Question

Which of the following statements is true about prediction?

a. Higher R squared is always associated with good prediction
b. If our goal is to interpret coefficients, we will likely use the model that yields the smallest errors out-of-sample
c. Sometimes we choose a model based on best prediction accuracy and not on R squared
d. If our goal is predicting with accuracy, we will likely use R squared to select a model

Answer 1

Sometimes we choose a model based on best prediction accuracy and not on R-squared. When the goal is predicting with accuracy, it is important to evaluate models based on their prediction accuracy, rather than solely relying on R-squared.

Step-by-step explanation:

The correct statement about prediction is option c. Sometimes we choose a model based on best prediction accuracy and not on R-squared.

R-squared is a measure of how well the linear regression model fits the data and explains the variations in the dependent variable. Higher R-squared values indicate a better fit, but they do not necessarily guarantee good prediction accuracy. It is possible to have a high R-squared value but still have poor prediction accuracy if the model is overfitting the data.

When the goal is predicting with accuracy, it is important to evaluate models based on their prediction accuracy, rather than solely relying on R-squared. Other measures, such as root mean square error (RMSE) or mean absolute error (MAE), can provide a better indicator of prediction accuracy.