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  1. All students are either enrolled in the in person section of Stat 100 OR the online version. Details are found here: In Person Section L2: Mon/Wed/Fri from 10:00AM-10:50AM. Lincoln Hall Theater. Online Section O1: Asynchronous lecture videos on Canvas. No in-person locations.

  2. karleflanagan.github.io › stat100S20 › pagesSTAT 100 - GitHub Pages

    See the University Student Code of Conduct. The Stat 100 Team has a group of highly trained students who know every trick in the book for catching cheaters. We hand grade each of the exams and have multiple versions of all exams. They may look identical at first glance, but they are not.

  3. The Stat 100 Team has a group of highly trained graders who know every trick in the book for catching cheaters. We hand grade each of the exams and have multiple versions of all exams. They may look identical at first glance, but they are not.

  4. stat100website.web.illinois.eduSTAT 100

    In Stat 100, we use statistics to research a topic we're all interested in - ourselves. We collect data on ourselves through anonymous surveys, largely on the sort of social questions on which students have shown intense interest.

  5. stat100netmath.web.illinois.edu › syllabusStat 100 NetMath Syllabus

    1. Public Stat100 Netmath website. Exam Study Guides, Data Program, p-value calculator and General Course Information are posted on the Public Course Website. https://stat100netmath.web.illinois.edu. 2. LON-CAPA site. All homework, surveys and bonus work are submitted and graded immediately on Lon Capa.

  6. This class covers the same statistical content as Stat 100, but you learn Python programming and get experience analyzing real data. We are dedicated to helping students on the U of I campus learn and understand basic statistical concepts so they can understand and make sense of things that are interesting or important to them.

  7. STAT 100 Statistics credit: 3 Hours. First course in probability and statistics at a precalculus level; emphasizes basic concepts, including descriptive statistics, elementary probability, estimation, and hypothesis testing in both nonparametric and normal models.

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