Press the Y= key and clear any equations.
Press the STAT key, use the arrow keys to select EDIT, and enter this data:
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The numbers under the L1 heading are years. The numbers under the L2 heading are the mile world records set in those years.
Press the STAT key, select CALC, and choose option number 4: LinReg. This will bring you back to the home screen, with LinReg showing. Type "L1, L2" by pressing 2nd and 1, then comma, then 2nd and 2. Type "Y1" by pressing VARS, moving across to Y-VARS, choosing Function, and choosing option number 1. Your home screen should show:
LinReg L1, L2, Y1
Now press ENTER. You will see something like:
y = ax+b
a = -.403019845
b = 1020.628689
The variables a and b are the coefficients for the linear equation that best fits the data you entered.
Press the StatPlot key (2nd and Y=). Choose Plot1 and press ENTER. Set the options as follows:
On
Type
Scatter
(the first icon)
XList
L2
YList
L2
Mark
Box
(the first icon)
Press the WINDOW key and enter the following:
Xmin
1900
Xmax
2000
Xscl
25
Ymin
200
Ymax
300
Yscl
25
These numbers will tell the graphing calculator what part of the graph it should display. When you do a stat plot, you need to look at your data and choose values that will include your data. You can let the calculator choose these values for you by pressing the ZOOM key and choosing option number 9: ZOOMSTAT.
Press the GRAPH key. The graph will show the individual data points as well as the best fit line. Press the TRACE key and use the arrow keys to predict the future of the world record in the mile (you may need to use the up arrow key to trace the line).
What will the record be in the year 2000?
When will someone first run under 3:30 for the mile?
To see exactly how closely your best fit line fits the data, press the VARS key and choose option number 5: Statistics. Move across to EQ and choose option number 7: r. This will display r on your home screen. Press ENTER to see the value of r.
r
-.9990286552
The variable r is the regression coefficient. It is a measure of how well the best fit line fits the data. It is always between 1 and -1. The closer it is to either 1 or -1, the better the fit.