'Undoing' a square root transformation for linear regression I have a time series that I'd like to perform regression on. It's not linear, or homoscedastic.
After applying a square root transformation to the dependent variable, which made the graph linear.
I applied the sklearn LinearRegression model, and checked the distribution of the residuals, which are normally distributed. Additionally, I checked for homoscedascity, and found that to be the case.
My question is, is it valid for me to just square the values of the regression line, and plot it against the graph of my original values to undo the transformation? Can I do the same with standard error lines?
(Edit: typos)

From comment:
If your estimate for the square root is say $\widehat{\sqrt{y_i}}=20$ then you can square this to give an estimate for the value of $\hat y_i=400$,
but you need to realise that this transformation makes this estimate biased and the uncertainty asymmetric (so a range for the square root of $10−30$ when squared becomes $100−900$, and $400$ is not in the middle of that)
