Exam MAS-I

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Welcome to the MAS-I Announcements Page!

Thu, 17 Nov 2016

Welcome to the Announcements Tab for the TIA MAS-I Seminar. Here you will primarily find records of important updates, changes, and corrections to the seminar content. Any old posts here do not need to be considered for new users of the seminar – if there are any old posts that need to be considered again, … Continue reading →

CAS MAS-I Going to CBT

Fri, 28 Aug 2020

As you are probably already aware, the CAS is moving MAS-I and MAS-II to a Computer-Based Testing (CBT) format. The first sitting in this new format will take place this fall on November 10th. With this format, you must first register for the exam with the CAS and then schedule your exam as soon as … Continue reading →

D.2 Summary sheet typo repair

Fri, 28 Aug 2020

The second formula for pi from the logistic Model was missing a negative sign in the exponent. This has been fixed now.

CAS CBT Information

Tue, 23 Jun 2020

According to the CAS, if you were registered for the Spring 2020 MAS-I sitting, you should be automatically registered for the Fall CBT sitting. HOWEVER!!!! You will still have to schedule your exam with Pearson VUE. The CAS says that you should receive an email with information on registering by Friday, June 26th. … Continue reading →

Before You Begin ... lessons ... min of video
- ***Start Here!*** ...
  - How to use the seminar (sample) (3:49)
- New to TIA? Take a tour. ...
  - How to Prepare for actuarial exams (sample) (15:41)
  - Practice App Tour (sample) (15:24)
  - Forum Tour (sample) (5:58)
  - You Failed. Now what? (12:00)
- Schedule, Syllabus and Study Notes ...
  - Customizable Study Schedule (handout)
  - Video Player Keyboard Shortcuts (sample) (handout)
  - 2019 Exam Syllabus (From CAS) (handout)
  - Exam Tables Overview (17:28)
  - Poisson Processes Study Note (From CAS) (handout)
  - Life Contingencies Study Note (From CAS) (handout)
  - GLM Study Note (From CAS) (handout)
  - Knowledge Mapping (From CAS) (handout)
- Formula Sheets ...
  - Cas MAS-I Exam Tables (handout)
  - A.1 Formula Sheet (handout)
  - B.1 Formula Sheet (sample) (handout)
  - B.2 Formula Sheet (handout)
  - B.3 Formula Sheet (handout)
  - B.4 Formula Sheet (handout)
  - B.5 Formula Sheet (handout)
  - C.1 Formula Sheet (handout)
  - C.2 Formula Sheet (handout)
  - C.3 Formula Sheet (handout)
  - D.1 Formula Sheet (old) (handout)
  - D.2 Formula Sheet (old) (handout)
  - D.1 Formula Sheet (2020 Update) (handout)
  - D.3 Formula Sheet (old) (handout)
  - D.2 Formula Sheet (2020 Update) (handout)
  - D.3 Formula Sheet (2020 Update) (handout)
  - D.4 Formula Sheet (handout)
  - D.5 Formula Sheet (handout)
  - D.6 Formula Sheet (handout)
  - E.1 Formula Sheet (handout)
  - E.2 Formula Sheet (handout)
  - E.3 Formula Sheet (handout)
A. Prerequisite Material ... lessons ... min of video
- Prerequisite Review ...
  - How to use these lessons (1:55)
  - Derivatives: Basic Formulas (7:27)
  - Derivatives: Chain Rule (6:39)
  - Derivatives: Product Rule (5:10)
  - Integration: Basic Formulas (6:27)
  - Integration: Substitution (6:12)
  - Integration by Parts (3:57)
  - A.1.2 Probability Review (37:14)
- Spreadsheet Examples ...
  - About these lessons (1:21)
  - Basic Formulas (13:06)
  - Poisson and Gamma Distributions (5:42)
  - Normal Distribution Calculations (5:37)
  - Matrix Multiplication (5:15)
  - Systems of linear equations (6:36)
  - Actuarial Present Value (4:23)
  - Binomial Distribution (4:23)
  - Percentiles (2:19)
  - T-distribution (9:22)
  - F-distribution (10:00)
  - Chi-square distribution (10:31)
  - Kolmogorov-Smirnov Statistic (3:09)
  - Linear Regression (4:14)
  - Autocorrelation (7:48)
