Independence (Linear Regression) 편집하기 (부분)

==Handling Violations of Independence==
When independence is violated, several techniques can be applied to address the issue:
*'''Time Series Modeling''': For time-dependent data, using models designed for time series analysis, such as ARIMA or exponential smoothing, can capture the dependencies.
*'''Random Effects Models''': In clustered data, using random effects or mixed models can account for the dependency within groups.
*'''Generalized Least Squares (GLS)''': Adjusting for correlated errors using GLS can improve model performance with non-independent observations.
*'''Sampling Methods''': In some cases, resampling techniques like bootstrapping can be used to create independent samples from the original data.
By ensuring or accounting for independence, Linear Regression models can provide more reliable estimates, conclusions, and predictions.