I think this is key for me if I don't want to wake up neighbors before sunrise and don't want to wait till fall or winter.

BTW, no particular reason why column A in spreadsheet could not be in 0.5, 0.25 or 0.1 second intervals. But would that compensate for altitude excursions? How does the displayed R-squared relate to that? (I don't even know how that is calculated. Just know it's a measure of how well the trendline fits the data curve.) I suspect not, unless altitude (variation) is incorporated into the equation.

Finn

R^2 is the correlation coefficent. R^2 =1 is a perfect fit between your data and equation at the points where your data was used. R^2 less than 0.95 in the hard sciences (airplane performance is) maybe acceptable. In the social sciences, 0.50 is frequently acceptable. Look here:

R-squared is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable.

www.investopedia.com

For an equation to have meaning you must have enough points for the function you are picking and the shape of the data. Enough is kind of subjective. I can think of a bunch of ways this can be messed up.

Classic is two points, and an equation of this form y = c1 + c2*x fits it perfectly. This is OK if the function is a straight line - an example is distance at a constant speed. If instead the data is travel with constant acceleration, y = c1 + c2*x +c3*x^2 is known to describe it perfectly.

Another classic is N number of data points and an N (or greater) order polynomial. It will fit the points perfectly, but between points it can be all over the place and thus does not describe what is going on very well either. Imagine 4 data points with a 4th order equation - perfect fit that does not reflect reality. So pick a function that has a shape that fits with the data.

The topic of multiple runs at one starting point has the advantage of averaging. Best way to do this is to combine data. If the time bases do match (t=0 is not the same starting speed for each run), they can be corrected by adding an offset to each time column to make the starting speeds line up, then proceed with curve fit for entire set. Use enough runs and this technique tends to average out the oscillations and other noises in the data. R^2 may not be better, but the equation will usually fit the real world better, which is the goal.

All the comments on how many decimal points to show is sort of missing the point. In my world, I need something like 3 or more significant digits for me to trust the outcome. For number less than unity, the zeros between the decimal point and the first non-zero digit are meaningless. If you are running fourth or higher order polynomials, the coefficient for the fourth order term can be pretty darned small and still have meaning. If you get less than three significant digits, please expand the equation display or change it to scientific notation with enough digits.

Billski