Title: Making Our Models Stronger
I know that real-world data can be tricky, so I did something called sensitivity analyses to make sure our regression models are tough and reliable. Sensitivity analyses mean we carefully played around with different factors, looked at how unusual data points affect our results, and thought about other things that could mess with our predictions. The goal was to find any weak points in our models and make them better so that they work well in different situations.
When I did sensitivity analyses I learned a lot about how steady our regression models are when the data changes. I paid close attention to how strange data points and other factors could mess with our models. This careful look helps us make sure that what we conclude is not only true for specific situations but is solid overall. It’s like checking our work to be sure we can trust what I found in my research.