Question

23:01 4G Midterm Econometrics II Semester 1 2.. 1 You are given data on price and quality of cocaine. You believe that the quality of cocaine affects the price, so you ran the following regression: The STATA output storage display valuc name type foemat l label variable label flat %9.0g float %9.0g float %9.0g float %9.0g peice per gram in dollars foe a cocaine number of grams of cocaine in a give quality of the cocaine expressed as a time variable with 1984-p to 199 price qu sale qual trend reg price qual Source SS df MS Number of obs 56 F(1, 54) 12.03 Model 7774.198257774.19825 Prob > F 0.0010 Residual | 34889.9129 54 646.109497 R-squared 0.1822 Adj R-squared 0.1671 Total | 42664 = 25.419 55 775.71 Root MSE price! Interval] Coef. Std. Err. PPM [95% Conf. t qual -6292784 1814129 3.47 0.001 -9929893 -2655675 cons| 108.1354 10.02789 10.78 0.000 88,03068 128.2401 Dashboard Calenda To do nbox

Answer 1

a) Equation of regression line :

y = b₀ + b₁*x

Here y is price and x is quality of cocaine , b₀ is constant and b₁ is coefficient of quality or slope .

Therefore regression line equation is :

y = 108.1354 - 0.6293*x

b) Interpretation of slope or coefficient of quality :

For every 1% purity increases in quality of cocaine , we expect to decrease the price of cocaine by $0.6293

c) H₀ : β₁ = 0 vs  H₁ : β₁  ≠ 0

We are given α = 0.05 and we have p -value = 0.001 ( P > |t| for quality on the table)

Decision : As p-value is less than level of significance α = 0.05, we reject H₀ .

Conclusion : There is significant evidence that slope β₁ is different that 0, it means the price is linearly associated with the quality of cocaine.