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.
Which we converted into a corresponding dummy variable called airport.
Yes.
With values one and two.
Second categorical variable was body, which had four categories, and we made three dummy variables
with value zero and one to correspond to this variable.
Let's see what that model has to say about these variables.
Let's talk about the airport variable first.
We look at the equation.
Here I have kept only airport in the linear equation.
You can keep all other variables also.
So basically the equation is why is equal to all the other variables multiplied with the coefficients
plus constant plus Biddulph Airport and to the airport variable.
What this means is there are two possibilities.
If there is a report, which means the value of x ray is one.
Then we'll have value of all the other predicted multiplied by the collisions plus the constant plus
beta one.
And if it is zero, this beta one will not be present since it's a zero, so beta one into XY is zero.
Therefore, this of airport is straightaway giving us the different and host place.
If there is an airport and if it is not.
So if I look at the results of my model airport, yes, variable has an estimate of one point one three.
This is GBW.
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 unit.
Also, look at the P value for this 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 that is not an
airport.
Also note that this cording of airport.
Yes, as one.
An airport known as zero is arbitrary.
We can use other venues also.
The desert will have the same interpretation regardless.
These values.
And the other variable, which is autobody.
We had three dummy variables.
So we received three different beta values for all the three variables.
So if you remember and 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.
Nada.
They were in the area.
So now what is the meaning of each of these variable waterboarding 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 Water Body River, it is saying that if there is a river, the host
price will go down in comparison to it.
There is no waterboarding 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 B does.
But statistically, we are not confident that the relationship given by these B does actually hold or
not.
So this is how qualitative variables that handle an interpreter didn't Linnean model.
We first transform them into dummy variables of and minus one categories.
Then we run the regression, then looking at the betas and the P values.
We interpret dessert.