Answer: The estimates will converge upon the true values as the sample size, n, increases.

## Explanation In multiple linear regression, when an estimator is described as **consistent**, it means that: - **The estimates converge to the true population values as the sample size increases** - As we collect more data (larger sample size n), the estimator's output gets progressively closer to the actual parameter values - This is a large-sample property that doesn't guarantee accuracy at any fixed sample size ### Why other options are incorrect: - **A**: This describes **unbiasedness**, not consistency. An unbiased estimator has an expected value equal to the true parameter, but this can occur at any sample size. - **B**: Consistency doesn't guarantee closeness "regardless of sample size" - it specifically depends on sample size increasing. - **D**: Consistency doesn't automatically imply unbiasedness or efficiency. An estimator can be consistent but biased in finite samples, and efficiency is a separate property related to minimum variance. ### Key Takeaway: Consistency is about asymptotic behavior - as n → ∞, the estimator converges in probability to the true parameter value. This is a fundamental property for reliable statistical inference in large samples.

Author: Tanishq Prabhu