Statistic using R

The course will address the topics of descriptive statistics, hypothesis tests, linear and non-linear models. Likewise, the elementary concepts and the main tools on Bayesian statistics are provided.

This course is part of a module called AlwaysR (I to V) organized by Cousteau Consultant Group, in such a way that each section is complementary to the following one.

Objectives

• Learn the basic concepts for the use of R as a statistical tool.
• Generate graphs and descriptive statistics, as well as to perform tests, linear, and non-linear models.
• Solve case study based on personal information or information provided in class.
• Confront the frequentist and Bayesian approaches in the estimation of parameters.

Course program

• Day 01:
  Descriptive statistics. Measures of central tendency and dispersion .
  Basic statistical plots.

• Day 02:
  Probability distributions.
  Parametric and non-parametric test.
  Correlation analysis.

• Day 03:
  Linear regresion: Basic concepts, plots, and interpretation.
  Non-linear regression and bootstraping to parameter estimation.
  Some multivariate methods: Principal Components Analysis (PCA) and Hierarchical clustering.

• Day 04:
  Introduction to Bayesian statistics ins R: basic examples.
  Concepts: Prior, Likelihood, Posterior.
  Link R and JAGS (Just Anither Gibbs Sampler) to Bayesian analysis.

• Day 05:
  Bayesian t-test, ANOVA.
  Bayesian linear and non-linear models.