# Should I use confidence or prediction interval to calculate cost uncertainty (Simple Linear Regression)? [duplicate]

(I'm going to give an analogy, since I can't use the real example as it's work related!)

I need to estimate how much it may cost to purchase a 200kg refrigerator, based on previous experience.

I have a linear graph (R^2 > 0.9) detailing over 100 purchases of refrigerators, detailing its weight (x-axis) and cost (y-axis). Let's say the refrigerators in the dataset vary from 10kg to 1000kg (i.e. no extrapolation required).

Assuming I wanted to know the y-value (cost) at X = 200 (weight), how could I calculate the uncertainty? Would this be the smaller confidence interval, or the larger prediction interval?

I've always struggled with this question and would greatly appreciate a distinction of when to use one rather than the other.

Thank you

• Kind of, but I'm still not sure why you wouldn't want the average cost of 200kg refrigerators (confidence interval) rather than the prediction interval? Jul 8, 2020 at 17:55