From Mary Jannausch · University of Michigan on researchgate.net
R^2, the coefficient of multiple determination, is defined as (SS_(REG))/(SS_(TOTAL)) or, equivalently, 1 - (SSE)/(SSTO). R^2 measures the proportionate reduction in variation of Y, associated with the set of X predictors. R^2 will be inflated as more X variables are added. The adjusted R^2 was therefore derived, as R_(adj)^2 = 1 - {[(n-1)/(n-p)][(SSE)/(SSTO)]}.
[If] (...), n=60, p=10. so, (n-1)/(n-p) = 59/50 = 1.18, and you have
R_(adj)^2 = 1 - (1.18)(SSE)/(SSTO). If the ratio (SSE)/(SSTO) is close enough to 1, then you can see how the R_(adj)^2. can be negative. [In which case it can be interpreted as zero.]
[If negative] (...); you have too many predictors chasing too little information (a la small n). Just because one can run a model with n=60, and 10 predictors, does not mean that one should.
