What is the formula for the line of best fit?

The formula for the line of best fit is represented by the linear equation y = mx + b. In this equation, 'm' denotes the slope of the line, indicating how much y changes for a unit change in x, while 'b' represents the y-intercept, the point where the line crosses the y-axis when x is zero.

This model is used extensively in statistics for linear regression analysis, where the goal is to find the best-fitting straight line through a set of data points, minimizing the distance (or the error) between the actual data points and the predicted values from the line. The simplicity of this equation makes it effective for representing relationships in data that can be approximated with linear functions.

The other options represent different types of equations. For instance, the quadratic equation y = ax^2 + bx + c captures the relationship of a parabola; the exponential model y = b(a^x) represents exponential growth or decay; and the inverse variation model y = k/x illustrates a hyperbolic relationship rather than a linear one. Each of these serves different purposes in modeling various types of real-world phenomena, but they do not depict a linear relationship in the same way that the line of best fit does.