Jun 07, 2023
 HELP 2019-2020 Undergraduate Academic Catalog [ARCHIVED CATALOG] Print-Friendly Page (opens a new window)

# AC 2303 - AS Lab I: Applications of Probability

3 lecture hours 0 lab hours 3 credits

Course Description
This course covers the applications of the probability concepts and theory that students learned in MA 2630 and MA 2631. The students will apply the tools they learned in these two probability courses to problems encountered in actuarial science for assessing risk.  (prereq: MA 2630  and MA 2631 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• Calculate probabilities of mutually exclusive events
• Calculate probabilities using addition and multiplication rules
• Calculate probabilities using combinations and permutations
• Calculate conditional probabilities
• Use Bayes theorem to calculate probabilities
• Apply probability mass, probability density, and cumulative distribution functions
• Calculate expected value, mode, median, variance, and standard deviation
• Calculate probabilities using probability generating and moment generating functions
• Perform calculations concerning joint probability and probability density functions, and cumulative distribution functions
• Determine conditional and marginal probability and probability density functions, cumulative distribution functions
• Calculate moments for joint, conditional and marginal random variables
• Apply joint moment generating functions
• Calculate variance and standard deviation for conditional and marginal probability distributions
• Calculate joint moments, such as covariance and correlation coefficient

Prerequisites by Topic
• Combinatorial probability
• Probability rules
• Conditional probability
• Total probability law and Bayes theorem
• Discrete random variables
• Binomial, Poisson, geometric, and other discrete distributions
• Continuous random variables
• Normal, chi-Square, t-distribution and other continuous distributions
• Mixed random variables
• Marginal distribution
• Moment generating functions
• Measures of central tendency and dispersion

Course Topics
• Application of probabilities of mutually exclusive events
• Applications of probabilities using addition and multiplication rules
• Applications of probabilities using combinations and permutations
• Applications of conditional probabilities
• Applications of Bayes theorem to calculate probabilities
• Probability mass, probability density, and cumulative distribution function applications
• Applications of expected value, mode, median, variance and standard deviation
• Calculate probabilities using probability generating and moment generating functions
• Applications of joint probability and probability density functions and cumulative distribution functions
• Applications of conditional and marginal probability and probability density functions, cumulative distribution functions
• Applications of moments for joint, conditional, and marginal random variables
• Applications of variance and standard deviation for conditional and marginal probability distributions
• Applications of joint moments, such as covariance and correlation coefficient

Coordinator
Dr. Yvonne Yaz