Answer:
Estimator 'b' from sample regression function 'y = b0 + b1x + u' satisfies OLS, if Σu^2 = Σ (Y - y)^2 is minimum
Step-by-step explanation:
Regression shows the relationship between independent (causal) & dependent (effected) variables.
As per given case : Independent variable = men's are = x ; dependent variable = number of push ups = y
- Population regression function [ PRF ] : Y | Xi = B0 + B1Xi
- Sample (estimated) regression function [SRF] : y = b0 + b1x + u
OLS [Ordinary Least Square] model is a method used for finding linear regression parametes (b's), such that it minimises the sum of squared residuals. Residuals are the error terms between PRF actual value & SRF estimated value
So, b's from SRF are OLS estimators if, Σu^2 = Σ (Y - y)^2 is minimum
