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Learning from Data: R programming
Learning from Data: R programming
  • Syllabus
  • Schedule
  • Content
  • Examples
  • Exercises
  • Assignments
  • Resources
Overview
  • Readings, lectures, etc.
Course content
  • **Before we start**
  • 1: Exploratory Data Analysis
  • 2: Data Science Basics
  • 3: Sampling; Probability Distributions
  • 4: Confidence Intervals; reshape data
  • 5: Inferential Statistics
  • 6: Hypothesis Testing
  • 7: Regression Models
  • 8: Workshop on regression models
  • 9. Regression for prediction; regression diagnostics
  • 10. Group Case Presentations

Workshop on regression

Read before class on Tuesday, March 30, 2021
  • Readings

Readings

  • ModernDive Chapter 10.2-10.3

Last updated on March 2, 2021

BIT: Learning from Data: R programming (Spring 2021)
Burkina Faso Institute of Technology   

Kostis Christodoulou   
kchristodoulou@london.edu

all days    all times
Virtual, hybrid

All content licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content 2021 Kostis Christodoulou

This site created with the Academic theme in blogdown and Hugo, with template modifications by Andrew Heiss. Interactive tutorials were developed by Mohannad Shaheen, LBS MAM 2020.

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