<p>In principle you could run the regression on subsample but there are far better alternatives. Any time you are splitting the sample the two subsequent regressions will loos a lot of degrees of freedom and hence the estimates will be less precise. Also, 250 observations is quite a low amount, in a linear regression you should have at least 25-30 observations per independent regressor to justify the asymptotic properties of your estimator and more is always better.</p>
<p>Instead of running 2 regressions if you are interested in knowing effect of working more than 50 hours on the dependent variable just add an dummy to your regression.</p>
<p>You can run a following model:</p>
<p><span class="math-container">$$y_i = \beta_0 + \beta_1 D_i + \beta_2 w_i + \beta_3D_iw_i + e_i $$</span></p>
<p>where <span class="math-container">$y_i$</span> is your dependent variable, <span class="math-container">$D_i$</span> is a dummy variable that indicates if individual <span class="math-container">$i$</span> works more than 50h (<span class="math-container">$D_i=1$</span>) or less than 50h (<span class="math-container">$D_i=0$</span>), <span class="math-container">$w_i$</span> is the amount of working hours individual works, <span class="math-container">$D_i w_i$</span> is an interaction term and <span class="math-container">$e_i$</span> is the error term.</p>
<p>This way you can extract the effect of working over 50h without splitting the sample into two subsample avoiding the loss precision.</p>

CC BY-SA 4.0
2022-04-28T19:19:57.367Z
As a note, if you exclude the interaction term, you assumes the effect is entirely a transition in $E(y_i|D_i)$ for the two groups. The interaction term shows how the marginal impact of those hours changes at 50.
RegressForward
CC BY-SA 4.0
2022-03-27T15:39:09.083Z
2022-03-27T15:39:09.083Z
1muflon1
<p>I study the influence of long working hours on health. In my sample 40% of people work more than 40 hours and other 60% don’t. I want to look at the effect of working exactly more than 50 hours compared to less or equal 40. Can I just take all people who work more than 50 hours from my sample (about 250 people) and those who work less or equal 40 (about 1600 people) and run a regression? Is it ok that 250 is much less than 1600? If not, how can I do it?</p>

<econometrics><regression><logit>
Can I run regression on uneven subsample?
Daria
# Can I run regression on uneven subsample?

(Asked by: username_0 on 2022-03-27)

I study the influence of long working hours on health. In my sample 40% of people work more than 40 hours and other 60% don’t. I want to look at the effect of working exactly more than 50 hours compared to less or equal 40. Can I just take all people who work more than 50 hours from my sample (about 250 people) and those who work less or equal 40 (about 1600 people) and run a regression? Is it ok that 250 is much less than 1600? If not, how can I do it?

**Tags:** econometrics, regression, logit


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## 1 answers

### Answer by username_1 on 2022-03-27. Score: 1

In principle you could run the regression on subsample but there are far better alternatives. Any time you are splitting the sample the two subsequent regressions will loos a lot of degrees of freedom and hence the estimates will be less precise. Also, 250 observations is quite a low amount, in a linear regression you should have at least 25-30 observations per independent regressor to justify the asymptotic properties of your estimator and more is always better.

Instead of running 2 regressions if you are interested in knowing effect of working more than 50 hours on the dependent variable just add an dummy to your regression.

You can run a following model:

$$y\_i = \beta\_0 + \beta\_1 D\_i + \beta\_2 w\_i + \beta\_3D\_iw\_i + e\_i $$

where $y\_i$ is your dependent variable, $D\_i$ is a dummy variable that indicates if individual $i$ works more than 50h ($D\_i=1$) or less than 50h ($D\_i=0$), $w\_i$ is the amount of working hours individual works, $D\_i w\_i$ is an interaction term and $e\_i$ is the error term.

This way you can extract the effect of working over 50h without splitting the sample into two subsample avoiding the loss precision.

> **Comments:**
> **username_2**: As a note, if you exclude the interaction term, you assumes the effect is entirely a transition in $E(y_i|D_i)$ for the two groups. The interaction term shows how the marginal impact of those hours changes at 50.  

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