Graphing Calculators are the New Slide R

Graphing Calculators are the New Slide Rule

Fancy calculators allow our statistics students to do a whole lot of statistics that would be very tedious to do by hand. Unfortunately the user interface with calculators has changed very little over the last 20 years and is now far behind other technology.

Let me try to make my case. Today in class I wanted students to make a scatterplot, find the line of best fit, and make a residual plot.

Option 1: Graphing Calculator

Make scatterplot:

STAT/EDIT/Edit
Enter values for the explanatory variable in List 1.
Enter values for the response variable in List 2.
2ND/Y=/ON/Scatterplot/L1/L2/ZOOM/Zoomstat

Find line of best fit:

STAT/CALC/LinReg(a+bx) L1,L2,Y1 (VARS/Y-VARS/Function/Y1)

Make residual plot

2ND/Y=/On/Scatterplot/L1/RESID (2ND/STAT/NAMES/RESID)
ZOOM/ZoomStat

Over 30 (memorized) keystrokes. And then I get a scatterplot and a residual plot with no axis labels or scale on either axis. Model once for the students, then have students help each other learn the calculator, then spend the rest of the hour helping out individual struggling students.

Better Option 2: Applet

Go to this applet.

Type in GPA for explanatory and ACT for response and add values.

Click “Begin Analysis”.

Click “Calculate least-squares regression line”.

Two clicks. Zero (memorized) keystrokes. And then I get a beautiful scatterplot and a residual plot with axis labels and scaled axes. Model once for the students, then allow them the rest of the hour to work through an activity that explores residual plots for several different data sets.

More time for thinking and reasoning about statistics

When using the graphing calculator to make residual plots, I spent half of the lesson making sure that all students learned the more than 30 keystrokes needed to make the graphs. The next day in class was spent reducing anxiety about calculator familiarity. Instead of wasting this valuable instructional time on calculators, now I can focus on the content. What makes this line the “best fit”? What does the residual plot tell us about the line of best fit? What would the residual plot look like for nonlinear data?

No more memorizing keystrokes.

Because applets were designed within the lifetime of our students (and not prior) the user interface is very intuitive. Johnny Student clicks through applets as quickly as he does through SnapChat or Twitter. This allows students to focus on the thinking and reasoning behind the statistics. Rather than memorizing keystrokes, students are pondering the meaning of a pattern that shows up in the residual plot.