Division for Traffic Safety and Reliability

Introduction to statistic with R [ISR]

Context Training course at Forschungszentrum Juelich
Organisation 5 * 4 hours of lecture and exercice with R
Material Slides, exercices, data and R-scripts

Introduction to descriptive and parametric statistics for univariate and multivariate data with the software R.

1.  Descriptive statistic for univariate and bivariate data

  • Repartition of the data (histogram, kernel density, distribution function)
  • Order statistic and quantile
  • Statistics for location and variability, boxplot
  • Scatter plot, QQplot
  • Covariance and correlation
  • Simple linear regression

2.  Descriptive statistic for multivariate data

  • Least squares and multiple linear and non-linear regression models
  • Principal component analysis and principal component regression
  • Clustering methods (K-means, hierarchical, density-based)
  • Linear discriminant analysis
  • Bootstrap technique
  • Artificial neural network

3.  Parametric statistic

  • Notion of Likelihood
  • Estimator: Definition and main properties (bias, convergence)
  • Punctual estimate (maximum likelihood estimation, Bayesian estimation)
  • Confidence and credible intervals
  • Information criteria
  • Test of hypothesis
  • Parametric clustering


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