If we have a linear regression of the form

$$ Y = \beta_0 + \beta_1X_1 + \beta_2X_2 $$

is it valid to interpret the coefficient $\beta_1$ as the associated change in $Y$ when $X_1$ increases by a unit of 1, when $X_2=0$?

  • $\begingroup$ The fact that rate of change of (expected) $Y$ wrt $X_i$ is the same no matter what the values of the $X$ variables are is essentially our assumption that the relationship is linear. $\endgroup$ Sep 13, 2022 at 22:06

1 Answer 1


Just omit the last caveat and your interpretation is basically accurate, though I will add one small edit based on dipetkov's helpful comment:

$\beta_1$ is the associated change in the expected $Y$ when $X_1$ increases by a unit of 1.

This holds whatever the value of $X_2$ (conditional on the model being accurate, though this caveat is also covered by the addition of 'expected' to the definition). Variation in $X_2$ is irrelevant here because there is no interaction term in your model.

  • 1
    $\begingroup$ I'd like to be explicit and say "the associated change in the expected Y" or "the expected change in Y associated with". $\endgroup$
    – dipetkov
    Sep 13, 2022 at 5:35
  • $\begingroup$ Thanks, is what I have a special case or is what I have incorrect as it stands? $\endgroup$
    – user321627
    Sep 13, 2022 at 6:24
  • $\begingroup$ @dipetkov Thanks, that is a good idea. I have edited to reflect that. $\endgroup$
    – mkt
    Sep 13, 2022 at 6:49
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    $\begingroup$ @Stef: it is a linear regression model - typically not all points fit the equation exactly, but there is assumed to be an error term with zero mean and independent of the $X_i$. $\endgroup$
    – Henry
    Sep 13, 2022 at 13:17
  • 2
    $\begingroup$ @Stef Regression is a model for the conditional mean E(Y | X = x). To understand this better, consider the Difference between confidence intervals and prediction intervals Also take a look at Section 10.2 in Regression and Other Stories. It's freely available online. $\endgroup$
    – dipetkov
    Sep 13, 2022 at 13:27

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