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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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.
Economics: Industrial Economics Since only allowed 3 questions = Please Answer Parts A. - C. (and include answers to the NOTE PART: sub-questions a.; b.; c. In order to solve the Parts A. - C. questions). Thanks QUESTION: You work for the manager of a small single-product firm. Assume that you have data for your firm on costs, capital rental rate, wages and output. You realize through regression analysis that the data fits very well the following equation: ln(C) = -1.18 + 0.33 ln(r) + 0.67 ln(w) + 0.81 ln(q) A. Determine the production function from which costs were derived. Explain whether it exhibits constant, increasing or decreasing returns to scale. B. From your answer in (a), determine whether your firm should explore the possibility of expanding, knowing that the market is perfectly competitive. The market consists of a couple of large firms producing around 1,000,000 units each, multiple medium-sized firms producing 100,000 units each and a huge amount of small firms…
7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755
You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this…
Suppose researchers use observational data to study the effect of class size and teacher experience on student score. Which of the following is a confounding variable? Student score 0000 Family income Class size Teacher experience

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