
(1) First carefully plot the points, on graph paper, or use a graphing calculator or a computer program with graphics.
To enter data into L1 (these numbers should be the x-values of the points you wish to plot), place the cursor on the first position in the column (see the left screen below), type in the first number, press ENTER, which moves the cursor to the next position in that column. Enter the next number, press ENTER, and continue the process until you have all the values entered in column L1. Then use the right cursor to go to column L2 and enter the y-values in this column. The x and y values of each pair of data points should be side by side in these two columns. Also, you must have the same number of entries in each column. Once the data points have been entered, you are ready to plot them with a scatter plot.


Before plotting the data points, do two things:
(2) Observe the scatterplot. Do the points appear to
lie almost on a line?
If so, then we want to find a line that is a good "fit." There are
many ways to do this.
If your points are on graph paper,
you can just use a clear plastic straightedge and manipulate it until
it
seems to best fit the points, then draw the line. Once the line
is
drawn, you can calculate the slope of the line, and find a point on the
line, and then give the equation of the line using the point-slope
formula (slope = m, (a, b) on line, equation is y = m(x -
a) + b). You can also use the calculator to find the "least-squares"
regression
line.
(3) Regression line (least squares fit).
Statisticians often use a standard method to find a "fit" line, called a least squares fit (the "fit" line is called a regression line). This method finds the line that minimizes the sum of the areas of the squares that have their vertical edges drawn from a data point to the line. The TI-83 can calculate the equation of this regression line with a few keystrokes. The animation below depicts several possible regression lines. It displays the "squared errors" for each data point (green boxes) as well as the total squared error (yellow box). A least squares regression on the TI-83 will select the line which makes the area of the yellow square as small as possible.



The screen displays the slope b and the y-intercept a of the regression line, and this equation is automatically entered as Y1 on the Y= screen. You can press GRAPH to see how well it fits the data (assuming STATPLOT 1 is still displayed and you have a good window chosen). You can access the value of the correlation, r, by pressing [VARS] [5] (to access the statistical variables) then scroll to the [EQ] menu (press the right arrow key twice) and finally press [7:r] and [ENTER]. If you'd like your calculator to always display the correlation r (and also r2) whenever it does a linear regression (like the right screen above), press [2nd][CATALOG], then [D] to jump to the commands which begin with the letter D, scroll down a few lines to the command DiagnosticsOn and press [ENTER] (twice). Now r will automatically be displayed whenever you execute a linear regression command. The residuals of the least squares regression line (the values found by subtracting the true y-values from the regression calculated values) will be stored in a list named RESID (or perhaps LRESID), which can be accessed by pressing [2nd][LIST] and then scrolling down to the line with RESID on it and pressing [ENTER]. You can see a residual plot by setting stat-plot 2 as a scatterplot, with XList = L1 and YList = RESID.