I am taking an online course on regression modeling and came across the following (which is actually taken from a Minitab blog):
R-squared as a Biased Estimate R-squared measures the strength of the relationship between the predictors and response. The R-squared in your regression output is a biased estimate based on your sample. An unbiased estimate is one that is just as likely to be too high as it is to be too low, and it is correct on average. If you collect a random sample correctly, the sample mean is an unbiased estimate of the population mean. A biased estimate is systematically too high or low, and so is the average.
I'm still pretty new to regression modeling and I don't quite understand why $r^2$ is a biased estimate. Could someone dumb this down a bit for me?