[JRMichler]

Your most accurate fit to the data is from a least squares fit of a quadratic equation to the raw data. Filtering the raw data makes look prettier while reducing accuracy. The lost accuracy comes from the phase characteristics of your filter.

Your road has enough slope to fuzz the results. I regularly drive a route of about 60 miles. One end is 800 feet higher than the other end, for an average slope of 13 feet per mile. Average mileage is about 5 MPG higher going downhill.

Your top speed was only 40 MPH, which is not fast enough for accurate results. A 5 MPH headwind increases aero drag by 27% at 40 MPH, and more at lower speeds. A 5 MPH head or tailwind will change my trip mileage by about 2 MPG.

Averaging several runs is good. Starting at a much higher speed is even better. The best starting speed is where the aero drag is at least ten times higher than the rolling resistance. Coasting down until almost stopped will also improve accuracy.

MTA: OK, I see that you already spotted some of this. You calculated that air drag is equal to RR at a speed higher than your highest test speed. That means that it is difficult to accurately estimate Cd from your data. Your Crr should be pretty good because the test data is almost all rolling resistance, with very little contribution from air drag.