Scatter plots are often the first honest look at whether two variables appear connected. A TI-84 style workflow helps students move from raw paired values into a visual pattern that can guide later modeling choices.
Plot before modeling
A scatter plot helps you see whether a line, curve, or no clear relationship is the better description. Skipping the plot often leads to weak model choices.
Check list pairing carefully
Scatter plots depend on matching x-values to the correct y-values. Misalignment creates nonsense patterns that can still look suspiciously real.
Read the pattern before pressing more keys
Direction, strength, and shape often become visible quickly. That observation improves the quality of later regression decisions.
Key takeaways
- Scatter plots should usually come before regression.
- Paired-list accuracy matters.
- Visual pattern reading helps choose the next analysis step.
Independent note
This guide explains an independent TI-84 style practice workflow and is not official device documentation.