(6.8 x10^2) x (1.3 x10^-3)
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Answer:
Step-by-step explanation:
Hello!
You have two study variables:
Dependent variable
Y: Profit of the firm in the U.S.
Independent variable
X: Market capitalization of the firm
a.
See the scatterplot in the attachment.
Looking at the graphic it seems that there is a positive regression between the profit and the market capitalization, meaning that every time the market capitalization of the firm increases, the profit of this firm also increases. But not all the "firms" adjust linearly, so I wouldn't expect the regression to be powerful.
b.
The regression model is E(Yi)= α + βXi
Where the parameters of the model are α and β, these two parameters are to be estimated to estimate the regression equation. The estimator for α is a and the estimator for β is b:
a= Y[bar] - bX[bar]
b= [∑XY-(∑X*∑Y)/n]/[∑X²-(∑X)²/n]
I'll use a statistic software to calculate the estimated regression equation:
^Yi= a + bXi
^Yi= 325.89 + 0.07Xi
c.
You need to predict the value of Y (Profit) given a value of X= 70721.3 (million). To make this prediction you have to use the estimated equation:
^Yi= 325.89 + 0.07Xi
^Yi= 325.89 + 0.07(70721.3)= 5276.381
The predicted profit for a market capitalization value of 70,721.3 is 5276.381 million.
I hope this helps!
