How you can predict compensation when you know something about a lawyer, and have lots of similar data from other lawyers
A scatter plot of data, for instance total compensation of a group of lawyers against how many years they have been practicing law, may look like a Milky Way galaxy of points, but much can be learned from the correlation. Even more can be learned if we also know such facts as the industry of each lawyer and the revenue of their company and their LSAT scores – any facts that might influence income.
We can use linear regression, a statistical calculation to understand and quantify the relationship between any and all of those “independent variables” and the “dependent variable” – total compensation. Spreadsheet programs can place a straight line within that cloud of dots such that the total distance between each of the dots and that line is the minimum.
A fascinating outcome of that calculation is the formula for the line. From it you can predict a lawyer’s total compensation if you are given any of those independent variables for the lawyer. This kind of linear regression for compensation data is what General Counsel Metrics produces as part of its benchmarking reports.
Interested in your pay? Participate, absolutely free, in the GC Metrics law department benchmark survey and get Release 3.0 later this month by providing six of your figures on staffing and spending by clicking on this secure survey link.