Week 3

Univariate analysis

Important

• Session I Sophie Germain (2017) Monday 16h-17h30
• Session II Sophie Germain (2011) Friday 16h45-18h15
• Calendar

Blackboard

We reviewed the toolkit of univariate analysis

• Lexicon
• Categorical samples
  • Counts, Contingency tables, barplots, colplots
• Numeric samples
  • Numerical summaries for location and scale
  • Cumulative distribution functions and Quantiles functions
  • Quantile-quantile plots
  • Histograms

Labs

We went (briefly) through the lab:

• Lab 4 Univariate numeric variables

NoteTake out

The last part of Lab 4 is about attempts to comply with the DRY principle. The production of tables and plots derived from column-wise analysis should be automated as much as possible.

The lab dedicated to categorical samples will be surveyed in week 4.

• Lab 5 Univariate categorical variables

We also relied on the previous labs

• Lab 1 - Introduction to R and RStudio
• Lab 2 - Introduction to Data Visualization using Gapminder dataset
• Lab 3 - Working with dplyr

You should have surveyed the solutions.

Further work

Review the content of the two labs. Work out every part you do not already know. Report an issue if you are unhappy with the proposed solutions/hints.

Further reading

• Bin Yu and Rebecca Barter, Veridical Data Science
• R for data science
• Advanced R
• Cours de Statistique Fondamentale
  • Chap Estimation de densité
  • Chap Test de Kolmogorov-Smirnov

Logistics

: you will work with the R programming language in this course.

You need either to install R, RStudio and VS Code on your computer, or to use your posit-cloud account.

Install Quarto on your computer to render the .qmd files.

Please follow the instructions here to install R, RStudio, VS Code, and Quarto or to access posit-cloud.

Please activate your ENT account (follow the instructions on Moodle). You will be able to access the PostGres server.

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