Actuarium

Formula sheets

Every formula on the syllabus, rendered natively, with the traps examiners set for each. Print one per exam and keep it by your desk.

SRM β€” Statistics for Risk Modeling

SOA

Coefficient of determination β€…β€ŠR2=1βˆ’SSESST\;R^2=1-\dfrac{SSE}{SST}, adjusted β€…β€ŠRΛ‰2=1βˆ’SSE/(nβˆ’pβˆ’1)SST/(nβˆ’1)\;\bar R^2=1-\dfrac{SSE/(n-p-1)}{SST/(n-1)}

Logistic regression β€…β€Šln⁑p1βˆ’p=x⊀β\;\ln\dfrac{p}{1-p}=\mathbf{x}^\top\beta, p=11+eβˆ’x⊀βp=\dfrac{1}{1+e^{-\mathbf{x}^\top\beta}}

Bias–variance β€…β€ŠE[(yβˆ’f^)2]=Bias⁑2+Var⁑+Οƒ2\;E[(y-\hat f)^2]=\operatorname{Bias}^2+\operatorname{Var}+\sigma^2

PCA proportion of variance of component jj: Ξ»j/βˆ‘kΞ»k\lambda_j/\sum_k\lambda_k

Exponential smoothing β€…β€Šy^t+1=Ξ±yt+(1βˆ’Ξ±)y^t\;\hat y_{t+1}=\alpha y_t+(1-\alpha)\hat y_t

Traps to remember

  • Adjusted R2R^2 can fall when adding a predictor; R2R^2 never does.

  • Interpreting a logistic coefficient as a change in probability instead of log-odds.

  • Confusing k-fold CV with LOOCV bias/variance properties.

  • Assuming random forests need pruning (they do not).

Ask the tutor