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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. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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. Like Stat 100, it uses a partially completed notebook that you fill in during class.

  7. Statistical computing using a statistical package such as R or a spreadsheet. Topics to be covered include data summary and visualization, study design, elementary probability, categorical data, comparative experiments, multiple linear regression, analysis of variance, statistical inferences and model diagnostics.

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