Scatterplot vs Correlation Coefficient
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Scatterplot
Graphs and displays
A scatterplot graphs paired values of two quantitative variables as points, showing the direction, form, and strength of their relationship.
A scatterplot plots each individual as a point, with the explanatory variable on the horizontal axis and the response variable on the vertical axis. You read it for direction (positive or negative), form (linear or curved), strength (tight or loose), and any outliers. For example, plotting hours studied against exam score for 30 students may show an upward, roughly linear cloud of points. When the pattern looks linear, a least-squares regression line can summarize it.
Correlation coefficient
Regression and correlation
The correlation coefficient r measures the direction and strength of a linear relationship between two quantitative variables, always between -1 and 1.
The correlation coefficient captures how tightly points cluster around a straight line and which way they slope. It runs from (perfect negative) through (no linear association) to (perfect positive), so values near mean a weak linear fit. For example, signals a strong positive linear trend, while signals a weak negative one. Because only measures linear association, a strong curved pattern can still give an near 0, and never establishes causation.