Residual Plot vs Histogram
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.
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.
Histogram
Graphs and displays
A histogram displays quantitative data by grouping values into equal-width intervals and drawing a bar for the count in each interval.
A histogram divides the number line into bins of equal width and draws a bar whose height is the frequency in that bin. For example, exam scores might be binned as 60 to 70, 70 to 80, and 80 to 90, with bar heights giving how many scores land in each. Unlike a bar graph, the bars touch because the horizontal axis is a continuous number scale. The bin width you choose affects how smooth or jagged the shape looks.