Scatter Plot, Correlation, and Regression on TI-83/84

See also: a separate version of these instructions for the TI-89

Step 0. Setup

The calculator will remember these settings when you turn it off: next time you can start with Step 1.

Step 1. Make the Scatter Plot

Before you even run a regression, you should first plot the points and see whether they seem to lie along a straight line. If the distribution is obviously not a straight line, don’t do a linear regression. (Some other form of regression might still be appropriate, but that is outside the scope of this course.)

Let’s use this example from Sullivan, Michael, Fundamentals of Statistics 3/e (Pearson Prentice Hall, 2011), page 179: the distance a golf ball travels versus the speed with which the club head hit it.

Turn off other plots.	[Y=] Cursor to each highlighted = sign or Plot number and press [ENTER] to deactivate.
Enter the numbers in two statistics lists.	[STAT] [1] selects the list-edit screen.   Cursor onto the label L1 at top of first column, then [CLEAR] [ENTER] erases the list. Enter the x values.   Cursor onto the label L2 at top of second column, then [CLEAR] [ENTER] erases the list. Enter the y values.
Set up the scatter plot.	[2nd Y= makes STAT PLOT] [1] [ENTER] turns Plot 1 on.
	[▼] [ENTER] selects scatter plot.
	[▼] [2nd 1 makes L1] ties list 1 to the x axis.
	[▼] [2nd 2 makes L2] ties list 2 to the y axis.
Plot the points.	[ZOOM] [9] automatically adjusts the window frame to fit the data, but does not adjust the grid spacing.   (optional) The grid (if one appears) isn’t really doing you any good, and you may get horizontal or vertical lines that hide the data. To turn off the grid, press [2nd ZOOM makes FORMAT] [▼] [▼] [ENTER] [GRAPH].   If you prefer to keep the grid and adjust its spacing, press [WINDOW], set Xscl=1 and Yscl=5, then press [GRAPH] to redisplay the graph. (Appropriate values of Xscl and Yscl may be different for other problems. Pick the values that make the graph look best to you.)
Check your data entry by tracing the points.	[TRACE] shows you the first (x,y) pair, and then [►] shows you the others. They’re shown in the order you entered them, not necessarily from left to right.

If the data points don’t seem to follow a straight line reasonably well, STOP! Your calculator will obey you if you tell it to perform a linear regression, but if the points don’t actually fit a straight line then it’s a case of “garbage in, garbage out.”

For instance, consider this example from De Veaux, Velleman, and Bock, Intro Stats (Pearson Addison Wesley, 2009), page 179. This is a table of recommended f/stops for various shutter speeds for a digital camera:

If you try plotting these numbers yourself, enter the shutter speeds as fractions for accuracy: don’t convert them to decimals yourself. The calculator will show you only a few decimal places, but it maintains much greater precision internally.

You can see from the plot at right that these data don’t fit a straight line. There is a distinct bend near the left. When you have anything with a curve or bend, linear regression is wrong. You can try other forms of regression in your calculator’s menu, or you can transform the data as described in De Veaux, Velleman, and Bock, Intro Stats (Pearson Addison Wesley, 2009), Chapter 10, and other textbooks.

Step 2. Perform the Regression

Write down a (slope), b (y intercept), r (correlation coefficient). (I follow your book’s conventions on rounding.)

a = 3.1661, b = −55.7966

R² = 0.88, r = 0.94

Show your work! Write LinReg(ax+b) plus the two lists and the y-variable that you’re using. Just “LinReg” isn’t enough.

Correlation Coefficient, r

“Several sets of (x,y) [pairs], with the correlation coefficient for each set. Note that correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom).”
source: Wikipedia article (accessed 2011-01-18)

Look first at r, the coefficient of linear correlation. r can range from −1 to +1 and measures the strength of the association between x and y. A positive correlation or positive association means that y tends to increase as x increases, and a negative correlation or negative association means that y tends to decrease as x increases. The closer r is to 1 or −1, the stronger the association. We usually round r to two decimal places.

For real-world data, the 0.94 that we got is a pretty strong correlation. But you might wonder whether there’s actually an association between club-head speed and distance traveled, as opposed to just an apparent correlation in this sample. The Web page Decision Points for Correlation Coefficient shows you how to answer that question.

Be careful in your interpretation! No matter how strong your r might be, say that changes in the y variable are associated with changes in the x variable, not “caused by” it. Correlation is not causation is your mantra.

