How can the effect of high leverage points be reduced in a regression model?By increasing the sample size.By transforming the dependent variable.By using robust regression techniques.By adding interaction terms

Reducing the effect of high leverage points in a regression model can be achieved through several methods:

1. Increasing the Sample Size: By increasing the sample size, the influence of high leverage points can be reduced. This is because as the sample size increases, the impact of any single point on the overall regression model decreases.

2. Transforming the Dependent Variable: Sometimes, a transformation of the dependent variable can help reduce the effect of high leverage points. For example, taking the logarithm or square root of the dependent variable can often help in reducing the impact of these points.

3. Using Robust Regression Techniques: Robust regression techniques are designed to be not overly affected by small departures from model assumptions. These techniques give less weight to high leverage points, thereby reducing their influence on the model.

4. Adding Interaction Terms: Adding interaction terms to the model can also help in reducing the effect of high leverage points. Interaction terms allow for the possibility that the effect of one predictor variable on the dependent variable depends on the value of another predictor variable. This can help in capturing more complex relationships in the data, which can in turn reduce the influence of high leverage points.