NO.PZ2020010801000027
问题如下：
Define homoscedasticity and heteroskedasticity. When might you expect data to be homoscedastic?
解释：
Homoscedasticity is a property of the model errors where they have the same variance. Heteroskedasticity is a property of the errors where their variance changes systematically with the explanatory variables in the model. Experimental data are highly likely to be homoscedastic. In general, the more homogeneous the data, the more likely the errors will be homoscedastic. In finance, we often use data with substantially different scales, for example, corporate earnings or leverage ratios. This heterogeneity is frequently accompanied by heteroskedas-ticity in model errors.
老师，红色部分不太理解，scale也就是取值范围，是指X和Y没有产生于同一个经济过程？能否举个更直观的例子？
公司利润和杠杆率是想说不相关？
