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  1. Course Materials. Required Workbook: Stat 100 Incomplete Lecture Notes Workbook by Ellen Fireman, Karle Flanagan, and John Marden. ... Exam 1: Wednesday Feb. 8th from 7-8:30pm; Exam 2: Wednesday March 8th from 7-8:30pm; ... All exams for both the in person and online sections will be in person. If you are not on the UIUC campus, please contact ...

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

    STAT 100 Syllabus Spring 2020 (View PDF) Instructor Contact Information. L2 (In-Person) & KF (Online) Instructor: Karle Flanagan. Email: stat100flanagan@gmail.com. L1 (In-Person) Instructor: Kelly Findley. Email: kfindley@illinois.edu. Course Webpage. https://go.illinois.edu/stat100. You can also google “stat 100” :) Course Materials.

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

    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 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.