It’s easy to think of associations where there is no cause. For example, if you scatter-plot US cities with x as number of books in the public library and y as number of murders, you’ll see a positive association: number of murders tends to be higher in cities with more library books. Does that mean that reading causes people to commit murder, or that murderers read more than other people? Of course not! There is a lurking variable here: population of the city.

When you have a positive or negative association, there are four possibilities: x might cause changes in y, y might cause changes in x, lurking variables might cause changes in both, or it could just be coincidence, a random sample that happens to show a strong association even though the population does not.

Regression Line, ŷ = ax+b

Write the equation of the line using ŷ, not y, to indicate that this is a prediction. b is the y intercept, and a is the slope. Your book rounds both of them to four decimal places, so you would write the equation of the line as

ŷ = 3.1661x − 55.7966

(Don’t write 3.1661x + −55.7966.)

These numbers can be interpreted pretty easily. Business majors will recognize them as intercept = fixed cost and slope = variable cost, but you can interpret them in non-business contexts just as well.

The slope, a, tells how much ŷ changes for a one-unit change in x. In this case, your interpretation is “the ball travels about an extra 3.17 yards when the club speed is 1 mph greater.” The sign of a is always the same as the sign of r. (A negative slope would mean that y decreases that many units for every one unit increase in x.)

The intercept, b, says where the regression line crosses the y axis: it’s the value of ŷ when x is 0. Be careful! The y intercept may or may not be meaningful. In this case, a club-head speed of zero is not meaningful. In general, when the measured x values don’t include 0 or don’t at least come pretty close to it, you can’t assign a real-world interpretation to the intercept. In this case you’d say something like “the intercept of −55.7966 has no physical interpretation because a club-head speed of zero is meaningless for striking a golf ball.”

Here’s an example where the y intercept does have a physical meaning. Suppose you measure the gross weight of a UPS truck (y) with various numbers of packages (x) in it, and you get the regression equation ŷ = 2.17x+2463. The slope, 2.17, is the average weight per package, and the y intercept, 2463, is the weight of the empty truck.

Coefficient of Determination, R²

The last number we look at (third on the screen) is R², the coefficient of determination. (The calculator displays r², but the capital letter is standard notation.) R² measures the quality of the regression line as a means of predicting ŷ from x: the closer R² is to 1, the better the line. Another way to look at it is that R² measures how much of the total variation in y is predicted by the line.

In this case R² is about 0.88, so your interpretation is “about 88% of the variation in distance traveled is associated with variation in club-head speed.” Statisticians say that R² tells you how much of the variation in y is “explained” by variation in x, but if you use that word remember that it means a numerical association, not necessarily a cause-and-effect explanation. It’s best to stick with “associated” unless you have done an experiment to show that there is cause and effect.

There’s a subtle difference between r and R², so keep your interpretations straight. r talks about the strength of the association between the variables; R² talks about what part of the variation in the y variable is associated with variation in the x variable. Your interpretation of R² should not use any form of the word “correlated”.

Only linear regression will have a correlation coefficient r, but any type of regression will have a coefficient of determination R² that tells you how well the regression equation predicts y from the independent variable(s).

Step 3. Display the Regression Line

Show line with original data points.

[GRAPH]

What is this line, exactly? It’s the one unique line that fits the plotted points best. But what does “best” mean?

The same four points on left and right. The vertical distance from each measured data point to the line, y−ŷ, is called the residual for that x value. The line on the right is better because the residuals are smaller.
source: Dabes & Janik’s Statistics Manual (1999)

For each plotted point, there is a residual equal to y−ŷ, the difference between the actual measured y for that x and the value predicted by the line. Residuals are positive if the data point is above the line, or negative if the data point is below the line.

You can think of the residuals as measures of how bad the line is at prediction, so you want them small. For any possible line, there’s a “total badness” equal to taking all the residuals, squaring them, and adding them up. The least squares regression line means the line that is best because it has less of this “total badness” than any other possible line. Obviously you’re not going to try different lines and make those calculations, because the formulas built into your calculator guarantee that there’s one best line and this is it.

See also: Once you have the regression line, you can use the calculator to predict the y value for any x in the model.

