Exam P for actuariess

[Panter]

Exam P For Actuariess
Published 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.51 GB | Duration: 19h 0m

The first actuary exam



What you'll learn
The Candidate will understand basic probability concepts, combinatorics, and discrete mathematics.
The Candidate will understand key concepts concerning discrete and continuous univariate random variables
The Candidate will understand key concepts concerning multivariate discrete random variables and the distribution of order statistics
Guide candidate to complete the first actuary exam

Requirements
Basic calculus. (differentiate and integration)
Basic algrebra

Description
Exam P is a three hour multiple choice examination and is offered via computer based testing (CBT). It's the first step toward being an actuary.The syllabus for Exam P develops the candidate's knowledge of the fundamental probability tools for quantitatively assessing risk. The application of these tools to problems encountered in actuarial science is emphasized. A thorough command of the supporting calculus is assumed. Additionally, a very basic knowledge of insurance and risk management is assumed.Candidates are expected to spend more than 300 hours studing and practice on this exam. The pass rate is around 45%This course is taught by Yodharin Monplub. Professor Yodharin Monplub has an almost ASA qualification from the SOA, with only FAP left. The following are his exams qualification:SOA: P FM IFM SRM STAM LTAMCAS: 1 2 3/F MAS1 MAS2 At a young age of 22, he is now a full time teacher with over 4 years of teaching experience. There are 7 sections on this exam.This will cover all of exam P syllabusExam P Section 1 Basic probability, Conditional probability, Independence, Combinatoric and PermutationExam P Section 2 Random variable, PDF and CDFExam P Section 3 Expectation and VarianceExam P Section 4 Frequently use discrete distributionExam P Section 5 Frequently use continuous distributionExam P Section 6 Joint, Marginal and Conditional distributionExam P Section 7 Transformation of variable Practice QuestionsMany practice questions to prepare students for exam P. The difficulty is similar to the real exam P sitting.

Overview
Section 1: Basic probability, conditional probability, independence and combinatoric

Lecture 1 Event and Venn Euler.mp4

Lecture 2 Union of events

Lecture 3 intersect of events

Lecture 4 Mutually exclusive outcome

Lecture 5 Complement of event

Lecture 6 Subset and subevent

Lecture 7 Independent of event A and B

Lecture 8 Conditional probability 1

Lecture 9 Conditional probability 2

Lecture 10 Conditional probability 3 4

Lecture 11 Conditional probability 5 6

Lecture 12 Conditional probability 7

Lecture 13 Conditional probability 8 9

Lecture 14 Conditional probability 10 11

Lecture 15 Permutation

Lecture 16 Combinatoric

Lecture 17 Permutation with duplicate objects

Lecture 18 Question 1

Lecture 19 Question 2

Lecture 20 Question 3

Lecture 21 Question 4

Lecture 22 Question 5

Lecture 23 Question 6

Section 2: Random variable, PDF and CDF

Lecture 24 Random variable

Lecture 25 Discrete 1

Lecture 26 Discrete 2

Lecture 27 Discrete 3

Lecture 28 Discrete 4

Lecture 29 Continuous 1

Lecture 30 Continuous 2

Lecture 31 Property of continuous distribution

Lecture 32 CDF and Survival function 1

Lecture 33 CDF and Survival function 2

Lecture 34 Hazard rate

Lecture 35 Hazard rate question 1

Lecture 36 Hazard rate question 2

Lecture 37 Hazard rate question 3

Lecture 38 Relationship between function

Lecture 39 Quiz 1 and 2

Lecture 40 Quiz 3

Lecture 41 Quiz 4

Lecture 42 Quiz 5

Section 3: Expectation and variance

Lecture 43 Expected value

Lecture 44 Expected value of h(x)

