Code Monkey home page Code Monkey logo

2017-05-03-rstatsintro-neu's Introduction

Beginner's statistics in R

This course was set up and taught by Meena Choi and Laurent Gatto in the frame the of the May Institute, at the Northeastern University, Boston, MA from 3 to 5 May 2017. The theoretical lectures were taught by Olga Vitek.

Suggested reading

Schedule and material

Material day 1

Day Time Content
3 May 1:30 - 3:00pm Keynote: Olga Vitek
3:00 - 3:30pm Refreshments
3:30 - 5:00pm R basics and RStudio
5:00 - 6:00pm R markdown

Material day 2

Day Time Content
4 May 8:00 - 9:00am Bring your own data
9:00 - 10:30am Data Exploration
10:30 - 11:00am Refreshments
11:00 - 12:30pm Visualisation
12:30 - 13:30pm Lunch break
13:30 - 3:00pm Lecture: basic stats
3:00 - 3:30pm Refreshments
3:30 - 5:00pm Basic stats, randomisation, error bars
5:00 - 6:00pm Extra practice

Material day 3

Day Time Content
5 May 8:00 - 9:00am Bring your own data
9:00 - 10:30am Lecture: sample size, linear regression, categorical data
10:30 - 11:00am Refreshments
11:00 - 12:30pm Statistical hypothesis testing
12:30 - 13:30pm Lunch break
13:30 - 3:00pm Sample size, categorical data hands-on
3:00 - 3:30pm Refreshments
3:30 - 5:00pm Linear models and correlation
5:00 - 6:00pm Extra practice

Lecture slides are available in the Slides directory.

Link to more teaching material

License

This material, unless otherwise stated, has been adapted from our is made available under the Creative Commons Attribution license.

You are free to:

  • Share - copy and redistribute the material in any medium or format
  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  • Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Credit

Some of the material from day 1 and 2 has been adapted from the Data Carpentry R lessons (see references in the respective sections), which are licensed under CC-BY.

2017-05-03-rstatsintro-neu's People

Contributors

laurentgatto avatar

Stargazers

 avatar  avatar

Watchers

 avatar  avatar  avatar

Forkers

fabianamoresi

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.