Assume that you have used a set of data on the TI-Nspire to create a scatter plot and perform a regression. Perhaps you want to construct a second scatter plot and associated regression on the TI-Nspire. Use the data from the table provided.

United States Immigrant Population Since 1900
Years Since 1900 Immigrant Population (in millions) U.S. Population (in millions)
0 10.3 76.2
10 13.5 92.2
20 13.9 106
30 14.2 123.2
40 11.6 132.2
50 10.3 151.3
60 9.7 179.3
70 9.6 203.2
80 14.1 226.5
90 19.8 248.7
100 31.1 281.4
  1. Enter the data.

    If you haven’t already done so, refer to the table and enter the data for U.S. Population in column C.

  2. Configure a second scatter plot.

    On the Graphs page, configure a second scatter plot (s2 in this case) to plot year for x and us_pop for y. Make sure that you delete the value in cell A12; otherwise, you will get an error message. The dimensions of two lists must be the same, or you cannot perform a regression.

    Because you are viewing a new scatter plot, the window settings must be adjusted.

  3. Change the window settings. Press [MENU]→Window→Zoom – Data. See the first screen.

  4. Decide on a regression model.

    Although the scatter plot might suggest a linear model, what you know about population growth also suggests that an exponential model might be a better fit.

  5. Perform an exponential regression:

    • Go back to the Lists & Spreadsheet page and press [MENU]→Statistics→Stat Calculations→Exponential Regression.

    • Configure the dialog box as shown in the second screen, and press [ENTER] to view the results of the regression, as shown in the third screen.

  6. Graph the regression equation. Go back to the Graphs page and plot your regression equation (stored in f2) as shown in the last screen.


Notice that the value of the correlation coefficient, r, is 0.9975. . . . Had you performed a linear regression, the value of r would have been slightly lower (0.9906 . . . ). This suggests that the exponential regression provides a better fit than a linear regression.

You may have noticed that the second scatter plot consists of hollow points to offset them from the first scatter plot. You can change the attributes of a scatter plot by moving the cursor on the scatter plot and pressing [CTRL][MENU]→Attributes. In total, you have nine different scatter plot styles to choose from.