Which of the following statements about linear regression and flexibility is true?

The assertion that linear regression is a low flexibility approach is accurate. Linear regression creates a model based on a linear relationship between the independent and dependent variables. This means it does not easily accommodate complexities or non-linear patterns in data, often resulting in lower flexibility compared to more sophisticated methods.

Regarding lasso regression, while it introduces regularization to prevent overfitting and can adjust the complexity of the model based on penalty terms, it is typically considered more flexible than standard linear regression. This flexibility allows for the selection of features and the handling of multicollinearity among predictors.

Bagging, or bootstrap aggregating, is indeed considered a highly flexible method. By combining multiple samples and averaging their outputs, bagging can capture a greater variety of patterns in the data, making it more adaptable to complex datasets.

Thus, understanding the levels of flexibility associated with each method allows for a clearer interpretation of the modeling approaches available and their suitability for various statistical problems.