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  1. 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.

  2. STAT 100 Office Hours will be offered each week Monday, Tuesday, Wednesday, Thursday, Friday from 2pm-5pm in the Computer Applications Building (CAB). The room number is 171 and CAB is on 6th and Springfield. Office hours start Week 2 :) We also have online office hours Monday and Wednesday evenings from 8-10pm on zoom!

  3. 8 sie 2024 · Integrated game browser with one-click download and install procedure. Requires 1.5GB of free hard drive space with PUBG Mobile installed. Optimized for use on modern Windows OS (7, 8 and...

  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. 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.

  6. 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.

  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.