習(xí)題：
　　Exercise:
　　Ordinary least squares refers to the process that:
　　A.     Maximizes the number of independent variables.
　　B.     Minimizes the number of independent variables.
　　C.    Produces sample regression coefficients.
　　D.    Minimizes the sum of the squared error terms.
　　解析：
　　Answer: D
　　Explanation: OLS is a process that minimizes the sum of squared residuals to produce estimates of the population parameters known as sample regression coefficients.
　　知識(shí)點(diǎn)：
　　Ordinary Least Squares (OLS) Estimator
　　The OLS estimator chooses the regression coefficients so that the estimated regression line is as close as possible to the observed data, where closeness is measured by the sum of the squared mistakes made in predicting Y given X.
　　Let b0 and b1 be some estimators of β0 and β1, the sum of these squared prediction mistakes over all n observation is:
 

 
　　The estimators of the intercept and slope that minimize the sum of squared mistakes are called the ordinary least squares estimators of β0 and β1.
　　The OLS estimators of the β0 and β1 are:
 

 
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