STATISTICS


Course Credits: 3 Units

Prerequisites: Math 153, Stat 104

STAT 173 - Elementary Actuarial Statistics

Course Description

This course covers the use of database software, spreadsheet and statistical software packages for data management.

Course Learning Outcomes

After completion of the course, the student should be able to:

  1. Demonstrate mastery of advanced statistical terms and concepts;
  2. Formulate steps in explaining phenomena and making decisions based on available data;
  3. Generate and present appropriate statistical analysis given statistical data; and
  4. Apply statistical procedures using software that are appropriate for different types of data.
Course Outline

UNIT 1. Theory of Interest

  1. Simple and Compound Interest
  2. Annuities
  3. The Present Value of an Annuity
  4. Loans
  5. Bonds
  6. Rate of Return
  7. Discount and Force of Interest

UNIT 2. Risk and Insurance

  1. Managing and Quantifying Risks
  2. Risk Measures
  3. Insurance Products

UNIT 3. Introduction to Life Insurance

  1. Survival Models
  2. Life Tables and Selections
  3. Life Insurance Benefits
  4. Life Annuities
  5. Life Insurance Pricing and Reserving

UNIT 4. Claims Reserving and Pricing

  1. Chain Ladder Methods
  2. Average Cost per Claim Method
  3. Loss Ratio Method
  4. Statistical Modelling and the Separation Technique

UNIT 5. Loss Distribution

  1. Introduction to Loss Distributions
  2. Classical Loss Distributions
  3. Fitting Loss Distributions
  4. Loss Distributions and Reinsurance

UNIT 6. Risk Theory

  1. Risk models for Aggregate Claims
  2. Collective Risk Models
  3. Individual Risk Models for S
  4. Premiums and Reserves for Aggregate Claims
  5. Reinsurance for Aggregate Claims

UNIT 7. Ruin Theory

  1. The Probability of Ruin in a Surplus Process
  2. Surplus and Aggregate Claim Processes
  3. Probability of Ruin and the Adjustment Coefficient
  4. Reinsurance and the Probability of Ruin

UNIT 8. Credibility Theory

  1. Introduction to Credibility Estimates
  2. Classical Credibility Theory

UNIT 9. Generalized Linear Models

  1. Linear and Generalized Linear Models
  2. Multiple Linear Regression and the Normal Model
  3. The Structure of GLMs
  4. Model Selection and Deviance