The amount of fixed and variable costs are estimated by every business organization. The high-low and least-square methods determine the fixed and variable costs from the mixed costs. The high-low method requires only small amounts of data to separate a mixed cost into fixed and variable costs. This method only considers the highest and lowest activity levels to make comparisons. On the other hand, the least-square method, also known as the regression analysis method, separates cost, which helps in dictating the relationship between two or more variables. Separating mixed expenses is a vital task that the company’s management must complete. Different strategies for segregating the blended cost may be used by company management. The total cost of the mixed cost must be separated into fixed and variable costs. Business managers must understand mixed costs regarding how much fixed and variable expenditures are incorporated. It is an essential factor for business managers to develop business planning and budgets.
High Low Method
Using the high-level and low-level activities, a formula is built to separate the combined expenses into discrete components. The variable component of the mixed cost is computed by dividing the difference between the highest and lowest activity costs by the difference between the highest and lowest activity units. The fixed component of the mixed cost is derived by multiplying the variable cost per unit by the highest activity units and subtracting the value from the highest activity cost.
Simplicity – The first advantage of the high-low technique is the ease of separating mixed expenses. Due to the simplicity of this method, business managers require relatively little information to separate the blended cost into fixed and variable components.
Variable and fixed cost constant -The linear relationship assumption is a drawback of the high-low technique. The high-low technique has the problem of assuming that the fixed and variable components of the mixed cost are constant. It contributes to the method’s simplicity.
Maybe unreliable – The biggest downside of this method is its unreliability. When a company organization employs this method the findings or consequences may be untrustworthy. This is because the company organization cannot rely solely on two extreme activity levels.
Least Square Method
The least-square approach is a regression analysis method. It is used to identify the best fit line for a data set using a visual representation of the relationship between the data points. It fetches the link between a known independent variable and an unknown dependent variable. As a result, this method is utilized to forecast the dependent variables. There are two variations of the least-square approach used in regression analysis.
Linear variant or simple linear variant – This is the most popular variation, and it is based on several assumptions. These assumptions are that the dependent and independent variables must have a linear relationship, that the independent variable must be carefully chosen (not chosen at random), that the residual value could be zero or constant, which follows the normal distribution. One of the most frequent least-squares approaches is the basic linear. The cost accountants use this approach to evaluate the connection between a dependent variable and an independent variable.
Multi-linear regression variant – Multiple linear regression analysis is not the most often utilized technique. It is only utilized under challenging conditions. It is associated with fundamental linear analysis. The sole distinction is that this analysis employs numerous independent variables rather than a single independent variable.
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