See also: Do you wonder what sort of calculations the calculator does to find the best line? Least Squares — the Gory Details derives the formulas for its slope and y intercept. Traditionally this is a calculus topic, but all that’s really necessary is some algebra.

optional appendix. Display the Residuals

I would like you to know the material in this section, but it's not part of the MATH200 syllabus so I don’t require it. No homework or quiz problems will draw from this section. You will, however, need to calculate individual residuals; see the last section of Finding ŷ from a Regression on TI-83/84.

“No regression analysis is complete without a display of the residuals to check that the linear model is reasonable.”

De Veaux, Velleman, and Bock, Intro Stats (Pearson Addison Wesley, 2009), page 227

The residuals are automatically calculated during the regression. All you have to do is plot them on the y axis against your existing x data. This is an important final check on your model of the straight-line relationship.

Turn off other plots.	Press [Y=]. Cursor to the highlighted = sign next to Y1 and press [ENTER]. Cursor to PLOT1 and press [ENTER].
Set up the plot of residuals against the x data.	Set up Plot 2 for the residuals. Press [2nd Y= makes STAT PLOT] [▼] [ENTER] [ENTER] to turn on Plot 2. Press [▼] [ENTER] to select a scatter plot.   The x’s are still in L1, so press [2nd 1 makes L1] [ENTER]. In this plot, the y’s will be the residuals: press [2nd STAT makes LIST], cursor up to RESID, and press [ENTER] [ENTER].
Display the plot.	[ZOOM] [9] displays the plot.

You want the plot of residuals versus x to be “the most boring scatterplot you’ve ever seen”, in De Veaux’s words (page 203). “It shouldn’t have any interesting features, like a direction or shape. It should stretch horizontally, with about the same amount of scatter throughout. It should show no bends, and it should have no outliers. If you see any of these features, find out what the regression model missed.”

Don’t worry about the size of the residuals, because [ZOOM] [9] adjusts the vertical scale so that they take up the full screen.

If the residuals are more or less evenly distributed above and below the axis and show no particular trend, you were probably right to choose linear regression. But if there is a trend, you have probably forced a linear regression on non-linear data. If your data points looked like they fit a straight line but the residuals show a trend, it probably means that you took data along a small part of a curve.

Here there is no bend and there are no outliers. The scatter is pretty consistent from left to right, so you conclude that distance traveled versus club-head speed really does fit the straight-line model.

Residual Plot Showing Problems

Refer back to the scatter plot of f/stop against shutter speed. I said then that it was not a straight line, so you could not do a linear regression. If you missed the bend in the scatterplot and did a regression anyway, you’d get a correlation coefficient of r = 0.98, which would encourage you to rely on the bad regression. But plotting the residuals (at right) makes it crystal clear that linear regression is the wrong type for this data set.

This is a textbook case (which is why it was in a textbook): there’s a clear curve with a bend, variation on both sides of the x axis is not consistent, and there’s even a likely outlier.

advanced: Residuals and R²

I said in Step 2 that the coefficient of determination measures the variation in the measured y associated with the measured x. Now that we have the residuals, we can make that statement more precise and perhaps a little easier to understand.

The set of measured y values has a spread, which can be measured by the standard deviation or the variance. It turns out to be useful to consider the variation in y’s as their variance. (You remember that the variance is the square of the standard deviation.)

The total variance of the measured y’s has two components: the so-called “explained” variation, which is the variation along the regression line, and the “unexplained” variation, which is the variation away from the regression line. The “explained” variation is simply the variance of the ŷ’s, computing ŷ for every x, and the “unexplained” variation is the variance of the residuals. Those two must add up to the total variance of the measured y’s, which means that if we express them as percentages of the variation in y then the percentages must add to 100%. So R² is the percent of “explained” variation in the regression, and 100%−R² is the percent of “unexplained” variation.

and

Now we can restate what we learned in Step 2. R² is 88% because 88% of the variance in y is associated with the regression line, and the other 12% must therefore be the variance in the residuals. This isn’t hard to verify: do a 1-VarStats on the list of measured y’s and square the standard deviation to get the total variance in y, s²_y = 59.93. Then do 1-VarStats on the residuals list and square the standard deviation to get the “unexplained” variance, s²_e = 7.12. The ratio of those is 7.12/59.93 = 0.12, which is 1−R². Expressing it as a percentage gives 100%−R² = 12% so 12% of the variation in measured y’s is “unexplained” (due to lurking variables, measurement error, etc.).

What’s New

• 24 Sep 2011: Suggest turning off the grid for a plot rather than fussing with the spacing.
• (intervening changes suppressed)
• Jun 16, 2002: new document