Introduction to Probability and Statistics 1: Probability
Lecture, three hours; discussion, one hour. Requisite: course 32B. Highly recommended: course 61 or 70. Not open to students with credit for course 170A, Electrical and Computer Engineering 131A, or Statistics 100A. Introduction to probability theory with emphasis on topics relevant to applications. Topics include discrete (binomial, Poisson, etc.) and continuous (exponential, gamma, chi-square, normal) distributions, bivariate distributions, distributions of functions of random variables (including moment generating functions and central limit theorem). P/NP or letter grading.
Review Summary
- Clarity
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1.7 / 10
- Organization
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3.3 / 10
- Time
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10-15 hrs/week
- Overall
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3.3 / 10
Reviews
Palmer's lectures were pretty disorganized. He spent a lot of time going over the derivation of each probability distribution (a lot of calculus for a stats class), and not very much time explaining how and when to apply the distributions. That was the opposite to how the exams were, which was almost no derivation but required advanced understanding of how to apply distributions. The exams were moderately difficult (but that meant an acceptable curve). The homework was weekly problem sets, which weren't too difficult, but they weren't a good preparation for the exams.
Overall, Palmer's lecture style is not that engaging - he speaks falteringly and anxiously. It seemed like this was his first time teaching a class, but maybe once he gets more familiar with lecturing it will improve.
His teaching style was not the greatest, but his exams were rather straightforward. He is also a very caring prof, especially if you come in for office hours.
Displaying all 2 reviews
Course
Grading Information
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No group projects
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Attendance not required
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2 midterms
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Finals week final
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100% recommend the textbook
Previous Grades
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