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?

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    $\begingroup$ "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

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%.

  • $\begingroup$ 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

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