In the last video, we have seen the result of our linear regression model, in this lecture, we will
learn how to use that result to predict the future values.
Now, if you remember.
This is the equation of linear regression.
Y equal to be not let's be the next one plus B two x2 plus B three 3x three up to be a Nexen, this
value is over.
That is the intercept value we have calculated B one, B to be three up to be in and these values are
located here.
So this one is one, this one is B2, this one is B three.
This one is before and so on.
No, afeared be not blessed be the next one that is this BE1 into this one.
And similarly, if we are the multiplication of these B with other independent values, we will be able
to find out the valuation of our business.
That is the variable.
So using this equation, we can find out the predicted values of why.
So let's copy this coefficient values in our previous shoot.
No, I have copied the Confucian values.
This one is Betelnut.
I can see that and for the so large, the value is 469 and you can see just about the store large.
We have Mr. Doko vision value for this, very similarly for a store equal to medium value.
We are getting is a 32 and you can see that here also for his store medium that we would lose 800 or
so.
VFP said the conversion values corresponding to this variables on the top row of this shoot.
So now if we take the sum of bitterness, plus with our next one, we take two weeks to be three x three
to four x four and so on, we will be able to get the valuation value.
So now let's predict the values for this first store.
Now, the equation is this, so we'll follow this equation first, we have to add we betelnut.
So our first term is Betelnut, which is this.
And then the second term is the multiplication of this value coefficient and to.
This X1, there's the value of forex, rebel or independent or rebel, then we have the application
of this into this.
Plus, this.
And to this.
There's this.
And to this, so this is the Corporation for Sumitra and this is the variable city metro.
So we are multiplying both of these two values.
Then we have coefficient of investment and to the value of investment, plus coefficient of number of
competitors and the number of competitors.
Plus.
This.
And to.
This.
Plus.
This to this.
So the value we will be getting from this equation will be the predicted value of valuation for this
system.
So the predicted value for this store one was 86000 and in reality, we are getting the valuation this
71.
And similarly, we can find appropriate value for the rest of those stoats.
And we can expand this formula, but before expanding, we have to fix the cell referencing.
So just fix the cell referencing for all this coefficients, we don't need to fix the cell referencing
for this X variable because when we are dragging down, we want these values to be changed accordingly.
So BE2.
Is our goal for you, it is also efficient.
I have to is also efficient due to its coefficient, etc. is also vision.
I talked to two.
Care to find to.
So we have extra fencing for all the coefficients, no, let's try dragging it down.
Now, for example, if you just click, you can see that the formula is working fine, we are adding
Binet with the multiplication of between X1 better to extra Pittodrie extra and so on.
So the formula is working fine.
And you can see that for the second they saw the predicted value is sixty five thousand.
And in reality, we are getting a valuation of 68.
Similarly, you can compare these values for other stores as well.
Let's expand the line so that we can get this value for all the 13000 stores.
Now, how are we going to use this model?
So suppose if your company or SBA retailers want to open another store.
And they want to protect the valuation before opening that store.
So here you can fill out all the details and you will get the predicted value of that.
So.
Let's fix the Penn.
The shortcut is w f or else you can just select the cell, go to this view option.
And click on this free speech on what this will do is if you just scroll down, this top three rows
are not going to move out of your view.
And in a way, you will be able to see the hiders of your data.
While scrolling as well.
So, yeah, let's suppose we are opening a new store.
And you have some data of your restored, so the first information that you have about this story is
that it is going to be a medium size store.
So just straight.
Medium.
Now for location, location is going to be a commercial location.
Now, the city is Michael.
So just fill out.
The information of the new search that you are planning to open.
No investment, the company is planning to invest around 55000 for this store, the number of competitors
in this area is just too.
And the population is around 12000.
The average household income is twenty five thousand.
So with the data that we have entered, we are getting a valuation predicted valuation of 90000.
So we are going to invest 55000 and we are getting a valuation of 90000.
So I think this is good for S.T. to open this location.
So similarly, if Steve wants to open some new store, they just have to put information of the store
that they are going to open and they will be able to find the valuation with this linear regression
model.
So that's how we protect the values of our dependent variable using the linear regression.
So that's all for this case study.
Thank you.