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Dummy variable trap.

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Last time we learned
how to create dummy variables

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to replace our categorical predictor
estate in the model, we also discussed

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that you can never include
both dummy variables at the same time.

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In our example,
we omitted the California dummy.

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Now why is that?

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What will happen if we include the second
dummy variable in the model as well?

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Let's see.

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The intuition here is that you're
basically duplicating a variable.

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This is because D2
always equals to one minus d1.

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The phenomenon
where one or several independent variables

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in a linear regression predict
another is called multicollinearity.

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As a result of this effect,
the model cannot distinguish between

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the effects of D1 from the effects of D2,
and therefore it won't work properly.

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And this is called
the dummy variable trap.

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If you do the math behind the scenario,
you will see that

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the real problem is
that you cannot have these three elements

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in your model at the same time
the constant and both the dummy variables.

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I will leave it up to you
to figure this one out on your own,

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and let me know if you have any trouble.

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I'll definitely help you out.

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So to sum up, whenever you're building
a model, always omit one dummy variable.

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And this applies irrespective
of the number of dummy variables

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they are in that specific dummy set.

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If you have nine,
then you should only include eight.

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If you have 100.

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Then you should only include 99 of them.

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Also note that
if you have two sets of dummy variables,

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then you need to apply the same rule
to each set.

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For instance,
we could have had a column which specifies

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the industry
in which the companies operate.

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To build the model.

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In that case, we would have had to perform
exactly the same steps and create

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another set of dummy variables
specifically for that column.

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And then we would include all but one of
those dummy variables in our actual model.

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I hope this explanation was useful,
and you will never fall

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victim of the dummy
variable trap in your modeling.

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Next time we're going to cover
the different ways you can build a model.

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We will learn all about backward
elimination,

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forward selection, stepwise regression,
and much more.

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It's going to be an exciting tutorial
and I look forward to seeing you then.