Chapter 2 Linear Regression Models, OLS, Assumptions and ...

For observation i we obtain the residual, then square it and finally sum across all observations to obtain the sum of squared residuals: e i =y i −yˆ i (2.19) e2 i=(y −yˆ )2 n ∑ i=1 e 2 i= n ∑ i=1 (y −yˆ ) Again, the coefficients b 0 and b 1 are chosen to minimize the sum of squared residuals: min b 0,b 1 ∑n i=1(y i −yˆ i) 2 ... ................
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