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Nov 23, 2024
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MTH 2620 - Probability for Actuarial Science II4 lecture hours 0 lab hours 4 credits
Course Description
This course is the second course in two-course Probability for Actuarial Science sequence that prepares students for Exam P/Exam I. Topics of discussion include continuous probability distributions such as the Gamma, Beta, Pareto and Weibull distributions. Other course topics are discrete joint probability distributions and continuous joint probability distributions. Expectation results, such as moment-generating functions, are also among the topics of discussion. Substantial focus will also be placed on developing efficient problem-solving skills for all topics covered in the Probability course sequence. (prereq: MTH 2610 ) (quarter system prereq: MA 2630) (coreq: MTH 2130 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Identify and apply the Gamma distribution
- Identify and apply the Beta distribution
- Identify and apply the Pareto distribution
- Identify and apply the Weibull distribution
- Understand, derive, and use joint probability functions (discrete)
- Understand, derive, and use joint probability density functions (continuous)
- Use the Central Limit Theorem
- Understand the meaning and relevance of and use measures of dispersion for continuous multi-variable probability distributions
- Understand and use conditional and marginal probability distributions
- Understand and use moments for joint, conditional, and marginal probability distributions
- Understand and use joint moment generating functions
- Understand, calculate, and use covariance
- Understand, calculate, and apply correlation coefficient to appropriate situations
- Perform transformations of continuous random variables
- Form and use linear combination of random variables with respect to calculation of probabilities and moment
Prerequisites by Topic
- Differentiation of multivariate functions and double integrals
- Discrete and continuous random variables
Course Topics
- Continuous probability distributions such as the gamma, beta, Pareto, and Weibull
- Joint probability functions, joint probability density functions, and joint cumulative density functions
- Conditional and marginal distributions and densities
- Moments for the discrete and continuous joint functions
- Joint moment-generating functions
- Measures of dispersion for multi-variable probability distributions
- Covariance Correlation coefficients
- Transformations of continuous random variables
- Linear combinations of random variables including probabilities and moments
Coordinator
Dr. Yvonne Yaz
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