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Hello and welcome back.

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Let's have a quick look at.

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Multiple linear regression.

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So here is the equation
for multiple linear regression.

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As you can see is quite similar
to the simple linear regression equation.

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Here we've got the dependent variable.

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Then we've got the y
intercept of the constant.

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Then we've got a slope coefficient
an independent variable.

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And then we've got more pairs of those.

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Then we've got another slope coefficient
an independent variable and so on.

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Add another slope coefficient
an independent variable.

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So basically we have as many slope
coefficients as we have independent

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variables.

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Now going back to our example
of producing potatoes on a farm,

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we might want to predict how many potatoes
we are yielding

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depending on how many kilograms
of nitrogen fertilizer we're using.

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What the average temperature, in
the season is and what the millimeter is,

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how many millimeters of rainfall,
we've seen in the season.

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So the potatoes in this case,
the equation for potatoes would be,

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for example, eight tonnes would be a
y intercept.

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then three tonnes per kilogram
would be the amount of fertilizer,

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the, coefficient for fertilizer used.

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Then for average temperature in degrees
Celsius,

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we might have a -0.54 tonnes
per degrees Celsius.

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And what that means

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is that the higher the temperature,
the less potatoes will be yielded.

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and then for the rainfall might be 0.04
tonnes per millimeter.

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Now, to finish off, what I wanted to say
is that, this we won't be using

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this example in all practical terms
will be a different example.

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It'll be fun
and interesting example as well.

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But if you wanted to learn more
about potatoes

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and specifically the applications
of multiple linear regression,

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here's a research paper
that you might be interested in.

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it's called the Application
of Multiple Linear regression

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and artificial Neural network models

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for yield prediction of very early potato
cultivators before harvest.

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So, this paper combines a multiple linear
regression and neural networks.

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It looks at two different ways
of modeling them.

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of course you can just focus on the
multiple linear regression if you like.

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It's quite an interesting read and
it can give you some additional, deeper

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knowledge into the world of harvesting
potatoes and multiple linear regression.

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There.

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Other than that, enjoy
the practical tutorials and for lunch,

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and I'll see you back here next time.

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Until then, enjoy machine learning.