https:/.../ti-n-spire-2-linear-regression-and-correlation-coefficient.mp4

You're going to insert a spreadsheet

and you're going to call it
distance and plant species

for x and y as given.

X and y and go through
the data,

2, 5, 8, 10, 13

2, 5, 8, 10, 13, 17, 23

and 35 and 40.

And then the y values are...

35, 34 to start with.

35, 34, 30 29, 24, 19, 15,

(inaudible)

13 and 8.

So our data is all in there.

We can stay in this window here
and we can get all the details we need.

We want the mean for each x and y.

We can also workout the variants or
standard deviation for each.

We can also work out the r value
and the equation of linear regression

so menu, statistics, statistical
calculations and that option number three

linear regression, mx+b will do it for us.

The x list we call it the x variable.
The y list we call the y variable.

It says save regression equation
to f1 that could come in useful.

And that's all we need,
the result go into column D

which is where we are highlighted
and that's everything you need.

So mx+b is the equation regression.

The m is the gradient, -0.7079.

And the b would be the y-intercept
which might tally with our graph.

And here is the r value, -0.9648.

That would classify as very strong
negative correlation.

And as we scroll down we've got
everything there.

I'm going to do something else
here as well.

I'm actually going to go menu,
statistics, stat calc,

two variable statistics.

Now this is going to be useful
for our projects, our interim assessment.

You probably wouldn't need this
for the actual exam,

but let's give it a go.

Okay, so here is what we might need,
so the average for x is given there,

17 and the standard deviation
is given here, 12.48.

We use this little guy here,
the sigma notation,

rather than the s notation.

And y bar is the mean for y
is 23 and the standard deviation

for y given that this little circle
here sigma is 9.165.

And the r value is also calculated here.

You can also get the medians in
quartiles for both x and y.

Okay so you'll need to have
both of those functions available to you.

The linear regression function here
and the two variable statistics.

It would also be nice if we went doc,

inserts, data and statistics and we
can actually plot a graph to

see what it looks like.

There it is.

And we can actually super impose
the line of best fit here.

If we go menu, we could go to
actually analyze and we can

actually plot function, because
do you remember when it was saved?

Do you remember where plot
function was saved?

Linear regression... f1.

So if we just type in f1 of x,
it will plot in our equation there.

So you got your line of best fit.

And your graph should look
very similar to that.

And when you draw your line
of best fit on your graph

it should look like that as well.

Okay.