How to Get a Simple Regression Analysis?

First, to identify the various cost drivers that might explain changes in overhead costs. In this article, direct labor hours, machine hours, and number of production setups are the three possible cost drivers.

Second, regression analysis needs actual data on selected variables for several periods. There are consecutive data of 12 months on overhead costs listed with three possible cost drivers in Table 1.

Third, use excel or the other system to do the separate regression analysis for each possible cost driver. R Square is the critical factor, bigger is better, to be considered.

Fourth, a bigger R Square number shows the closer relationship between independent variables and dependent variables. So the bigger number will be chosen as the related cost drivers.

To select the proper base, regression analysis is one of quantitative techniques. It helps a) determining and analyzing the extent of the relationship between overhead costs and various cost drivers and b) estimating the linear of curvilinear relationship between overhead costs and cost drivers. Among regression analysis, R square, T value, and P value are the factors that should be put into consider.

For example, if using excel as the system to analyze regression, first thing is to input all data into excel sheet. Then we open "Data" function page, select "Regression" item. Choose all numbers of "Overhead Cost" as Y-range, Choose one column data of the three possible cost drivers as X-range. After click OK button, we get the regression analysis data showing in the place we selected. We should analyze R Square, T value, P value to determine which possible cost driver is related to Overhead Cost.