1. The problem statement, all variables and given/known data "You are asked to do an experiment where you will need to use a rotating blade to measure the wind speed. You measure the number of rotations of the blade at 10 different wind speeds, 10 times each and will make a linear fit to determine the wind speed as a function of rotations." Justification Question: Suppose the uncertainty of the independent variable is the same for each. Give an upper estimate of the value of that uncertainty that justifies neglecting this uncertainty when doing the linear fit. 2. Relevant equations -Uncertainty we assign is + or - half of the least count. -Propagation of Errors? L = L0 + ΔL -Mean of sample distribution? 3. The attempt at a solution This is a uncertainty and analysis type problem for my intermediate physics lab course. So I am given a set of data points, which I didn't post because I am more interested in arriving on how to do the calculations. I just want to make it clear that I'm not seeking to get the work done, but would rather learn.. So a set of example data points are however: 10 mph: 12, 13, 14, 17, 14, 14, 15, 13, 14, 14 rotations 15 mph: 18, 19, 20, 18, 18, 18, 17, 19, 21, 19 rotations Therefore would the uncertainty be just 0.5 rotations per each data point? But then is it really possible to have an error of rotations? So I just apply the fractional uncertainty equation, which is (Uncertainity)/(Value), in which for the value I just take the average for each set, since this is not a combination type uncertainty measurement. I get a value for each mph data points, example, i get 3.5% for 10mph, and 2.6% for 15mph. Is this approach correct? However, I do not understand why this uncertainty would be neglected in linear regression. Is it because in linear regression, it takes the sample distribution? Thank you very much!