How To Find Residuals On Ti 84

Hunting for Leftovers: A TI-84 Adventure

Ever feel like life leaves you with crumbs? Like after a delicious cookie, you're left with… well, crumbs. Your TI-84 calculator can actually help you find something similar, statistically speaking.

We're talking about residuals. Don't worry, it's not about dusting your kitchen. Think of it as finding the "leftovers" after your calculator makes a prediction.

Step 1: Feed the Beast Data

First, you need to give your TI-84 something to chew on. Enter your data into lists. Imagine you're feeding squirrels – each number is a tiny nut.

Go to STAT, then EDIT. List 1 gets your X-values, and List 2 gets your Y-values. Hopefully, you don't have more data than your squirrel brain can handle.

Step 2: Making a Prediction

Now, tell the calculator to draw a line through your data scatterplot. Press STAT again, then CALC. Choose your favorite regression – maybe LinReg(ax+b) because lines are cool.

Tell it where your squirrels are hiding, using List 1, List 2, and Y1. This part is crucial so, double-check you’ve got it right. Press ENTER, and POOF! A line appears.

Step 3: The Great Residual Reveal

The magic happens when your TI-84 stores those leftovers. After the LinReg, your calculator secretly calculated the residuals.

These "leftovers" are actually the difference between what the line predicted and what you actually observed. They’re lurking in a list called RESID. How sneaky!

Step 4: Summoning the Residuals

To see these hidden residuals, go back to STAT, then EDIT. Check List 3. Is it full of numbers? Congrats, you've found the leftovers!

If it’s empty, go back to your LinReg and make sure you told it to store the regression equation into Y1. The calculator needs that equation to calculate what's “left over.”

Step 5: Plotting the Leftovers

Feeling adventurous? Plot these residuals. A scatterplot of residuals is a powerful way to see if the original line was a good fit.

Go to 2nd, Y= (STAT PLOT). Turn Plot1 ON. Choose the scatterplot icon, set Xlist to List 1 (or whatever your x-values are), and Ylist to RESID. Prepare to be amazed (or slightly underwhelmed!).

What Do the Leftovers Mean?

If the residuals look randomly scattered above and below zero, your line probably fit well. Good job, you're a data whisperer!

But if you see a pattern – like a curve – it means a line might not be the best way to describe your data. Time to explore other options, like a quadratic or exponential function!

Embrace the Crumbs

So, the next time you're staring at a data set, remember the TI-84 adventure. Residuals aren't just some dusty mathematical concept.

They're the little clues that tell you how well your model fits the real world. They're the delicious crumbs that lead to better understanding. Happy hunting!