil Recovery A U.S. state's Bureau of…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?
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Suppose you have run four regression models: A, B, C, and D. You are going to make a decision on which one to use just based on the adjusted r² value. Here are the adjusted r² values for each model: A: 0.71 B: 0.57 C: 0.65 D: 0.76 Which regression model would you choose based on the adjusted r²? OD since it has the highest adjusted r² value B since it has the lowest adjusted r² OC since it has an adjusted r² between the adjusted r² of regressions B and D. Either B or C since they have the lowest adjusted r²
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
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
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You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)
Regression analysis and the correlation coefficient can both be used to analyze the relationship between two economic variables. What are the advantages of using regression analysis instead of looking at the correlation coefficient?
4. Suppose you run the following regression log(Earnings) = Bo+B₁Education + B₂ Experience + ß3 Female + U with EU Education, Experience, Female] = 0. How do you interpret ₁ here?
Each cell represents a regression with a different outcome variable from Krueger’s (1993) study of workers who use computers. The left hand side variable is log wages. Answer based on column 2 of the above regression table. 1. Write a sentence that explains the relationship the regression is describing in row 2. 2. What is the number in parenthesis? What is it trying to communicate? 3. Write a causal statement that captures what the authors are trying to argue in row 2.
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.
CHOICES OF ANSWERS: A. 44.9032 B. 45.8520 C. -44.5327 D. 43.2112

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