1/4 teaspoon ground thyme. 1 teaspoon salt. - Combine butter, garlic, onion and celery in a saucepan. - Braise over medium-low heat until tender but not brown. - Add the flour, and mix to make a roux. - Drain clams, reserving juice. - In a separate saucepan, combine clam juice, half-and-half, thyme and salt.
Loosely speaking, a residual is the error in a result. To be precise, suppose we want to find x such that =. Given an approximation x 0 of x, the residual is â whereas the error is â If the exact value of x is not known, the residual can be computed, whereas the error cannot. Residual of the approximation of a function Similar terminology is used dealing with differential, integral and functional equations. For the approximation of the solution of the equation
Dividing the sum of squares of the residual (851.469) by its degrees of freedom (72) yields 11.826. That is the mean sum of squares. If you further take a square root, you'll get Root MSE (3.4289 in the output).Basically, it's a measurement of accuracy. The more accurate model would have less error, leading to a smaller error sum of squares, then MS, then Root MSE. Square them. 2  Add them up, this will give you the Error sum of squares, SS in Stata output. 3  Divide it by the error's degrees of freedom, this will give you the Mean error sum of squares, MS in Stata output. 4  Take a square root of it, and this is the Root MSE.