Free CAS MAS-II (Modern Actuarial Statistics II) Lessons

All 21 CAS MAS-II (Modern Actuarial Statistics II) lessons are free to read, each with worked examples and audio narration. No signup required.

Introduction to Credibility

• Calculate classical (limited fluctuation), Buhlmann, Buhlmann-Straub, and Bayesian credibility-weighted estimates for frequency, severity, and aggregate loss. (16 min)
• Understand the framework used for the classical (limited fluctuation), Buhlmann, Buhlmann-Straub, and Bayesian credibility procedures. (20 min)

Linear Mixed Models

• Understand the assumptions behind the linear mixed model design. (11 min)
• Understand how to use a hierarchical model. (11 min)
• Interpret output from a linear mixed model and make appropriate choices when evaluating modeling options. (13 min)
• Interpret linear mixed model diagnostics and summary statistics to evaluate the linear mixed model structure and variable selection. (13 min)

Statistical Learning

• Compute K-nearest neighbors (KNN). (13 min)
• Calculate measures of model predictive accuracy (e.g., Lift, Gini index, AUROC). (13 min)
• Compare models via predictive performance measures (e.g., double lift chart). (13 min)
• Prune decision trees. (13 min)
• Calculate the summary statistics for a set of decision trees (e.g., Gini index, entropy, residual sum of squares). (11 min)
• Understand the assumptions underlying different tree ensemble methods and the improvements they can make to decision trees. (13 min)
• Compute elements of principal components analysis (PCA) (e.g., loading vectors, variance explained). (12 min)
• Interpret principal components analysis (PCA) software outputs. (11 min)
• Perform the computations behind clustering procedures (e.g., K-means, hierarchical). (13 min)
• Interpret clustering procedures outputs. (12 min)
• Interpret neural network results. (14 min)

Time Series with Constant Variance

• Model relationships of current and past values of a statistic or metric. (15 min)
• Understand the framework of ARIMA models (e.g., trends and seasonality). (13 min)
• Calculate trends and seasonality using time series with regression (e.g., deterministic vs. stochastic trend). (15 min)
• Interpret time series output to make forecasts. (15 min)