Scatterplot vs Residual Plot
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.
Residual plot
Regression and correlation
A residual plot graphs the residuals against the explanatory variable or predicted values, used to check whether a line fits the data well.
A residual plot helps you judge whether a linear model is appropriate by spreading the leftover errors out for inspection. Random scatter with no pattern around the horizontal zero line suggests a line fits well, while a curved or fanning pattern warns that it does not. For example, a U-shaped residual plot signals the true relationship is curved, so a line understates it at the ends. You read residual plots for both curvature and changing spread before trusting a regression.