Prais–Winsten estimation

Imagine you're playing with building blocks, and you want to know how tall your tower is. But you keep accidentally knocking it over as you add more blocks.

That's sort of like what happens when scientists try to measure things that change over time-- like the height of a building, or the number of people who use a certain app. They might collect data at different points in time, but there might be other factors that cause the data to bounce around-- like a really snowy winter or a big news event.

So scientists use something called Prais-Winsten Estimation to help them figure out what the data would look like if there weren't all these noisy factors. It's like putting a 'stabilizer' on your tower, so you can see how tall it really is.

Prais-Winsten Estimation uses math to look at the patterns in the data, and figure out what factors might be causing the fluctuations. Then it can 'smooth out' the data, so that it's easier to see the real trends.

Basically, Prais-Winsten Estimation helps scientists better understand changes over time, by removing noise and providing more accurate estimates.