Grade distributions are collected using data from the UCLA Registrar’s Office.
For Stats 101A with Professor Gould, I feel as if I learned quite a bit about regression while not having to worry about my grade too often during the class. Professor Gould provided us with real-world examples in which the skills we acquired in this class could be used. I found this topic fascinating from the start of the quarter, and Professor Gould only increased my love for regression.
While some lectures were more of the standard fare (i.e. him lecturing, us taking notes), there were a few interactive activities to ensure we were understanding the material. Moreover, he would sometimes ask us to collaborate and work in R toward solving a problem he posed, giving us greater practice and thus greater understanding of regression. The homework assignments were appropriately assigned for the most part, with none of them taking an egregiously absurd amount of time. The midterm was an extremely fair assessment of the class up to that point; there were no tricks or convoluted questions.
At the end of the quarter, things became hectic with the coronavirus, but Professor Gould adapted accordingly and made the grading scheme for the final and the class more lenient as a result. This professor genuinely cared about students learning the material and ensuring that everything made sense while also grading favorably on exams.
TL;DR: Professor Gould is a wonderful professor with whom I would take another class; he cares about the students understanding the material while also making grades reasonable. However, if you are a stats major/minor, you should not just be caring about the grade because this material is extremely relevant to potential future jobs, and I believe Professor Gould established a great foundation upon which I can expand.
Given the extenuating circumstances that the coronavirus has put UCLA in this quarter, I could not have been more grateful to have as understanding and accommodating of a professor as Dr Gould. He was very lenient with grading on the final project/exam, telling us to complete as much of the final project as possible and that we'd only be graded on those problems.
The structure of this class was very manageable to begin with, and he focused a lot on solidifying our intuition of the concepts. We have weekly homework assignments where you do modeling in R, but none of them took more than a few hours. There was only one midterm, which was scheduled before multiple linear regression so we were only tested on simple linear regression concepts that are fairly straightforward. From the beginning of quarter, the final was just going to be a take-home project that ended up being like just another homework assignment.
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