  - Time Series Forecasts (2:37)
B. Stochastic Processes ... lessons ... min of video
- B.1 Poisson Processes ...
  - B.1.1 Exponential Random Variables Part 1 (26:26)
  - B.1.2 Exponential Random Variables Part 2 (22:17)
  - B.1.3 What is a Poisson Process? (sample) (18:13)
  - B.1.4 Waiting Times, Classification (25:09)
  - B.1.5 Nonhomogeneous Poisson Processes (17:01)
  - B.1.6 Compound Poisson Processes (22:53)
- B.2 Continuous Multiple Lives/Decrements ...
  - B.2.1 Hazard Rate (26:36)
  - B.2.2 Joint-Life Status (18:59)
  - B.2.3 Last-Survivor Status (15:01)
  - B.2.4 Multiple Decrements (12:42)
  - B.2.5 Multi-Component Joint-Life and Last-Survivor (21:42)
  - B.2.6 The Bridge System (35:44)
  - B.2.7 The k out of n system (22:18)
  - B.2.8 The Random Graph (22:15)
  - B.2.9 Expected Lifetimes (21:26)
- B.3 Markov Chains ...
  - B.3.1 Discrete Markov Chains (22:31)
  - B.3.2 Classification of States (sample) (20:42)
  - B.3.3 Long Run Behavior (26:21)
  - B.3.4 Applications 1 (22:41)
  - B.3.5 Applications 2: Branching Processes (18:32)
- B.4 Life Contingencies ...
  - B.4.0 Intro to Life Contingencies for MAS-I (7:30)
  - B.4.1 Life Tables and the Illustrative Life Table (ILT) (31:25)
  - B.4.2 Life Tables Applications (25:54)
  - B.4.3 Whole Life Insurance (28:08)
  - B.4.4 Insurance Examples (24:17)
  - B.4.5 Whole Life Annuities (17:52)
  - B.4.6 Annuities Examples (25:00)
  - B.4.7 Premiums (22:24)
- B.5 Simulation ...
  - B.5.1 Simulating Uniform Random Numbers (17:37)
  - B.5.2 Inverse Transform Method (10:51)
  - B.5.3 Acceptance-Rejection Method (31:41)
C. Statistics ... lessons ... min of video
- C.1 Parameter Estimation ...
  - C.1.1 Estimator Basics (sample) (19:28)
  - C.1.2 Comparing Estimators: Mean and Variance (22:59)
  - C.1.3 Comparing Estimators: Likelihood and Sufficiency (26:22)
  - C.1.4 The Maximum Likelihood Estimator (30:38)
  - C.1.5 Advanced Likelihood Functions (26:40)
  - C.1.6 Variance of MLE (18:34)
  - C.1.7 Exponential Family (31:42)
  - C.1.8 Method of Moments (15:53)
  - C.1.9 Percentile Matching (20:47)
  - C.1.10 Kernel Density Estimation (31:32)
- C.2 Hypothesis Testing ...
  - C.2.1 Hypothesis Testing (34:57)
  - C.2.2 Tests for the mean: ``Known'' variance (32:04)
  - C.2.3 Tests for the mean: ``Unknown'' Variance (25:45)
  - C.2.4 Comparing Populations (29:20)
  - C.2.5 Tests for Variance (16:05)
  - C.2.6 Confidence Intervals (36:51)
  - C.2.7 Other Chi-Square Tests (26:07)
  - C.2.8 Likelihood Ratio Test (39:52)
  - C.2.9 Kolmogorov-Smirnov Test (20:21)
- C.3 Probability for Insurance ...
  - C.3.1 Order Statistics (24:38)
  - C.3.2 Uniform and Exponential Order Stats (17:53)
  - C.3.3 Mixtures (17:24)
  - C.3.4 Transformations (13:04)
  - C.3.5 Tail Properties (26:23)
  - C.3.6 Policy Adjustments (25:25)
D. Extended Linear Models ... lessons ... min of video
- D.1 Linear Regression ...
  - D.1.0 Into to Extended Linear Models (11:31)
  - D.1.1 Statistical Learning (21:15)
  - D.1.2 Minimal and Maximal Models (29:29)
  - D.1.3 Single Variable Linear Regression (41:49)
  - D.1.4 Variations on Regression (27:17)
  - D.1.5 Multiple Linear Regression (28:08)
  - D.1.6 One Categorical Predictor - One-Way ANOVA (38:58)
  - D.1.7 Two Factor ANOVA (34:35)
  - D.1.8 General Linear Models (21:53)
- D.2 Generalized Linear Models ...
  - D.2.0 Intro to GLM (9:15)
  - D.2.1 Model Definition (17:42)
  - D.2.2 Exponential Family for GLM (30:13)
  - D.2.3 Model Estimation (39:01)
  - D.2.4 Continuous Response Models (17:35)
  - D.2.5 Binary Response Data (22:52)
  - D.2.6 Non-linear Binary Response Models (21:13)
  - D.2.7 Poisson Count Models (14:21)
  - D.2.8 Multinomial Response Models (31:38)
- D.3 Model Testing ...
  - D.3.0 Intro to GLM Testing (3:35)
