IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The R-squared value, or the coefficient of determination, plays a crucial role in regression analysis as it quantifies how well the independent variables explain the variability of the dependent variable. It ranges from 0 to 1; an R-squared of 0 means that the model does not explain any variability in the response data around its mean, while an R-squared of 1 means that the model explains all the variability.

Understanding R-squared is vital for evaluating the goodness of fit of a model. A higher R-squared value indicates that a greater proportion of variance in the dependent variable is accounted for by the independent variables, reflecting a better model performance. This metric helps data scientists to determine which model best captures the underlying trends of the data and assists in model selection.

In contrast, the other options address different concepts. The accuracy of predictions is a broader measure that encompasses various aspects of model performance but is not specific to what R-squared quantifies. The speed of convergence pertains to optimization algorithms and how quickly they find the best-fitting model, which is not related to R-squared. Lastly, the relationship strength between categorical variables generally involves measures such as chi-squared tests or Cramér's V rather than R-squared, which