This plot shows how salary depends on developer role or type, but specific technologies also
affect salary. Staying current with high-demand technologies can have a big impact
on salary. For example, this year, we see a significant increase in salary for developers
who tell us they use React.js or AWS in their professional lives. The flip side here is
that some technologies are used so broadly that they do not increase or decrease salary
significantly. For example, so many developers use JavaScript that identifying yourself
as a JavaScript developer isn’t a significant predictor of salary up or down.
Building a model
The kind of model we built to predict salaries for the calculator uses multiple linear regression.
The salaries were modeled on a log scale, because we found the salary distributions were log-normal
(as such distributions often are) with a long tail of very high salaries.
In this salary calculator, we report a predicted salary for the location, education, experience,
and other information you enter. We also report a 50% prediction interval. The specific statistical
meaning of this interval is that we expect 50% of people with the same characteristics as you to
have salaries within that range; it spans the 25th to 75th percentiles. The interval is just as
important as the prediction itself (the 50th percentile), because it gives you an understanding
of what the range of expected salaries could be.
Another characteristic of the model that can help you understand its predictions are the model’s
residuals, the differences between the predicted salaries and the known salaries for the dataset
we used for modeling. Let’s look at these for the countries and individual cities that are supported
in this initial release of our calculator.