My dataset ($N \approx 10,000$) has a dependent variable (DV), five independent "baseline" variables (P1, P2, P3, P4, P5) and one independent variable of interest (Q).
I have run OLS linear regressions for the following two models:
DV ~ 1 + P1 + P2 + P3 + P4 + P5 -> R-squared = 0.125 DV ~ 1 + P1 + P2 + P3 + P4 + P5 + Q -> R-squared = 0.124
I.e., adding the predictor Q has decreased the amount of variance explained in the linear model. As far as I understand, this shouldn't happen.
To be clear, these are R-squared values and not adjusted R-squared values.
Is there any reason I could be seeing this phenomenon? Perhaps something relating to the OLS method?