2.6 Competing Function Model Validation

Thinking of food in general, when it cools down in a room (since it was removed from the oven), how does this tend to happen?

• linearly - the temperature of the food continues to drop toward freezing 

• parabolically - the temperature of the food reached a maximum temperature in the oven and after being removed the temperature of the food continues to drop toward freezing 

• exponentially - after food cooks at peak temperature in the oven, the food settles down to the temperature of the room it is in 

What could the horizontal asymptote of the exponential model represent in this context? 

• The room's temperature

• The oven's temperature

A point on the x-axis of a residual plot means ____.

*More than one answer may apply.

• The point was on the regression curve.

• There was no vertical distance between the actual and predicted.

• The point above or below the regression curve.

When the ERROR calculation is positive, the predicted value is an overestimate. 

• TRUE

• FALSE

NOTE: The model is the line in the middle.

If point P was above the line, what would that point indicate in context?

• The model overestimates the actual braking distance for a car of a certain weight

• The model underestimates the actual braking distance for a car of a certain weight

• The model shows no error in the actual braking distance for a car of a certain weight

If you can detect a clear pattern or trend in your residuals, then your model is a good fit for the data.

• TRUE

• FALSE