Whether to standardize the data depends on what method you want to use, how you want to interpret the model and your data structure. For example, when we use some models that are not invariant to scaling, like ridge regression and lasso, normally we would standardize the data to make the model reproducible.

On the other hand, in some special cases, the different scales of variables might be indicators of their real structural effects which are what we want to incorporate. In such cases, we might not standardize them.

And in most cases, the thing that really make a difference is the standardization on predictors, rather than the response. In normal linear regression, we would still have intercept (mean of y) if we don't centralize the response (y). In many literature, people prefer to centralize y as well, then they don't get the intercept.

是否有截距和标准化无关

不同的回归模型，对数据要求不一，OLS，ARMA，GARCH等模型是不要求的
主成分分析和聚类分析需要标准化

4# yanbridge 那主成分标准化提取成分后做的线性回归是不是有截距呢？

2# ltx5151 谢谢啦