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Say I had a multiple regression model with two independent variables and one dummy variable, and I wanted to demonstrate the effect the dummy variable has on the dependent variable (and the other two explanatory variables), how can I graphically show the relationship? Is it okay to produce two graphs where each has a different explanatory variable on the x-axis (as such)? I'm just confused as most examples only involve one explanatory variable (thus involves one graph) and I feel like by regressing two different models (with different x-axis), I'm not really achieving my goal (which is to demonstrate the effect of the dummy variable on the dependent variable with regards to the entire model).

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You can just plot the marginal effects from your model, which should produce something like this:

enter image description here

In a linear model, these are just the coefficients from the regression that tell you the change in price associated with a one-unit change in each control variable, holding the others constant. The figure also shows the 95% CI from the regression table.

Here's the code. Note that I rescaled the units of mpg and weight variables to make the graph a bit easier to read.

sysuse auto, clear
replace mpg = mpg/100
replace weight = weight/1000
reg price i.foreign c.mpg c.weight
margins, dydx(*)
marginsplot, horizontal xline(0)
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  • $\begingroup$ Consider also an added variables plot (in Stata, check out avplots or favplots from SSC). $\endgroup$ – Nick Cox May 17 '20 at 6:58

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