Lecture 45 Moments of random variables

Lecture 46 Quiz Question 1

Lecture 47 Quiz Question 2

Lecture 48 Variance

Lecture 49 Variance question 1

Lecture 50 Variance question 2

Lecture 51 Variance property

Lecture 52 Moment generetion function

Lecture 53 Moment generation function

Lecture 54 Moment generation function

Lecture 55 Probability generation function

Lecture 56 Percentile

Lecture 57 Percentile

Lecture 58 Percentile discrete

Lecture 59 Mode

Lecture 60 Minimun of variables

Lecture 61 Minimun of variables

Lecture 62 Maximun of variables

Lecture 63 Input and Output

Lecture 64 Important notes

Lecture 65 Quiz Question 1

Lecture 66 Quiz Question 2

Lecture 67 Quiz Question 3

Lecture 68 Quiz Question 4

Section 4: Frequently use discrete distribution

Lecture 69 Discrete Uniform Distribution

Lecture 70 Discrete Uniform Distribution

Lecture 71 Binomial Distribution

Lecture 72 Binomial Distribution

Lecture 73 Binomial Distribution

Lecture 74 Binomial Distribution Expect and variance

Lecture 75 Binomial Distribution question 1

Lecture 76 Binomial Distribution question 2

Lecture 77 Poisson Distribution

Lecture 78 Poisson Distribution question 1 2 3

Lecture 79 Poisson Distribution question 4

Lecture 80 Poisson Distribution property

Lecture 81 Geometric Distribution

Lecture 82 Geometric Distribution question 1 2

Lecture 83 Negative binomial Distribution

Lecture 84 Negative binomial expect and variance

Lecture 85 Negative binomial question 1

Lecture 86 Hypergeometric Distribution

Lecture 87 Multinomial Distribution

Lecture 88 Question 1

Lecture 89 Question 2

Lecture 90 Question 3

Lecture 91 Question 4

Section 5: Frequently use continuous distribution

Lecture 92 Uniform Distribution

Lecture 93 Uniform Distribution question 1

Lecture 94 Normal Distribution

Lecture 95 Z-table

Lecture 96 Z-table question

Lecture 97 Z-table question

Lecture 98 Z-table question

Lecture 99 Normal approximation

Lecture 100 Normal approximation question 1

Lecture 101 Normal approximation question 2

Lecture 102 Normal approximation question 3

Lecture 103 Central Limit Theorem

Lecture 104 Exponential distribution

Lecture 105 Exponential Distribution question 1

Lecture 106 Exponential Distribution question 2

Lecture 107 Exponential lack of memory property

Lecture 108 Exponential lack of memory property

Lecture 109 Exponential lack of memory property

Lecture 110 Link between Poisson and Exponential distribution

Lecture 111 Link between Poisson and Exponential distribution

Lecture 112 Minimun of collection of exponential distribution

Lecture 113 Exponential Distribution quesiton 1 2

Lecture 114 Exponential Distribution question 3

Lecture 115 Gamma Distribution

Lecture 116 Quiz question 1

Lecture 117 Quiz question 2 and 3

Lecture 118 Quiz question 4

Section 6: joint, marginal and conditional distribution

Lecture 119 Joint Distribution

Lecture 120 Joint Distribution

Lecture 121 Joint Distribution question

Lecture 122 Double integration

Lecture 123 Double integration question

Lecture 124 Double integration question

Lecture 125 Double integration question

Lecture 126 Double integration question

Lecture 127 Double integration question

Lecture 128 Expectation of H(X)

Lecture 129 Expectation of H(X) question

Lecture 130 Expectation of H(X) question

Lecture 131 Expectation of H(X) question

Lecture 132 Joint Distribution question

Lecture 133 Marginal Distribution

Lecture 134 Marginal Distribution question

Lecture 135 Marginal Distribution question

Lecture 136 Marginal Distribution question

Lecture 137 Independent of event

Lecture 138 Independent of event question

Lecture 139 Independent of event question

Lecture 140 Independent of event question

Lecture 141 Independent of event question

Lecture 142 Expect value from joint and marginal

Lecture 143 Expect value from joint and marginal

Lecture 144 Conditional probability

Lecture 145 Conditional probability

Lecture 146 Conditional probability

Lecture 147 Conditional probability

Lecture 148 Double expectation rule

Lecture 149 Double expectation rule

Lecture 150 Double expectation rule

Lecture 151 Double expectation rule

Lecture 152 Covariance, Correlation and Variance

Lecture 153 Covariance and Correlation question

Lecture 154 Covariance and Correlation question

Lecture 155 Covariance and Correlation question

Lecture 156 Moment generation function of joint

Section 7: Transformation of variable

Lecture 157 one-to-one transformation

Lecture 158 one-to-one transformation

Lecture 159 one-to-one transformation

Lecture 160 one-to-one transformation

Lecture 161 one-to-one transformation

Lecture 162 Two-to-one transformation

Lecture 163 Two-to-one transformation

Lecture 164 Order statistic

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