Which of these is a coefficient estimated by a linear regression?

In the context of linear regression, the intercept is a fundamental coefficient that represents the expected value of the dependent variable when all independent variables are equal to zero. It serves as the starting point of the regression line on a graph, indicating where the line crosses the y-axis. The estimation of the intercept provides crucial insight into the relationship defined by the regression model.

The other options are not coefficients estimated during the regression process. Residuals represent the differences between the observed values and the predicted values but are not modeled coefficients. Squared error refers to the squared difference between the predicted and actual values, often used to assess the model’s performance but does not denote a parameter of the model itself. Correlation measures the strength and direction of a linear relationship between two variables, but it is not an estimated coefficient within regression. Therefore, the intercept is indeed the correct answer as it is directly estimated during the fitting of a linear regression model.