Introduction to Statistical Models and Data Mining
Lecture, three hours; discussion, one hour. Enforced requisite: course 101A. Recommended: 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.
Review Summary
- Clarity
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10.0 / 10
- Organization
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6.7 / 10
- Time
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0-5 hrs/week
- Overall
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10.0 / 10
Reviews
great class, nice prof. His lectures are very engaging
Good professor, enjoyed the Kaggle competition
Displaying all 2 reviews
Course
Grading Information
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Has a group project
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Attendance required
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1 midterm
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Finals week final
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50% recommend the textbook