Stats don’t appear too correlated, although all cross correlations are negative around −7% to −10% which is interesting. I guess it might have to do with data construction. Simple logistic regression gives coeffs: CHA=0.143 CON=0.141 DEX=-0.016 INT=0.099 STR=0.116 WIS= 0.156. Based on these one would push WIS+8 to 20 and allocate the remaining two points CHA+2.
However values are close, and there are standard errors around the estimates. Bootstrapping strategies to account for that allocates: WIS+7, CHA+2, CON+1.
Accounting for cross effects flips them around, with allocation: CHA+6, WIS+3, CON+1. Going full second order allocates CHA+6, WIS+3, STR+1, but obviously with higher complexity.
My answer would overweigh the linear when blending with the ones with more parameters. Final answer WIS+6, CHA+3, CON+1.
Stats don’t appear too correlated, although all cross correlations are negative around −7% to −10% which is interesting. I guess it might have to do with data construction. Simple logistic regression gives coeffs: CHA=0.143 CON=0.141 DEX=-0.016 INT=0.099 STR=0.116 WIS= 0.156. Based on these one would push WIS+8 to 20 and allocate the remaining two points CHA+2.
However values are close, and there are standard errors around the estimates. Bootstrapping strategies to account for that allocates: WIS+7, CHA+2, CON+1.
Accounting for cross effects flips them around, with allocation: CHA+6, WIS+3, CON+1. Going full second order allocates CHA+6, WIS+3, STR+1, but obviously with higher complexity.
My answer would overweigh the linear when blending with the ones with more parameters. Final answer WIS+6, CHA+3, CON+1.