How does a higher R-squared value impact the inference in multiple linear regression?

It decreases the number of observations
It improves the interpretability of the model
It increases the residuals
It makes the model more complex

The R-squared value measures the proportion of the variance in the dependent variable that is predictable from the independent variables. A higher R-squared value, closer to 1, implies a higher proportion of variability in the response variable is explained by the predictors, improving the model's interpretability and predictive power.

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