Short-Term Decision-Making

Question: Regression analysis is similar to the scattergraph approach in that both fit a straight line to a set of data points to estimate fixed and variable costs. How does regression analysis differ from the scattergraph method for estimating costs?

Answer: Regression analysis uses a series of mathematical equations to find the best possible fit of the line to the data points and thus tends to provide more accurate results than the scattergraph approach. Rather than running these computations by hand, most companies use computer software, such as Excel, to perform regression analysis. Using the data for Bikes Unlimited shown back in Table 5.4 "Monthly Production Costs for Bikes Unlimited", regression analysis in Excel provides the following output. (This is a small excerpt of the output; see the appendix to this chapter for an explanation of how to use Excel to perform regression analysis.)

Thus the equation used to estimate total production costs for Bikes Unlimited looks like this:

Y = $43,276 + $53.42X

Now it is possible to estimate total production costs given a certain level of production (X). For example, if Bikes Unlimited expects to produce 6,000 units during August, total production costs are estimated to be $363,796:

$\begin{aligned} \mathrm{Y} &=\$ 43,276+(\$ 53.42 \times 6,000 \text { units }) \\ \mathrm{Y} &=\$ 43,276+\$ 320,520 \\ \mathrm{Y} &=\$ 363,796 \end{aligned}$

Regression analysis tends to yield the most accurate estimate of fixed and variable costs, assuming there are no unusual data points in the data set. It is important to review the data set first - perhaps in the form of a scattergraph - to confirm that no outliers exist.