Soa Exam P: Probability (Part 1 Of 2) [Actuarial] By Logenthiran Kanisan

Part 1 of 2 courses covering the content of SOA Exam P/CAS Exam 1

What you'll learn

Prepare for Society of Actuaries (SOA) Exam P (Probability) [This is part 1 of 2 parts]

Understand basic probability concepts

Understand key concepts concerning discrete univariate random variables

Understand key concepts concerning continuous univariate random variables

Requirements

College level calculus & algebra knowledge is required.

Description

Suitable for students looking for an in-depth understanding of concepts tested in SOA Exam P/ CAS Exam 1.All of the course objectives for SOA Exam P are covered in depth in this course.Also suitable for actuarial students and practitioners who are looking to brush up their understanding of Probability.Part 1 covers the following course objectives:Course Objective 1: General ProbabilityCourse Objective 2: Discrete Univariate Random VariablesCourse Objective 3: Continuous Univariate Random VariablesExam P is a pre-requisite for Exam FAM and Exam ASTAM/ALTAM and other upper level SOA exams.Please be sure to refer to the SOA and CAS websites for the most current details regarding the exams. About the InstructorI am very passionate about actuarial science and education, and have been actively involved in the actuarial education scene for many years. I have been tutoring students for SOA (Society of Actuaries) exams for 5 years, and have volunteered as the Education Chairperson for the Actuarial Society of Malaysia from 2018 to 2020.I have also lectured in a number of universities, including Inti International University & Colleges, Universiti Malaya and UCSI University. I am also currently an Industry Advisor at the Asia Pacific University (APU).I actively volunteer to speak to students about actuarial science, and do mentor students.

Overview

Section 1: Combinatorial Probability

Lecture 1 The Probability Model, Tree Diagram, Multiplication Principles

Lecture 2 Permutations, Combinations, Partitions

Lecture 3 Sampling & Distribution

Lecture 4 Tutorial - Combinatorial Probability

Section 2: General Rules of Probability

Lecture 5 Set Theory

Lecture 6 Conditional Probability & Independence

Lecture 7 Bayes Theorem & Credibility

Lecture 8 Tutorial - General Probability

Section 3: Discrete Random Variables

Lecture 9 Discrete Random Variables, Cumulative Probability Distribution

Lecture 10 Measures of Central Tendency

Lecture 11 Measures of Dispersion

Lecture 12 Chebychev’s Theorem, Coeff of Variation, Conditional Expectation and Variance

Lecture 13 Jointly Distributed Random Variables, the Probability Generating Function

Lecture 14 Tutorial - Discrete Random Variables

Section 4: Some Discrete Distributions

Lecture 15 Discrete Uniform, Bernoulli, Binomial, Geometric

Lecture 16 The Negative Binomial Distribution, the Hyper-Geometric Distribution

Lecture 17 Poisson Distribution

Lecture 18 Tutorial - Some Discrete Distributions

Section 5: Continuous Distributions

Lecture 19 Cumulative Density Functions & Probability Density Functions

Lecture 20 Expectations, Variance, Mode, Median, Percentiles & Expected Value with the CDF

Lecture 21 Mixed Distributions, Applications to Insurance- Deductibles, Caps

Lecture 22 CDF Method for Deductibles and Caps, Moment Generating Functions

Lecture 23 Tutorial - Continuous Distributions

Students preparing for Society of Actuaries (SOA) Exam P/ Casualty Actuarial Society (CAS) Exam 1