Lecture, three hours; discussion, one hour. Enforced requisite: course 100B or Mathematics 170S. Theory of linear models, with emphasis on matrix approach to linear regression. Topics include model fitting, extra sums of squares principle, testing general linear hypothesis in regression, inference procedures, Gauss/Markov theorem, examination of residuals, principle component regression, stepwise procedures. P/NP or letter grading.

Review Summary

Clarity
6.7 / 10
Organization
8.3 / 10
Time
10-15 hrs/week
Overall
6.7 / 10

Reviews

    Quarter Taken: Fall 2022 In-Person
    Grade: A-

    This is an incredibly difficult class. You will learn a lot, but the exams and homework are on a different level. I think everybody gets out with a good grade, but it's gonna take a lot of work. I highly recommend going to office hours for help on the homework, and going to class every day is pretty much necessary to getting a good grade. If you miss a single day of class, you'll be really behind on the concepts because Christou literally created the content of this class, which is unavailable on a textbook that guides you, so he's the only guide. Pay attention in class and do your best!

    Quarter Taken: Spring 2023 In-Person
    Grade: A

    Class was extremely difficult, but that makes getting a good grade all the more satisfying for this class

    Quarter Taken: Spring 2023 In-Person
    Grade: A

    Super hard tbh. But also very interesting and filled in the gaps where 101A was lacking. I would suggest taking it at the same time as 101A, which I did not do. Homework is tough and time consuming. If you didn't take 100B with Christou, it might be a bit difficult to adjust to his teaching style. Exams are extremely difficult (there was one where I didn't have a confident final answer for a single question), but he curves generously. Homework is also tough but prof holds a TON of office hours.

Course

Instructor
Nicolas Christou
Previously taught
24S 23F 23Su 23S 22F 22Su 22S 21F 21Su 21S 20F 20Su 20S 19F 19S 18F 18Su 18S 17F 17Su 17S 16F 16Su 15F 14S 13S 12S 12W

Grading Information

  • No group projects

  • Attendance not required

  • 2 midterms

  • Finals week final

  • 33% recommend the textbook