As you add irrelevant independent variables to a regression model,the coefficient of determination R² will increase.

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Quizplus · Accepted Answer

Adding irrelevant independent variables to a regression model can increase the coefficient of determination (R^2) because R^2 measures the proportion of the variance in the dependent variable that is predictable from the independent variables, and adding more variables (even irrelevant ones) can artificially inflate this measure by capturing more variance, even if it's not meaningful to the model's predictive power.

As You Add Irrelevant Independent Variables to a Regression Model,the