Dec 08, 2022
 HELP 2021-2022 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 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