### Final Answer
**True**

Polynomial regression indeed can model nonlinear relationships by transforming the features. In a standard linear regression model, the relationship between the independent variable(s) and the dependent variable is linear, which means it can be represented by a straight line. However, many real-world phenomena are nonlinear in nature.

To address this, polynomial regression extends linear regression by introducing polynomial terms of the independent variables. This transformation allows the model to capture the curvature and complexity in the data. For example, instead of fitting a straight line (linear), polynomial regression can fit a parabolic curve (quadratic), cubic curve, or even higher-degree curves by including terms like $ x^2, x^3, $ etc., in the regression equation.

Thus, through these transformations, polynomial regression effectively provides a way to model relationships where linear regression would fail, making it a powerful tool in statistical modeling.

Question

### Final Answer
**True**

Polynomial regression indeed can model nonlinear relationships by transforming the features. In a standard linear regression model, the relationship between the independent variable(s) and the dependent variable is linear, which means it can be represented by a straight line. However, many real-world phenomena are nonlinear in nature.

To address this, polynomial regression extends linear regression by introducing polynomial terms of the independent variables. This transformation allows the model to capture the curvature and complexity in the data. For example, instead of fitting a straight line (linear), polynomial regression can fit a parabolic curve (quadratic), cubic curve, or even higher-degree curves by including terms like $ x^2, x^3, $ etc., in the regression equation.

Thus, through these transformations, polynomial regression effectively provides a way to model relationships where linear regression would fail, making it a powerful tool in statistical modeling.

Knowee AI · Accepted Answer

### Final Answer
**True**

Polynomial regression indeed can model nonlinear relationships by transforming the features. In a standard linear regression model, the relationship between the independent variable(s) and the dependent variable is linear, which means it can be represented by a straight line. However, many real-world phenomena are nonlinear in nature.

To address this, polynomial regression extends linear regression by introducing polynomial terms of the independent variables. This transformation allows the model to capture the curvature and complexity in the data. For example, instead of fitting a straight line (linear), polynomial regression can fit a parabolic curve (quadratic), cubic curve, or even higher-degree curves by including terms like $ x^2, x^3, $ etc., in the regression equation.

Thus, through these transformations, polynomial regression effectively provides a way to model relationships where linear regression would fail, making it a powerful tool in statistical modeling.

Polynomial regression can model nonlinear relationships by transforming the features.Group of answer choicesTrueFalse

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