When trying to fit curvature in the data, you can also fit a quadratic or cubic model, which adds quadratic or cubic terms to the model. Instead, consider transforming the X or Y variable because you don't have to include additional terms in the model. Adding terms to the model uses additional degrees of freedom, which reduces the degrees available for explaining the variation in the response.
Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the mean response. Similarly, the prediction interval indicates that you can be 95% confident that the interval contains the value of a single new observation. For a given set of data, a lower confidence level produces a narrower interval, and a higher confidence level produces a wider interval.
In Title, you can enter a custom title for the fitted line plot.