Forecasting with Excel: Regression Analysis Can Help Predict Revenues and Costs
[ILLUSTRATION OMITTED] Your client, Dave's BBQ, a local independent restaurant, is interested in determining the effect on sales revenue of certain advertising strategies. Dave has weekly data on advertising dollars spent as well as sales revenue from the restaurant and has come to you, his CPA, to help him determine the link between the two. It certainly seems reasonable that advertising, at least in part, drives sales revenue, but you need to measure the strength of that relationship and then use the information to help forecast sales revenue for future periods. Creating financial forecasts from historical data can be daunting for managers. However, several tools in Excel are readily accessible and easy to use to help with forecasting revenues and costs. The use of regression analysis can significantly enhance the accuracy of your financial forecasts and make the budgeting process more efficient. This article guides you through the process of using one of these tools, linear regression. The purpose of this article is to demonstrate how to use Excel for forecasting--not to teach forecasting or regression analysis. The discussion below assumes a basic understanding of linear regression (for more details, see box, "Further Reading.") The screenshots demonstrate the tools in Excel 2003. The tools function much the same way in Excel 2007, but accessing them is slightly different. See the sidebar "Accessing Regression Tools in Excel 2007" for directions. To follow the analysis of Dave's BBQ, download the illustration file with the online version of this article at www.journalofaccountancy.com; enter 2008884 in the search box. PLOT THE DOTS Linear regression is a statistical tool that can help determine whether the link between a measure of activity (x) and an output measure, such as cost or revenue (y), is sufficient to be used to forecast that output measure. The x is generally referred to as the independent variable and the y as the dependent variable. In the case of Dave's BBQ, we analyze weekly sales revenue (the dependent variable) versus advertising dollars (the measure of activity or independent variable). Scanning the data in Exhibit 1, it appears there is a positive correlation between advertising dollars and weekly sales revenue. In other words, higher levels of advertising seem to be associated with greater sales revenue. To confirm this assessment and generate forecasts, we will use a graphical approach to regression analysis. Begin by highlighting cells B1:C21 where the advertising costs and sales revenue data exist. With those cells highlighted, start Excel's Chart Wizard by selecting Insert, Chart from the menu bar as illustrated in Exhibit 2. In Step 1 of the Chart Wizard select XY (Scatter) from the Standard Types list as shown in Exhibit 3, then select Next. In Step 2 of the Chart Wizard you should see a thumbnail of the chart being created (see Exhibit 4). It should be noted that Excel assumes the first column of data selected represents the independent variable (x), while the second column of data represents the dependent variable (y); in this case x and y represent advertising and sales revenue, respectively. [ILLUSTRATIONS OMITTED] Select Next again to move on to Step 3 of the Chart Wizard shown in Exhibit 5. Here you can specify the Chart Options including title, labeling the axes, and other aesthetic elements. After completing those tasks as desired, select Next. In Step 4 of the Chart Wizard, select the location of the chart. I normally leave the default to include the chart as an object in the current worksheet tab and select Finish as illustrated in Exhibit 6. Your worksheet should now look similar to Exhibit 7 with the completed chart and data all in a single worksheet. [ILLUSTRATIONS OMITTED] Visually we can confirm our initial assessment of a connection between advertising and sales revenue. More advertising leads to more sales revenue. …