Answer:
(A): A least-squares model predicts that the more baggage items that are unloaded from a flight, the greater the time required to deliver the items to the baggage claim area.
Step-by-step explanation:
When a least squares regression line is used to try and predict the behavior of a variable y based on observations (x1,y1), (x2,y2),...(xn,yn) of values of y when values of x1, x2,..., xn of another variable x changes, an equation of the form
y = mx + b
is established to help predict the value of y for a given value of x that might not be among the values x1, x2,..., xn used to derive the model.
If the linear model prove to be the most appropriate, then you can have either a <em>positive linear association or a negative linear association.
</em>
A positive linear association means that the slope of the line is positive (m>0), so the values of y will increase if the values of x do.
In this case, the variable x is how many baggage items were unloaded from each flight upon arrival, and the variable y is the time required to deliver all the baggage items on the flight to the baggage claim area.
As the linear association is positive, it means
(A): A least-squares model predicts that the more baggage items that are unloaded from a flight, the greater the time required to deliver the items to the baggage claim area.
