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

All 26 CAS MAS-I (Modern Actuarial Statistics I) lessons are free to read, each with worked examples and audio narration. No signup required.

Probability Models

• Model claim frequencies using Poisson processes. (10 min)
• Calculate expected values, variances, and probabilities for any Poisson process. (10 min)
• Calculate limited expected value. (11 min)
• Perform survival model and hazard rate calculations. (11 min)
• Perform joint life calculations. (11 min)
• Calculate simple whole life or annuity problems. (12 min)
• Simulate random variables using the inversion method and basic Monte Carlo techniques. (8 min)
• Calculate system reliability for series, parallel, and bridge configurations of independent components, and analyze Markov chain transition matrices. (11 min)

Statistics

• Estimate the mean and variance given a sample. (9 min)
• Estimate a sufficient statistic for a distribution. (8 min)
• Test statistical hypotheses, including Type I and Type II errors. (10 min)
• Test means and variances using critical values from a sampling distribution. (9 min)
• Model insurance claim frequency and severity. (9 min)
• Model insurance claims in aggregate. (9 min)
• Calculate order statistics of a sample. (10 min)
• Perform point estimation of statistical parameters using maximum likelihood estimation (MLE) applying criteria such as consistency, unbiasedness, sufficiency, efficiency, minimum variance, and mean square error (including censoring and truncation). (9 min)
• Adjust calculations for the effect of missing data values, including censoring and truncation. (10 min)

Extended Linear Models

• Select the appropriate model for an extended linear model. (15 min)
• Select the appropriate model structure for an extended linear model given the behavior of the data set (e.g., appropriate link function and distribution for GLM). (14 min)
• Evaluate models developed using an extended linear model approach. (15 min)
• Interpret the extended linear model output from statistical software, such as parameter estimate tables and ANOVA tables. (13 min)
• Distinguish among categorical, ordinal, and continuous predictors and their interactions, and how these relate to their usage in an extended linear model. (16 min)
• Understand and apply control and offset variables in GLMs. (16 min)
• Understand and calculate AIC, BIC, deviance, and R-squared. (14 min)
• Analyze model diagnostic plots (e.g., residual, marginal model, QQ, and added variable plots) to assess quality of fit. (15 min)
• Interpret exploratory data analysis plots for various data types (e.g., box, univariate, histograms). (15 min)