STATS 101B
Introduction to Design and Analysis of Experiment
Description: Lecture, three hours; discussion, one hour. Enforced requisite: course 101A. Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Spring 2020 - Literally the most useless professor I've ever had. Never again. He didn't tell us what the final would be like until after the P/NP deadline. Told us the format of the final 2 days before the day of the final. On top of that, doesn't reply to emails even though we're doing the entire quarter ONLINE. Unacceptable. Don't take this class with him if you can help it.
Spring 2020 - Literally the most useless professor I've ever had. Never again. He didn't tell us what the final would be like until after the P/NP deadline. Told us the format of the final 2 days before the day of the final. On top of that, doesn't reply to emails even though we're doing the entire quarter ONLINE. Unacceptable. Don't take this class with him if you can help it.
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Most Helpful Review
Fall 2021 - This review is for Stats 101C since there's isn't a 101C page for her. I found her lectures to be very dry so I didn't go and just ended up reading the textbook which was far more helpful. Her class is pretty easy. The grading scheme is 25% homework, 35% midterm (24 hour take home), and 40% final exam project. The final exam project was a competition on Kaggle along with a project report. The grading on the homework and midterm were extremely lenient and I did good on all of those. However we never got a grade for our final exam and I somehow ended up with a B+. There were 6 homework assignments, each assignment being only 2-3 multipart problems but it was easy to do since the problems were from the textbook. I think professor is nice. She recorded all the lectures and posted her slides so you don't need to go to class. I don't think I learned a lot in this class so I don't know if I'd take another class with her again.
Fall 2021 - This review is for Stats 101C since there's isn't a 101C page for her. I found her lectures to be very dry so I didn't go and just ended up reading the textbook which was far more helpful. Her class is pretty easy. The grading scheme is 25% homework, 35% midterm (24 hour take home), and 40% final exam project. The final exam project was a competition on Kaggle along with a project report. The grading on the homework and midterm were extremely lenient and I did good on all of those. However we never got a grade for our final exam and I somehow ended up with a B+. There were 6 homework assignments, each assignment being only 2-3 multipart problems but it was easy to do since the problems were from the textbook. I think professor is nice. She recorded all the lectures and posted her slides so you don't need to go to class. I don't think I learned a lot in this class so I don't know if I'd take another class with her again.
Most Helpful Review
Winter 2022 - *This is a review for STATS 101A, taken Winter 2022* Professor Vazquez is really nice and funny. He breaks things down in a very easy to understand manner and is overall a fairly good professor. He outlines his class very clearly about what you will learn and you will come out of this class with a very good foundation for regression and modeling techniques. As a former stats minor (who dropped because of 100B), I do think this class was very important and interesting. The grading, on the other hand, leaves much to be desired. The breakdown is as such: 25% Homework, 30% Midterm Exam, 30% Final Exam, and 15% Final (Group) Project. All the homeworks are done in RMarkdown and are really straightforward. It is quite easy to get 100s on all of them, just don't make silly mistakes. Grading for these is quite lenient as well. The mean on the midterm was a 73 even though the majority of the class felt they did really well. He lulls you into a false sense of security, because the exam itself is not hard if you pay attention in class and do the homeworks (pretty much exactly the same as these) - he does grade quite strictly though so you will lose points if you aren't clear. The final exam was just as "easy" although this time the class learned from their mistakes and the mean was 89. The final group project was on League of Legends - we were given a dataset of 25000 league games and were supposed to create a model to determine what factors are most important in winning gold in the game. Not that interesting imo, and he grades harshly here as well but you don't get a rubric or know what you missed out on. Overall, grading is terrible, but you get a good foundation of regression.
Winter 2022 - *This is a review for STATS 101A, taken Winter 2022* Professor Vazquez is really nice and funny. He breaks things down in a very easy to understand manner and is overall a fairly good professor. He outlines his class very clearly about what you will learn and you will come out of this class with a very good foundation for regression and modeling techniques. As a former stats minor (who dropped because of 100B), I do think this class was very important and interesting. The grading, on the other hand, leaves much to be desired. The breakdown is as such: 25% Homework, 30% Midterm Exam, 30% Final Exam, and 15% Final (Group) Project. All the homeworks are done in RMarkdown and are really straightforward. It is quite easy to get 100s on all of them, just don't make silly mistakes. Grading for these is quite lenient as well. The mean on the midterm was a 73 even though the majority of the class felt they did really well. He lulls you into a false sense of security, because the exam itself is not hard if you pay attention in class and do the homeworks (pretty much exactly the same as these) - he does grade quite strictly though so you will lose points if you aren't clear. The final exam was just as "easy" although this time the class learned from their mistakes and the mean was 89. The final group project was on League of Legends - we were given a dataset of 25000 league games and were supposed to create a model to determine what factors are most important in winning gold in the game. Not that interesting imo, and he grades harshly here as well but you don't get a rubric or know what you missed out on. Overall, grading is terrible, but you get a good foundation of regression.
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