So we have learned how to run a simple linear regression model.
It is time to learn how to run a multiple linear regression model.
Not.
In multiple linear regression model.
Instead of using one predictor, variable will be using multiple predictive variables.
So let us first run this model with all the variables that we have in dataset to.
We'll just create and one even call you multiple Morlin.
This will get you on the same function, L.M..
And when the bracket will come in place, this is the dependent variable, then a dollar.
Well, if you want all variables from your data, say dust right around.
So apart from price, it will take all the news from your day, does it?
And what did your data say that will define no comma?
Data is equal to be it.
If put on this.
We have another variable called McLibel model.
Look at the design.
All right, somebody.
I went back and realized makeable model.
But in this.
To continue, we have the that we want.
We have better values in this column of all the variables.
These are all the variables that we had.
You have better values.
This is because it'll be done.
We do.
And so on.
This is the column giving a standard error, just as saw in Berlin, integration, that is devalue.
This is P-value.
And if you remember these, Tojo's telling us the level of significance, so point zero zero one means
we had ninety nine point nine percent confident of this redub being non.
Those does will mean you're 99 percent confident.
And a single star will mean that we are 95 percent confident that this mutation on.
So basically, you're getting six variables with these starts.
Four of them are ninety nine point nine percent sure that they are related with these guys.
Or two of them.
We have 95 percent confidence that they are impacting the place we live in.
What are those?
Although they have a non it'll be done.
But we are not really sure that they are actually impacting the response video.
Not.
So this part is telling you about the coefficient and the significance level for each individual variable,
and this part is about Diebel model.
What this whole model did is a double standard.
It it is coming out to be forty point nine to five.
With four hundred ninety degrees of freedom.
So the degrees of freedom in simple linear regression was 500 foot because we calculated it by doing
and minus two.
So that's you're under six.
We got 504.
But in this case.
Degrees of freedom is for 90 because we are subtracting from it.
The number of variables also.
So 506 minus the 16 variables.
That is why I did this, the freedom.
Or maybe.
So the city R-squared value, and this is the adjusted R-squared value.
Australia does not take into account the number of variables.
This is coming or do we were in Toronto, meaning that 72 percent of the variance is being explained
by this model Variant A. Osprey's data, the.
I think it is a pretty good model, since it is able to explain 72 percent of the variance.
It destroyed R-squared takes into account the number of variables.
That is why it is coming out to be little less.
Since it has a lot of variables.
You increase the number of variables, it go down.
And for malleability integration.
This is a more useful parameter.
So it is better to report adjusted R-squared in case of multiple integration.
This last is the after stick barometer.
We calculate the value of a statistic to save it.
Confidence that whether the variables that we took actually impact the response variable.
So we get a essayistic rally of eighty four point eighty four.
And it's P-value is very small.
The P value is very small.
We are pretty confident that these variables are impacting.
They respond very well.
And I just noticed that this last very one average, this is also significantly larger.
It also has three stars.
So we have at least seven variables that are significantly impacting the response variable and others,
we are not really sure.
And these are the values for all of these.
In terms of business, all this is going to help me is as follows, I can see that air quality.
Impacts the house price negatively and its leader will lose also very large.
So one unit changed equality, if it is an increase, it will reduce the house price.
But if you've been unit.
So if I want to maximize the price, I should plan to build a house.
An area with lower value of this index.
Similarly, room them has a positive impact.
If I add one unit, the house price will increase by four units.
Now, you can also see that room number.
Eliot had a bit of a leave of mine in a simple linear model.
It had a value of nine.
Now it has a value of food.
So it's into the added new variables, the value of biddable name has also changed.
So meaning you have to know this here is this P-value is saying that your variables are impacting house
price.
So first we look at this P-value, then we go and look at these P values to identify which of the individual
variables we are confident are impacting our despond variable.
So we get these seven variables.
And from these seven variables, we look at that.
We does, and there are signs, so positive sign means if I increase, you know, it will increase house
price and buy this many units.
A negative sign means if I increase air quality unit by one, it will decrease house place by 50 per
unit.
So this is how we run multiple linear regression in odd.
And this is also how we interpret it to make business sense out of the reported result.