I'm trying to move away from "gut feelings" in my project planning. Can I use multiple linear regression to predict how long a software module will take to build based on variables like team size, lines of code, and historical bug rates? How many independent variables are too many before the model becomes unreliable? I want to use business statistics to improve our Agile velocity.
3 answers
Multiple linear regression is excellent for this, but watch out for "Overfitting." If you add too many variables—like what the team ate for lunch—the model will fit your past data perfectly but fail to predict the future. Stick to 3-5 high-impact independent variables. Also, check for "Multicollinearity." For example, if 'lines of code' and 'number of files' are perfectly correlated, you shouldn't use both. Use the $R^2$ value to see how much of the variance in time your model actually explains. It’s a game-changer for sprint planning!
How are you handling the qualitative factors, like "team experience level"? Do you plan to use dummy variables to turn those categories into numbers for your regression analysis?
It’s much better than guessing! Just make sure your data is clean. If your historical bug rates were tracked inconsistently, the whole model will be "garbage in, garbage out."
Linda is right. Data cleaning is 80% of the work in business statistics. If the inputs are flawed, the regression coefficients won't mean a thing for your Agile team.
Kevin, dummy variables are the way to go for things like seniority. You can assign '1' for senior teams and '0' for junior ones. This allows the regression model to calculate a specific "coefficient" for experience. Just remember that regression assumes a linear relationship; if a team of 10 isn't twice as fast as a team of 5, you might need a more complex non-linear model.