A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (y) - measured in dollars per month - for services rendered to local companies.
One independent variable used to predict service charge to a company is the company's average annual sales revenue (x) - measured in $million, ranging between 4 to 15 $million.
Data for 21 companies who use the bank's services were used to construct the following model.
E(y) = β0 + β1x
The results of the simple linear regression analysis returned the equation:
y = 2,700 + 20x
Interpret the estimate of β0, the y-intercept of the line.
Select one:
a. About 95% of the observed service charges fall within $2,700 of the least squares line
b. We expect that companies will be charged at least $2,700 by the bank
c. For every $1 million increase in sales revenue, we expect the service charge to increase $20
d. For every $1 million increase in sales revenue, we expect a service charge to increase by $2,700
e. No practical interpretation since a sales revenue of $0 is a nonsensical value