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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← Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price $2.50 $3.50 $1.90 $4.20 $3.20 $4.10 Number Sold 760 515 980 250 320 490 D Using simple linear regression and given that the price per cup is $1.75, the forecasted demand for mocha latte coffees will be 1862.8 cups (enter your response rounded to one decimal place). 88
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?
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