Recent content by paqqj

Those are not the answers I would get. Using those 10 data points in your example, the best fit relationship between X and Y is: Y=0.116667 X + 54.25 So when X=45, then Y=59.5 Part two is where I am unsure. If I simply rearrange Y=0.116667 X + 54.25 to solve for X: X=8.571404 Y - 465...

Thank you for your help. I admit am having a difficult time understanding the concept. To me, if the speed I need to keep my eye on (when the wind most likely is 50KT at the unknown station) is when the real wind is 76KT at the known one, then in a real world situation one would think that if...

I did filter out some obviously bad data...there were some archived data that had zero velocity instead of "missing". I also considered throwing out all the data from either station that was less than some fraction of the other, or greater than some multiplier of the other but I wasn't...

Your second point... It is a monitoring problem.

Structural integrity of a building requires certain action when winds exceed 50KT, but due to its remote location and lack of on site anemometer I'd like to estimate the speed based on a known nearby station. Correlation between the two is 0.75, so not great, but somewhat.

I have archived data of several years of hourly wind speed from 2 nearby stations. The first station no longer reports anything, but the second one does. I want to know what the best guess of the wind speed at the first station is, based on the latest reading from the second one. X = known...

Thanks I get what you are both saying. I did notice that when the correlation was 1.0 then X and Y could be swapped and the new best fit slope became 1/m. Is there a way to get a slope such that the inverse of Y=mX +b is X = Y/m - b/m when X and Y are swapped? Here are the specifics of...

inverse best fit slope...expected vs calculated Given a set of data (X,Y) pairs, we can get the best fit line using many techniques. Assume the correlation is not perfect. We can use the least squares method to get the best fit line slope and intercept. So we have Y = mX + b In other...