1
$\begingroup$

The R-squared value for a regression analysis on two predictor variables is 0.90. Explain what this means.

This implies that two predictor variables account for 0.90 or 90% of the total variation in its outcome variable. Since R2 = 0.90 is very close to 1, this suggests that most of the variability in y is almost perfectly explained by the regression model.

Did I interpret the R-squared correctly?

$\endgroup$
1
  • 1
    $\begingroup$ "Explanation" is a different kind of concept. It would be better to say, 'can be predicted by knowledge of the regressors'. $\endgroup$ – gung - Reinstate Monica Jun 7 at 15:21
3
$\begingroup$

Your answer seems good to me. Two little caveats: First be aware that this is only an in-sample measure. Within the sample that the model was trained on, you have 90% variance explained. Unless you have a very large sample, do not expect the same for fresh data that the model was not trained on. Also be cautious with statments like '$R^2$ is very close to $1$'. Depending on the field you work in and the question your are investigating $R^2 = .9$ may be huge or insufficiently small.

If you compute your bank account from the amounts you payed in and the amounts that were spend from that account, you'll expect a prediction much more precise then 90%.

$\endgroup$
2
  • $\begingroup$ Thank you for your inputs. It surely helps me a lot. $\endgroup$ – Aron Louise C. Morales Jun 4 at 7:33
  • $\begingroup$ @AronLouiseC.Morales, if this answer helped you, please consider accepting it (by clicking the check mark below the vote total) & upvoting it (by clicking on the upward triangle above the vote total). $\endgroup$ – gung - Reinstate Monica Jun 7 at 15:20

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.