Meaning: Mathematical optimization technology. It seeks the best function matching of data by minimizing the sum of squares of errors. Unknown data can be easily obtained by least square method, and the sum of squares of errors between these obtained data and actual data is the smallest. Mathematical tools are widely used in error estimation, uncertainty, system identification and prediction, forecasting and other data processing fields.

The least square method is the most commonly used method to solve the curve fitting problem. Other optimization problems can also be expressed by least square method by minimizing energy or maximizing entropy. According to the sample data, the estimator of parameters of simple linear regression model can be obtained by using the least square estimation formula.

Three characteristics

1, linear characteristic

The so-called linear feature means that the estimator is a linear function of the sample observation value, that is, the linear combination of the estimator and the observation value.

2. Unbiasedness

Unbiasedness means that the expected values of parameter estimators are equal to the overall real parameters respectively.

3. Minimum variance

The minimum variance means that the variance of the estimator is the smallest, that is, the best, compared with the estimators obtained by other methods. The minimum variance is also called validity. This property is the famous Gauss-Markov theorem. This theorem clarifies that the ordinary least squares estimator is the best compared with any linear unbiased estimator obtained by other methods.

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