How to Find ŷ from a Regression on TI89
with a Note on Finding Residuals
Copyright © 2001–2024 by Stan Brown, BrownMath.com
Copyright © 2001–2024 by Stan Brown, BrownMath.com
Summary: The regression line represents the model that best fits the data. One important reason for doing the regression in the first place is to answer the question, what average y value does the model predict for a given x? This page shows you two methods of answering that question.
You can make predictions while examining the graph of the regression line on the TI89.
Advantages to this method: aside from being pretty cool, it avoids rounding errors, and it’s very fast for multiple predictions.
Display the graph, if it’s not already on screen.  [◆ ] [GRAPH ] 
Trace the regression line, not the data points.  [F3 ] brings up the trace cursor.
But note the P1 in the upper right corner of
the screen. That tells you that you’re tracing the data
points and not the regression line.
Press [ ▼ ] until the upper right displays
1 rather than P1 . 
The current x and y coordinates are displayed at the bottom
of the screen. You can type your desired x, and the calculator will
figure the corresponding ŷ.

Press the whiteongray numeric keys including
[() ] and decimal point if needed. The x coordinate
changes to match your typing.
After entering your number, press [ ENTER ]. The
calculator moves the cursor and displays the corresponding
ŷ value. 
Caution: ŷ = 267.1 yd is the predicted or expected average distance for a clubhead speed of 102 mph. But that does not mean any particular golf ball hit at that speed will travel that exact distance. You can think of ŷ as the average travel distance that we would expect for a whole lot of golf balls hit at that speed.
Caution:
A regression equation is valid only within the range of actual measured x values,
and a little way left
and right of that range. If you try to go too far outside the valid
range, the calculator will display ERR:INVALID
.
But what if you don’t still have the regression line on your calculator, for instance if you’ve done a different regression? In that case, you can go back to your writtendown regression equation and plug in the desired x value.
Advantage of this method: You already know how to substitute into equations. Disadvantages: depending on the specific numbers involved, you may introduce rounding errors. Also, since you’re entering more numbers there’s an increased chance of entering a number wrong.
Example To find the predicted average y value for x = 102, go back to the regression equation that you wrote down, and substitute 102 for x:
ŷ = 3.1661x − 55.7966
ŷ = 3.1661*102 − 55.7966
ŷ = 267.1456 → 267.1
In this example, the rounding error was very small, and it disappeared when you rounded ŷ to one decimal place. But there will be problems where the rounding error is large enough to affect the final answer, so always use the trace method if you can.
Again, please observe the Cautions above. With this method, the calculator won’t tell you when your x value is outside a reasonable range, so you need to be aware of that issue yourself.
Each measured data point has an associated residual, defined as y−ŷ, the distance of the point above or below the line. To find a residual, the actual y comes from the original data, and the predicted ŷ comes from one of the methods above.
Example: Find the residual for x = 102.
Solution: From the original data, y = 264. From
either of the methods above, ŷ = 267.1. Therefore
the residual is y−ŷ = 264−267.1 =
−3.1 yards.
If a given x value occurs in more than one data point, you have multiple residuals for that x value.
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