An economist uses regression analysis to determine the relationship between used car price (y) and the age of a car (x). The analysis resulted in the following equation: Ý – 30,000 - 500"X The above equation implies that an increase of O 1 year in the age of the car is associated with an increase of $500 in the price of the car 1 year in the age of the car is associated with a decrease in $500 in the price of the car O$500 in the price of the car is associated with an increase of 5 years in the age of the car 5 years in the age of the car is associated with a decrease of $100 in the price of the car

An economist uses regression analysis to determine the relationship between used car price (y) and the age of a car (x). The analysis resulted in the following equation: Ý – 30,000 - 500"X The above equation implies that an increase of O 1 year in the age of the car is associated with an increase of $500 in the price of the car 1 year in the age of the car is associated with a decrease in $500 in the price of the car O$500 in the price of the car is associated with an increase of 5 years in the age of the car 5 years in the age of the car is associated with a decrease of $100 in the price of the car

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Please no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…
Discuss the FIVE (5) importance of adding error term in the regression model.
"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity
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
To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.
Please no written by hand solution a) Suppose in a regression of weekly salaries on years of schooling for males(m) and females(f), the following results are obtained. Wm = 50Sm and Wf = 40Sf. where Wm (Wf) denotes weekly salary and Sm (Sf) denotes years of schooling for males and females respectively. 50 and 40 are the coefficients on schooling in the male and female regression respectively. On average, men have 12 years of schooling and women have 10 years of schooling. What is the average male-female wage differential? Is this a good estimate of discrimination? Explain why/why not. Using the information in the question, what would you propose as a better estimate of discrimination? State any assumptions that you use and explain your answer.
18 Calculate the least square regression líne equation with the given X and Y values. Consider the values: X Y 60 3.1 61 3.6 62 3.8 63 4 65 4.1 To Find, Least Square Regression Line EquationY = a+ b X
Define coefficients of the Linear Regression Model?
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QUESTION 2 Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u]x] #0 and that z is a valid instrument for r. Knowing that Cov(y, z) = 0.5 and Cov(z, x) = 0.5, the IV estimate of 3 is 1. %3D O True O False
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Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?