  - D.3.1 The Wald Statistic and Single Coefficient Testing (29:43)
  - D.3.2 Deviance Testing (25:35)
  - D.3.3 F-test, Comparing Two Models (26:20)
  - D.3.4 F-test, More Than Two Models (18:55)
- D.4 Model Evaluation ...
  - D.4.1 Residuals (20:21)
  - D.4.2 Evaluating Fit Using Residuals (35:59)
  - D.4.3 Goodness of Fit Statistics (23:16)
  - D.4.4 Cross Validation (28:10)
  - D.4.5 Bootstrap (15:00)
- D.5 Model Selection ...
  - D.5.1 Subset Selection (23:22)
  - D.5.2 High-Dimension Models (19:11)
  - D.5.3 Shrinkage Methods (30:18)
  - D.5.4 Dimension Reduction Methods (26:18)
- D.6 Generalized Additive Models ...
  - D.6.1 Generalized Additive Models (17:33)
  - D.6.2 Regression Splines (25:31)
  - D.6.3 Smoothing Splines and Local Regression (21:29)
- D.7 Modeling with R ...
  - D.7.1 Continuous Response (38:56)
  - D.7.2 Categorical Response (28:20)
  - D.7.3 Count Data (25:56)
  - D.7.4 High Dimensional Modeling Part 1 (20:20)
  - D.7.4 High Dimensional Modeling Part 2 (19:54)
  - D.7.5 GAM Part 1 (19:57)
  - D.7.5 GAM Part 2 (16:02)
- D.1 Classical Linear Models (Pre-2020 Old lessons) ...
  - D.1.0 Statistical Learning (20:42)
  - D.1.1 Single Variable Linear Regression (33:31)
  - D.1.2 Multivariable Linear Regression (37:16)
  - D.1.3 Categorical Variables (30:35)
  - D.1.4 General Linear Models (27:48)
- D.2. Generalized Linear Models (Pre-2020 Old Lessons) ...
  - D.2.1 GLM Overview (8:25)
  - D.2.2 Exponential Families, Redux (23:48)
  - D.2.3 Continuous Response Distributions (15:19)
  - D.2.4 Categorical Response: Binary (18:06)
  - D.2.5 Categorical Response: Multinomial (26:57)
  - D.2.6 Categorical Response: Count Data (13:09)
- D.3 Model Testing and Estimation (Pre-2020 Old Lessons) ...
  - D.3.1 Score Statistic (31:11)
  - D.3.2 Wald Statistic (17:15)
  - D.3.3 Likelihood Ratio Statistic: Deviance (20:05)
  - (Old - Watch New D.2.3 instead) D.3.4 Model Estimation (20:29)
  - D.3.5 Testing with the Wald Statistic (44:00)
  - D.3.6 Testing with Deviance (39:49)
  - D.3.7 Testing General Linear Models (38:10)
E. Time Series ... lessons ... min of video
- E.1 Time Series Basics ...
  - E.1.1 Time Series (19:03)
  - E.1.2 Decomposition (16:53)
  - E.1.3 Correlation (27:35)
  - E.1.4 Cross-Correlation (17:14)
- E.2 Time Series Models ...
  - E.2.1 Random Walks (31:47)
  - E.2.2 Autoregressive Models (24:18)
  - E.2.3 Moving Average Models (19:41)
  - E.2.4 ARMA Models (23:15)
  - E.2.5 Non-seasonal ARIMA Models (17:09)
  - E.2.6 Seasonal ARIMA Models (20:06)
- E.3 Time Series Regression ...
  - E.3.1 Regression Modelling (16:13)
  - E.3.2 Seasonal Regression (24:46)
  - E.3.3 Logarithmic and Nonlinear Models (24:46)
What to do after you finish the lessons ... lessons ... min of video
- Managing the work problems study phase ...
  - The Last Lesson (8:33)
Review Lessons ... lessons ... min of video
- Stochastic Processes Review ...
  - Poisson Processes Review (25:28)
  - Continuous/Multiple Lives Review (37:23)
  - Markov Chains Review (21:29)
  - Life Contingencies Review (41:37)
  - Simulation (20:59)
- Statistics Review ...
  - Parameter Estimation Review (33:23)
  - Hypothesis Testing Review (42:49)
  - Probability for Insurance Review (35:46)
- General Linear Models Review ...
  - Linear Regression Review (Coming Fall 2020) (handout)
  - General Linear Models Review (Coming Fall 2020) (handout)
  - Model Testing Review (Coming Fall 2020) (handout)
  - Model Evaluation Review (33:22)
  - Model Selection and Regularization Review (26:46)
  - General Additive Models Review (23:04)
- Time Series Review ...
  - Time Series Basics Review (27:12)
  - Time Series Models Review (31:22)
  - Time Series Regression Review (19:16)

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Exam MAS-I