In this video, we will discuss the results we received for the categorical variables, if you remember,
there were two categorical variables in our dataset.
One was airport with values, yes and no.
But we converted into corresponding the variable called airport.
Yes, with values 192.
Second categorical variable was waterboarding, which had four categories, and we made three dummy
variables with value zero and one to correspond to this very well.
Let us see what that model has to say about these variables.
Now, let's talk about the airport very well first.
We look at the equation.
Here I've kept only airport in the linear regression, you can keep all other variables also.
So basically the equation is Y is equal to all the other variables multiplied with the coefficient plus
constant plus bit of airport into the airport variable.
What this means is there are two possibilities if there is an airport, which means the value of X is
one.
Then we'll have value of all the repeaters multiplied by the commissions plus the constant plus between.
And if it is zero, this one will not be present since it's a zero, so we don't need to say is zero.
Therefore, this bit of airport is away, giving us the difference in price.
If there is an airport and if there is not.
So if I look at the result of my model airport, this variable has an estimate of one point one three,
the GDP value.
So this means that if there is an airport and all the other variables are seeing the value of the house,
the price of the house will increase by one point one three units.
Also, look at the p value for this, the p value is low, which means there is a statistical evidence
of a difference in the house price, depending on whether there is an airport or whether there is not
an airport.
Also note that this coating of airport, yes, as one an airport known as zero is arbitrary.
We can use other venues also.
The result will have the same interpretation regardless these values.
In the other variable, which is waterboarding, we had three dummy variables.
So we received three different beta values for all the three variables.
So if you remember in this, we selected the baseline of waterboarding, none, that is, we said that
all of these values as zero will mean that there is neither a leak nor a river in the area.
So now what is the meaning of each of these variable autobody lake means that compared to the situation
where there is no water body, if there is a lake in that area, how much will the price increase?
So zero point two six units will be the increase in price of house if there is a lake in the area.
If you look at the lake and river coefficient, it is minus point six eight, which means that if there
is both lake and river, the house price will go down in comparison to if there was no water body in
the area.
And the third variable, which is what was it, is saying that if there is a river, the house price
will go down in comparison to if there is no water body in that area.
Next thing we have to look at is the P-value, all of these P values are large, which means that there
is no statistical evidence that there will be an impact of these three variables on the house price.
So all although the relationship is given by these betas, but statistically, we are not confident
that the relationship given by these betas actually holds or not.
So this is how qualitative animals are handled and interpreted in linear model.
We first transform them into dummy variables of and minus one categories.
Then we run the regression.
Then looking at the Beatles and the P values, we interpret the result.