Proving Equivalence of Arithmetic Mean and Least Square Criterion

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Homework Statement



Show that the calculation of an arithmetic mean for a given set of values y1,y2...yn is equivalent to fitting the line y=y_av to the data under the least square criterion

Homework Equations



distance between the data points and the best fit curve:
d(y_n,f(x))= \Sigma_{i=1}^{n}(y_i-f(x_i,a_1,a_2,...,a_n))^2

arithmetic mean of a set of values y_n:
y_av= \frac{1}{n} \Sigma_{i=1}^{n}y_i

The Attempt at a Solution



I need help getting started please!
 
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In your distance formula, just put f=y_av. Now how would you minimize d with respect to y_av?
 
set the partial derivative of d equal to zero?
 
Last edited:
kreil said:
set the partial derivative equal to zero?

Absolutely. What do you get?
 
\frac{d(R^2)}{dy_{av}}= -2 \Sigma_{i=1}^n(y_i-y_{av})=0
 
What's your next move?
 
i was thinking maybe distribute the sum, but i don't know how the -2 helps, since I am trying to get the formula in the form of the arithmetic mean
 
-2*x=0 means x=0, doesn't it?
 
how is this:

\frac{d(R^2)}{dy_{av}}= -2 \Sigma_{i=1}^n(y_i-y_{av})=0 \implies \Sigma_{i=1}^n(y_i-y_{av})=0 \implies \Sigma_{i=1}^ny_i=\Sigma_{i=1}^ny_{av}=y_{av}
 
  • #10
The sum from 1 to n of y_av is NOT = y_av.
 
  • #11
oh ok that sum is n y_av, so the sum of 1/ny_i is y_av correct?
 
  • #12
That's what you are trying to prove, right?
 
  • #13
right! thanks a lot man
 
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