STATS 101C
Introduction to Statistical Models and Data Mining
Description: Lecture, three hours; discussion, one hour. Enforced requisite: course 101B. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2019 - Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture). He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours. Homework is book problems, which generally aren't bad. Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
Fall 2019 - Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture). He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours. Homework is book problems, which generally aren't bad. Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
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