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Hello and welcome back.

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Let's have a look at simple linear
regression.

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So here's the equation.

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And we will look at the parts
of this equation one by one.

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So on the left we have our dependent
variable which we're trying to predict.

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On the right we have our independent
variable which is the predictor.

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here we have b0 which is the y intercept
also known as a constant.

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And b1 is the slope coefficient.

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Now to make things more fun
we're going to use that example

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we mentioned about predicting
the output of potatoes

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on a farm based on the amount
of fertilizer that we use.

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So here's our equation.

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And if we modify it to fit our example
it will look like this.

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And let's say that we ran
the simple linear regression algorithm.

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And it came up with the following values
B0 equals eight tons

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and b1 equals three tons per kilogram.

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So what does this mean
in terms of the graphical representation.

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How to how can we better understand
this on an intuitive level.

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So let's plot a simple scatter plot.

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So here we have on the x axis
a nitrogen fertilizer used in kilograms

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as our x1, variable.

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And here we have the Y variable,
which is a potato yield in tons.

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And here on the scatter plot
we have several data points.

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What are these data points.

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Well each one represents
a separate harvest on the farm

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that we are talking about.

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So, multiple times, 
the potatoes were harvested over

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many years, and the farmer recorded
how much fertilizer they used

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and also how many potatoes they were able
to harvest in that specific season.

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So there is a scatter plot.

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And what this, equation on the left
represents

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is a sloped line
that goes through a point.

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the y intercept is over here.

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That's our eight tons.

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And what the slope coefficient means
is that if you increase

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the amount of nitrogen fertilizer
by one kilogram,

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then the amount of potato output

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will increase by three tons.

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And of course, these numbers
are made up for illustrative purposes.

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So there we go. That's
how the simple linear regression works.

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I look forward to seeing you next time.

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Until then, enjoy machine learning.