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  1. This notebook is the only thing that’s required to purchase for Stat 100! You will use this notebook each week during class! Required Calculator: You’ll also need a calculator for exams.

  2. 1 Class Times for Spring 2022 3. Stat 100 Syllabus: Spring 2022 Course Info. Statistics 100 (Stat 100), 3 credit hours Meets the Quantitative Reasoning I general education requirement. Instructor Contact Information. L2 (In-Person) & O1 (Online) Instructor: Karle Flanagan. Email: stat100@illinois.edu. O2 (Online) Instructor: Jonas Reger.

  3. 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.

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

    Course Webpage. https://go.illinois.edu/stat100. You can also google “stat 100” :) Course Materials. Required Workbook: Stat 100 Incomplete Lecture Notes Workbook. Either the Fall 2019 OR Spring 2020 edition by Ellen Fireman, Karle Flanagan, and John Marden. Available at the Illini Union Bookstore for $35.

  5. stat100website.web.illinois.eduSTAT 100

    Students tell us that after Stat 100 they: Read the newspaper in a new way, without their eyes glazing over when they see quantitative information. Know what questions to ask in evaluating studies and surveys.

  6. 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.

  7. A: For a more advanced statistics course, we highly recommend taking Stat 200! This class starts with a quick review of Stat 100 material and then moves on to talk about things like hypothesis testing, power, multiple regression, ANOVA, etc.

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