Which algorithm is used to find the best-fit line for a scatterplot?

The best-fit line for a scatterplot is determined through a process that minimizes the distance between the data points and the line itself. The least-squares regression algorithm is specifically designed for this purpose. It calculates the line that minimizes the sum of the squares of the vertical distances (residuals) between the observed values and the values predicted by the linear equation.

While linear regression is the overarching method that describes fitting a line to data, it is the least-squares principle that is commonly utilized in this context. Therefore, the least-squares regression algorithm represents the specific technique used to identify the best-fitting line by ensuring that these distances are minimized as efficiently as possible. This is key to producing a line that accurately represents the trend in the data.

Other choices like polynomial and exponential regression involve fitting different types of curves to data rather than a straight line, making them unsuitable for finding the best-fit line specifically within the context typically defined by linear relationships in scatterplots.