用python进行多元线性回归，对数据进行中心化和标准化

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于 2021-10-05 11:51:00 发布

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本文链接：https://blog.youkuaiyun.com/YTHAAA/article/details/120612407

多元线性回归用于处理与多个因素相关的现象，比单变量线性回归更实用。通过最小二乘法估计模型参数，并进行统计检验。数据的中心化是减去平均值，而标准化则是减去平均值后再除以标准差，使数据具有0均值和1标准差，用于统一不同变量的数据尺度。

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In regression analysis, if there are two or more independent variables, it is called multiple regression. In fact, a phenomenon is often associated with multiple factors. It is more effective and practical to predict or estimate the dependent variable by the optimal combination of multiple independent variables than to predict or estimate only one independent variable. Therefore, multivariate linear regression is more practical than univariate linear regression.

Multivariate linear regression is similar to univariate linear regression. The least square method can be used to estimate the model parameters, and the model and model parameters need to be statistically tested.

Data centralization is different from standardization. Centralization is to subtract the average from the original data, while standardization is to subtract the average from the original data and then divide it by the standard deviation. The data obtained is the data with 0 as the average and 1 as the standard deviation. The purpose of data centralization is to unify the scale of data of different variables.
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