Where u is an error/noise term, and in this case takes a normal distribution. The error term is meant to Hoover up all the stuff you can't account for because it's impossible to measure everything. As ever, what you're trying to estimate is the parameters for everything but the error term.
He's maybe being a little sloppy with the notation, but TBH it's a pedantic distinction that I haven't seen anyone make a big deal of since I last took intro stats. In R, for example, you'd specify the model as "y ~ log(x)" and leave it at that.
He's maybe being a little sloppy with the notation, but TBH it's a pedantic distinction that I haven't seen anyone make a big deal of since I last took intro stats. In R, for example, you'd specify the model as "y ~ log(x)" and leave it at that.