What is a potential consequence of using linear regression on data with outliers
Practice Questions
Q1
What is a potential consequence of using linear regression on data with outliers?
Increased accuracy of predictions
Decreased interpretability of the model
Bias in the estimated coefficients
Improved model performance
Questions & Step-by-Step Solutions
What is a potential consequence of using linear regression on data with outliers?
Step 1: Understand what linear regression is. It is a method used to predict a value based on the relationship between variables.
Step 2: Identify what outliers are. Outliers are data points that are significantly different from other data points in the dataset.
Step 3: Recognize that outliers can affect the results of linear regression. They can pull the regression line towards themselves.
Step 4: Realize that this pulling effect can change the estimated coefficients, which are the values that represent the relationship between variables.
Step 5: Understand that biased coefficients can lead to inaccurate predictions, meaning the model may not perform well on new data.
