How to Find the Correlation Coefficient on a TI-84 Calculator

There are multiple methods to calculate the Correlation Coefficient. The TI-84 has a built-in function called LinReg(ax+b) to calculate it. So, we’ll discuss that method to easily find it without doing any extra steps.

Let’s break down this method into 3 easy steps:

Step 1: Turn On Diagnostics

Before your TI-84 will show r, you have to turn diagnostics ON.

  1. Press [2nd], then [0] to open the Catalog
  2. Scroll down to DiagnosticOn
  3. Press [ENTER], then [ENTER] again
  4. You’ll see Done on the screen

This only needs to be done once, unless your calculator is reset.

Step 2: Enter Data into Lists

  1. Press [STAT], then choose 1: Edit
  2. Enter your x-values (independent variable) into L1
  3. Enter your y-values (dependent variable) into L2
  4. Make sure each x-value has a matching y-value

Example:
L1 = {2, 4, 6, 8, 10}
L2 = {1, 3, 5, 7, 9}

Step 3: Run the Linear Regression

  1. Press [STAT], then scroll to CALC using the right arrow
  2. Choose 4: LinReg(ax+b)
  3. You should see:
    LinReg(ax+b) L1, L2
    If not, enter it manually:
    LinReg(ax+b) L1, L2
    (Use [2nd] + 1 for L1, [2nd] + 2 for L2)
  4. Press [ENTER] to calculate

The calculator will show:

  • a (slope)
  • b (y-intercept)
  • r (correlation coefficient)
  • (coefficient of determination)

Try calculating correlation coefficients yourself using our online TI-84 Plus calculator with built-in stats functions. Apply the above method and practice it.

Correlation Coefficient (r):

A number between -1 and 1 that measures the strength and direction of a linear relationship between two variables.

  • +1 = perfect positive correlation
  • -1 = perfect negative correlation
  • 0 = no linear correlation

Coefficient of Determination (r²):

The square of the correlation coefficient (r²). It tells you the percentage of variation in one variable that can be explained by its relationship with the other variable.

  • Ranges from 0 to 1
  • Example: r² = 0.85 means 85% of the variation is explained by the model.

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