Please see attached.
第一题知识点比较多，答案都是知识点提炼而成的。
Heteroskedasticity refers to a situation where the variance of the residuals is unequal over a range of measured values.这句话够了，可以再补一句解释：
heteroskedasticity happens when the standard deviations of a predicted variable, monitored over different values of an independent variable or as related to prior time periods, are non-constant over time.
Consequence:
Pure heteroskedasticity does not cause bias in the regression coefficient estimates.
Heteroskedasticity causes OLS to no longer be a minimum variance estimator.
Heteroskedasticity causes the estimated variances of the regression coefficients to be
biased, leading to unreliable hypothesis testing. The t-statistics will actually appear
to be more significant than they really are.
How to deal with it:
Weighted least squares:
This type of regression assigns a weight to each data point based on the variance of its fitted value.
Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals. When the proper weights are used, this can eliminate the problem of heteroscedasticity.
Another way to fix heteroscedasticity is to transform the dependent variable in some way. One common transformation is to simply take the log of the dependent variable.
For example, if we are using population size (independent variable) to predict the number of flower shops in a city (dependent variable), we may instead try to use population size to predict the log of the number of flower shops in a city.
