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In this were gonna learn how to take a given dataset
enter into our calculator and then use the calculator to help us find what's
called the line
of best fit. Using a process called linear regression.
So the data that we have here should look familiar
let's run through the steps again to get the data into our calculator. So
for step one we're gonna press stat and then
edit, which is the first option, and the data that are here
should reflect the data that are in the table
on the screen. So you gonna enter in 0 through 5 in column L1
and the numbers that are listed in the
output row here, into column L
2. once you do that go on down to step
2. Which is to turn on your stat plot and graph the data in the appropriate
viewing window.
Let's go back to Y equals. To turn on the stat plot you're going to
arrow up to plot one, hit enter. Then we're
return to Y1. Then
let's check our window. Appropriate viewing window for this data set is
0 to 5
Xmin to Xmax. 185 to 197,
Ymin to Ymax, then hit graph
and that should give you the same shape that you see
in the screen over here on the right.. Now step 3 to access
linear regression section of the calculator we're gonna go back to the
STAT menu
but now we want this calculate area.
So we're gonna arrow once to the right and now we have all our options for
calculating with the data that are
entered into L1 and L2. We are interested in linear regression
love the form a X plus be this is like what we're used to
MX plus P so I'm any use my arrow keys to go down to
option for now my screen
looks like what we see here when I scroll down on the pace you can see what
happens next we hit enter
that's gonna tell the calculator to do linear regression on the data that are
in l1 and l2
gonna hit enter again and that gives the information in this window
here so if you look over at the steps on the right hand side
then the a and the b value are the slopes and the y-intercept
so if we identify those to two decimal places
we get a is negative 1.69
Mb is 190 5.3 8 to write are linear equation
we replace slope with negative 1.69 St a value
and the y-intercept with not 190 5.38
let's write that equation in terms of the initial variables which were time
input and wait output and then finally
are result in function notation WFT
is negative 1.6 19 plus $1.95 point 38
so you really not done into you get to this
final stop here in terms of writing your equation
now what we want to do is to plot that equation to graph it using a calculator
and see what it looks like compared to the data that we have
serving a lever plot on and in the Y one
we're gonna answer negative y/n point 6
9 in this case axe plus
y/n 9 5 .3
ain't so there's our regression equation it is are rounded
equation and then so that's what we've done here
enter the regression equation with the rounded values
that i'm gonna press graph and that's gonna give me the picture
above the line a best-fit notice that that line doesn't
really hit very many ever data points however what your cat litter has done
is to take into account all the data points that are there in the dataset
and give the line that is the best approximation
for all of them again as an
are the previous example that we did when you're finished you need to turn
off your plot
so go back to why equals scroll up to plot one
hit enter and then return and make sure that none of your plots are turned on
when you finish working with data