[[Actuarial Notes Wiki|Wiki]] / [[Actuarial Certifications]] / [[Casualty Actuarial Society (CAS)]] / **MAS-I (CAS)** **Exam Track** [[Associate of the Casualty Actuarial Society (ACAS)|ACAS]] <div class="exam-nav" data-color="#7c3aed" data-prev="Exam FM|Financial Mathematics|Exam FM-2 (SOA).md|SOA/CAS" data-current="MAS-I|Modern Actuarial Statistics 1" data-next="MAS-II|Modern Actuarial Statistics 1|Exam MAS-II (CAS).md" </div> ## Exam MAS-I Syllabus [April / May 2026 Content Outline - MAS-I]([https://www.soa.org/globalassets/assets/files/edu/2026/spring/syllabi/2026-03-exam-p-syllabus.pdf](https://www.casact.org/sites/default/files/2025-05/MASI_ContentOutline_2025.pdf)) **Exam MAS-I (Modern Actuarial Statistics)** is a 4 hour exam with a 15 minute break, with 45 multiple choice questions about probability models, statistics, and extended linear models. ### Sample Questions and Answers - [4 Sample Questions and Answers for Exam MAS-I](https://www.casact.org/sites/default/files/2021-04/MASI_Sample_Questions.pdf) **Past Exams** <div class="download-dropdown" data-color="#2563eb" data-label="Download Past Exams" data-files="MAS-I Exam - Fall 2019|https://www.casact.org/sites/default/files/2021-02/admissions_studytools_exammasi_fmasi-19.pdf,MAS-I Exam - Spring 2019|[https://www.soa.org/globalassets/assets/files/edu/2026/spring/syllabi/2026-05-exam-p-syllabus.pdf](https://www.casact.org/sites/default/files/2021-02/admissions_studytools_exammasi_spmasi-19.pdf),MAS-I Exam - Fall 2018|https://www.casact.org/sites/default/files/2021-02/admissions_studytools_exammasi_fmasi-18.pdf,MAS-I Exam - Spring 2018|https://www.casact.org/sites/default/files/2021-02/admissions_studytools_exammasi_spmasi-18.pdf"> </div> ### Learning Objectives > [!example]- ➗ Probability Models (Stochastic Processes and Survival Models) {20-30%} >A ==Stochastic Process== is a collection of random variables indexed by time, {X(t), t ≥ 0}, describing how a system evolves randomly over time. "Stochastic" simply means random. > >A ==Survival Model== is a specification of the probability distribution of a non-negative random variable T, representing the **time until an event of interest.** > > ### Learning Objectives > Solve problems using stochastic processes and determine the probabilities and distributions associated with these processes. > 1. Model [[claim frequencies]] using Poisson processes > 2. Calculate expected values, variances, and probabilities for any [[Poisson process]] > 3. Calculate [[limited expected value]] > 4. Perform [[survival model]] and [[hazard rate]] calculations > 5. Perform [[joint life]] calculations > 6. Calculate [[simple whole life]] or [[annuity]] problems > [!example]- 📊 Statistics {20-30%} > ==Statistics== is the science of collecting, analyzing, and drawing inferences from data. > > ### Learning Objectives > Apply the concepts typically covered in the 2nd semester of a two-semester undergraduate sequence in Probability and Statistics. >1. Estimate the mean and variance given a sample >2. Estimate a sufficient statistic for a distribution >3. Test statistical hypotheses, including Type I and Type II errors >4. Test means and variances using critical values from a sampling distribution >5. Model insurance claim frequency and severity >6. Model insurance claims in aggregate >7. Calculate order statistics of a sample >8. Perform point estimation of statistical parameters using maximum likelihood estimation (MLE) applying criteria to estimates such as consistency, unbiasedness, sufficiency, efficiency, minimum variance, mean square error (e.g., accounting for censoring and truncation in the data) >9. Adjust calculations for the effect of missing data values, including censoring and truncation > > **Readings:** > - Hogg, McKean, and Craig > - Tse > [!example]- 📈 Extended Linear Model {45-55%} > > ### Learning Objectives > Solve problems using extended linear models and determine when these models are appropriate to use. > > 1. Select the appropriate model for an extended linear model > 2. Select the appropriate model structure for an extended linear model given the behavior of the data set to be modeled (e.g., appropriate [[link function]] and distribution for the dependent variable for GLM) > 3. [[Evaluate models]] developed using an extended linear model approach > 4. Interpret the extended linear model output from statistical software, such as [[Parameter Estimate Tables]] and [[ANOVA]] tables > 5. Distinguish among [[categorical]], [[ordinal]], and [[continuous]] predictors and their interactions, and how these relate to their usage in an extended linear model > 6. Understand and apply [[Control Variable|control]] and [[offset variables]] in GLMs > 7. Understand and calculate [[AIC]], [[BIC]], [[deviance]], and [[R-squared]] > 8. Analyze model diagnostic plots (e.g., residual, marginal model, QQ, and added variable plots) to assess quality of fit > 9. Interpret exploratory data analysis plots for various data types (e.g., box, univariate, histograms) > - Dobson and Barnett > - Hogg, McKean, and Craig > - James et al. > - Larsen