# The Exam P Syllabus Refresher Series Program

## Register Today!

### Join Exam P Syllabus Refresher Classes to gain a much stronger conceptual understanding of difficult Exam P topics and learn how you will be tested on these probability concepts in P/1 problem solving.

Charles Major has accumulated over 40 years of experience in leading students to academic and actuarial exam success.  Professor Major leads classes at both New York University and University of North Florida for students preparing for their actuarial exams, specializing in Exams P, FM, and IFM.  Professor Major holds a BS in Math and Statistics from University of New Hampshire, MS in Math, Statistics, and Operations Research for Finance from New York University, and is working towards a PhD in Math and Science from University of New Hampshire.

## Every Class Registration Provides the Following:

1. Live lecture offering the teaching and reviewing of exam syllabus topics, reinforcing the concepts by working through practice problems, and illustrating how concepts are tested in P/1 problems
2. Open forum immediately following live lecture to discuss any questions with instructor
3. Problem set/solutions distributed to students following lecture to reinforce concepts learned
4. Q & A correspondence with instructor
5. Online office hours
6. Recording of lecture sent to students

## Live Classes

Class 1: Sat, 2/6
12:00 PM – 3:00 PM EST
Class 1: Bayes Theorem

• Prior Distributions. What are they and examples
• The Likelihood Distribution. Why is it useful and examples
• The Posterior Distribution. How is it used and interpretation
• The Formula and setting up the problem.

Class 2: Sat, 2/13
12:00 PM – 3:00 PM EST
Class 2: Loss Functions

• Defining the terminology of Loss Problems
• Calculating Loss with Deductibles
• Calculating Loss with Limits of Insurance Payments

Class 3: Sat, 2/13
4:00 PM – 6:00 PM EST
Class 3: Moment Generating Functions

• Definition of a MGF
• Examples from a density function
• Properties of MGF
• The MGF for Sums of density functions
• Expected Value using MGF
• Variance using MGF

Class 4: Sat, 2/20
12:00 PM – 4:00 PM EST
Class 4: Negative Binomial, Geometric, Binomial, Hypergeometric, Multinomial

• Definition of each
• How to choose the correct distribution
• Examples of each distribution

Class 5: Sun, 2/27
12:00 PM – 3:00 PM EST
Class 5: Multivariate and Multivariate Conditional

• Examples of Multivariate Functions
• Finding Expectations
• Finding Marginal Distributions
• Finding Conditional Probability
• Finding Conditional Expectation

Class 6: Sat, 2/27
12:00 PM – 3:00 PM EST
Class 6: Variance and Covariance

• Univariate Variance Definition
• Examples of Univariate Variance for Discrete Variables
• Examples of Univariate Variance for Continuous Variables
• Covariance of several Discrete and Continuous Variables
• E(x^2) – (E(x)}^2 = V(x)
Applications

Class 7: Sun, 3/7
12:00 PM – 3:00 PM EST
Class 7: Special Distributions

• Exponential Distribution
- The Distribution and its components
- The Expected Value and Variance
- Survivorship Applications The Compliment
- The Exponential and Poisson Interaction
- Inter-arrival Times
• Beta Distribution
- The Distribution and it components
- The Expected Value and Variance
- The Tau Function
- Applications
• Gamma Distribution
- The Distribution and its components
- The Expected Value and Variance
- Sums of Exponential Distributions
- Applicating ons

Class 8: Sun, 3/7
5:00 PM – 7:00 PM EST
Class 8: Mixed Distributions

• Definition of a Mixed Distribution
• Examples for Probability Computation
• Examples for Expectation and Variance