 Compute a Regression Model on the TI-83 Plus - dummies

# Compute a Regression Model on the TI-83 Plus

Regression modeling is the process of finding a function that approximates the relationship between the two variables in two data lists. To compute a regression model for your two-variable data on the TI-83 Plus, follow these steps:

1. If necessary, turn on Diagnostics.

When the Diagnostics command is turned on, the calculator displays the correlation coefficient (r) and the coefficient of determination (r2 or R2) for appropriate regression models. By default, this command is turned off. After you turn this command on, it stays on until you turn it off. Here’s how to turn on Diagnostics:

• Press [2nd][x–1] to access the Catalog menu and to advance the Catalog to the entries beginning with the letter D.

• Repeatedly press • Press [ENTER] to paste this command on the Home screen, and press [ENTER] again to execute this command.

2. If necessary, put the calculator in Function (Func) mode.

If the regression model is a function that you want to graph, you must first put the calculator in Function mode.

3. If you haven’t already done so, graph your two-variable data in a scatter or xy-line plot.

4. Select a regression model from the Stat Calculate menu.

To do so, press to access the Stat Calculate menu. Repeatedly press until the number or letter of the desired regression model is highlighted, and press [ENTER] to select that model.

5. Enter the name for the Xlist data, press [,], and then enter the name of the Ylist data.

6. If necessary, enter the name of the frequency list.

7. Press [,] and enter the name of the function (Y1, … , Y9, or Y0) in which the regression model is to be stored.

To enter a function name, press and then enter the number of the function.

8. Press [ENTER] to calculate and view the equation of the regression model.

The equation of the regression model is automatically stored in the Y= editor under the name you entered in Step 7.

9. Press [GRAPH] to see the graph of your data and regression model. 