Estimating Standard Deviation by Using Curve Fitting Toolbox

I am using Curve Fitting Toolbox to fit a 10 points data. Here's the result.

Linear model Poly1:

       f(x) = p1*x + p2
Coefficients (with 95% confidence bounds):
       p1 =  1.247e-013  (1.193e-013, 1.3e-013)
       p2 = -1.544e-015  (-4.863e-015, 1.775e-015)
Goodness of fit:
  SSE: 3.551e-029
  R-square: 0.9972
  Adjusted R-square: 0.9969
  RMSE: 2.107e-015

I am neither using robust fitting nor with my own weighting factor. Therefore I suppose the SSE is not weighted and I can get the standard deviation by simply dividing the SSE by 10 (points) and take the square root of it. But is this correct?

May I also know in Curve Fitting Toolbox, the 95% confidence bound is equivalent to how many standard deviation? About 2 standard deviation by using the results obtained through Curve Fitting Toolbox?

If I did a robust linear fit, since the SSE is weighted, is there any way for me to get a good estimate of the standard deviation?

Here's the result for the robust fit:

Linear model Poly1:

       f(x) = p1*x + p2
Coefficients (with 95% confidence bounds):
       p1 =  1.239e-013  (1.182e-013, 1.297e-013)
       p2 = -1.258e-015  (-4.831e-015, 2.315e-015)
Goodness of fit:
  SSE: 4.116e-029
  R-square: 0.9968
  Adjusted R-square: 0.9964
  RMSE: 2.268e-015

Thanks.

[laurent jalabert]

https://fr.mathworks.com/matlabcentral/fileexchange/34765-polyfitn