Srbija Posted August 20, 2023 Share #1 Posted August 20, 2023 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 For actuaries, actuary students and those who are interested in becoming an actuary Hidden Content Give reaction to this post to see the hidden content. 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