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5.0
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1,000
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
STAT 4351 Sam Efromovich | |||||
B- | |||||
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| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
STAT 4351 Sam Efromovich | |||||
B- | |||||
Grades: 658
Median GPA: B+
Mean GPA: 3.074
2.3
Professor rating
4.1
Difficulty
16
Ratings given
36%
Would take again
Probability
STAT 4351
School of Natural Sciences and Mathematics
Sample spaces, probability of events, Kolmogorov's axioms, independence and dependence, Bayesian methodology. Discrete and continuous random variables. Probability distributions, mass functions and densities of univariate and multivariate random variables. Expected values, variances, moment generating functions, covariances and related issues. Probability inequalities. Special probability distributions and special probability densities. Functions of random variables, distribution function techniques, transformation techniques for one and several variables, moment-generating techniques. The law of large numbers, the central limit theorem and classical sampling distributions. Proofs of all main results. Practical examples illustrating the theory. The course can be used as a preparation for the first (Probability) actuarial exam. 3 credit hours.
Prerequisite: MATH 3351 or equivalent.
Offering Frequency: Each year
Grades: 1,025
Median GPA: B+
Mean GPA: 3.003
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Grades: 658
Median GPA: B+
Mean GPA: 3.074
2.3
Professor rating
4.1
Difficulty
16
Ratings given
36%
Would take again
Probability
STAT 4351
School of Natural Sciences and Mathematics
Sample spaces, probability of events, Kolmogorov's axioms, independence and dependence, Bayesian methodology. Discrete and continuous random variables. Probability distributions, mass functions and densities of univariate and multivariate random variables. Expected values, variances, moment generating functions, covariances and related issues. Probability inequalities. Special probability distributions and special probability densities. Functions of random variables, distribution function techniques, transformation techniques for one and several variables, moment-generating techniques. The law of large numbers, the central limit theorem and classical sampling distributions. Proofs of all main results. Practical examples illustrating the theory. The course can be used as a preparation for the first (Probability) actuarial exam. 3 credit hours.
Prerequisite: MATH 3351 or equivalent.
Offering Frequency: Each year
Grades: 1,025
Median GPA: B+
Mean GPA: 3.003
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