The high-low method is a technique used to analyze and estimate the fixed and variable components of a mixed cost. Mixed costs contain both a fixed portion, which remains constant regardless of the level of activity, and a variable portion, which changes in proportion to the level of activity.

The high-low method involves selecting two data points: one with the highest level of activity (high point) and one with the lowest level of activity (low point). These data points consist of both the level of activity and the total cost associated with that activity level.

Here are the steps to apply the high-low method:

**Select the High and Low Data Points:**Identify the data point with the highest level of activity and its corresponding total cost. Similarly, identify the data point with the lowest level of activity and its corresponding total cost.**Calculate the Variable Cost Component:**Determine the change in total cost between the high and low points of activity. Then, calculate the change in activity levels. Divide the change in total cost by the change in activity to obtain the variable cost per unit of activity.**Calculate the Fixed Cost Component:**With the variable cost per unit of activity known, determine the total fixed cost by subtracting the variable cost component from either the high or low data point. This represents the fixed portion of the mixed cost.

Once the fixed and variable components are determined using the high-low method, the cost formula for the mixed cost can be expressed as:

**Total Cost = Fixed Cost + (Variable Cost per Unit of Activity × Activity Level)**

It’s important to note that the high-low method provides an estimate of the fixed and variable cost components based on the selected high and low data points. While it can be a simple and quick method, it assumes a linear relationship between the cost and activity level, which may not always be accurate.

Other more sophisticated methods, such as regression analysis or least squares regression, can provide more precise estimates by considering a larger set of data points and accounting for potential non-linear relationships between cost and activity. These methods are commonly used when a more robust analysis of mixed costs is required.