Answer-Assertion (A): A very high R2 value (e.g., 0.98) in a multiple linear regression model always indica...

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Analysis of the Assertion A  The assertion is false for two primary reasons    Correlation vs. Causation R2 measures the proportion of variance in the dependent variable explained by the independent variables. It does not prove a causal relationship.   Predictive Accuracy A high R2 on training data does not guarantee success with new data. If the model is overfit too complex for the amount of data, it may have a high R2 but fail miserably when predicting future outcomes.    Analysis of the Reason R  The reason is true and highlights why high R2 values can be misleading    Inflated R2 Mathematically, adding any variable to a regression modeleven random noisewill either keep the R2 the same or increase it. It will never decrease. This is why Adjusted R2 is often preferred.   Multicollinearity If independent variables are highly correlated with each other, they can produce a high R2 even if the individual predictors are not statistically significant.       Key Takeaways for High R2 Values

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