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Jan 28, 2025
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MA 2631 - Probability II4 lecture hours 0 lab hours 4 credits
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• be able to understand and apply continuous probability distributions to appropriate probability situations
• be able to understand, derive and use continuous probability density functions, conditional probability density functions, marginal functions, and moment-generating functions
• be able to understand, derive, and use continuous joint probability functions
• be able to understand the meaning and relevance of, and use, measures of dispersion for continuous multi-variable probability distributions
• be able to understand, calculate, and use covariance
• be able to understand, calculate, and apply to correlation coefficient appropriate situations
• be able to perform transformations of continuous random variables
• be able to form and use linear combination of random variables with respect to calculation of probabilities and moments
Course Topics
• Continuous probability distributions such as the Gaussian (normal) distribution, Student-t, chi-squared, F, exponential, gamma, beta, etc.
• Continuous probability density functions
• Continuous cumulative density functions
• Continuous moment-generating functions
• Continuous 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 considered
• 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
Ron Jorgensen
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