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A regression line is a statistical tool used to model the relationship between two variables by fitting a linear equation to observed data. The equation typically takes the form y = mx + b, where m represents the slope and b the y-intercept. This line helps predict the value of the dependent variable based on the independent variable, making it essential for understanding trends in data.

Outliers are data points that differ significantly from other observations in a dataset. They can skew the results of statistical analyses, including regression, by affecting the slope and intercept of the regression line. Identifying and understanding outliers is crucial, as they can indicate variability in the data or errors in measurement.

A scatterplot is a graphical representation of two variables, displaying points that correspond to the values of each variable. It helps visualize the relationship between the variables, allowing for the identification of patterns, trends, and potential outliers. Analyzing scatterplots is a fundamental step in regression analysis, as it provides insight into the data's distribution and correlation.