Circulation: Use Regression Analysis to Optimize Catalog Circ and Sales Data
We want to know if sales increases on the East Coast match increases on the West Coast. Before we can justify spending time and money to discover if differences are due to things such as demographics or customers’ personal income distribution, we first must discern if there’s a statistical difference in sales increases. Gather the data from the coupons returned from the two localities and chart them with their corresponding sales increases. The more data points gathered, the better regression analysis works. Use the graphing capabilities of your spreadsheet software to create XY scatter diagrams to give a better visual illustration.
When graphing East Coast sales increases: If a diagonal line greater than a 30 degree slope can be drawn with the same number of points above and below it, and equidistant from the line (with the points still being fairly close to the line), this indicates there’s a good correlation between the two variables.
But, if on the West Coast chart you find it would be more difficult to force a line through the points while being close to any of them, your discount offer may not be boosting sales as you suspected.
Next, highlight the appropriate columns in the number charts and run the linear regression program according to the routine used by your spreadsheet program. (Using my favorite spreadsheet, this process takes only four or so mouseclicks.) Analyze the data that’s generated.
¥ The regression R-squared tells you how well your two variables matched -- a simple cause and effect. R-squared ranges from 0.0 to 1.0, and a value of more than 0.7 is considered a satisfactory correlation.
¥ The X-coefficient tells you how much the dependent variable changes for each unit increase in the independent variable. This number also is used to predict future results when used with the predictor formula and added to the constant.