Hello and welcome back.
Let's have a quick look at.
Multiple linear regression.
So here is the equation for multiple linear regression.
As you can see is quite similar to the simple linear regression equation.
Here we've got the dependent variable.
Then we've got the y intercept of the constant.
Then we've got a slope coefficient an independent variable.
And then we've got more pairs of those.
Then we've got another slope coefficient an independent variable and so on.
Add another slope coefficient an independent variable.
So basically we have as many slope coefficients as we have independent
variables.
Now going back to our example of producing potatoes on a farm,
we might want to predict how many potatoes we are yielding
depending on how many kilograms of nitrogen fertilizer we're using.
What the average temperature, in the season is and what the millimeter is,
how many millimeters of rainfall, we've seen in the season.
So the potatoes in this case, the equation for potatoes would be,
for example, eight tonnes would be a y intercept.
then three tonnes per kilogram would be the amount of fertilizer,
the, coefficient for fertilizer used.
Then for average temperature in degrees Celsius,
we might have a -0.54 tonnes per degrees Celsius.
And what that means
is that the higher the temperature, the less potatoes will be yielded.
and then for the rainfall might be 0.04 tonnes per millimeter.
Now, to finish off, what I wanted to say is that, this we won't be using
this example in all practical terms will be a different example.
It'll be fun and interesting example as well.
But if you wanted to learn more about potatoes
and specifically the applications of multiple linear regression,
here's a research paper that you might be interested in.
it's called the Application of Multiple Linear regression
and artificial Neural network models
for yield prediction of very early potato cultivators before harvest.
So, this paper combines a multiple linear regression and neural networks.
It looks at two different ways of modeling them.
of course you can just focus on the multiple linear regression if you like.
It's quite an interesting read and it can give you some additional, deeper
knowledge into the world of harvesting potatoes and multiple linear regression.
There.
Other than that, enjoy the practical tutorials and for lunch,
and I'll see you back here next time.
Until then, enjoy machine learning.