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Introduction to statistic with R

        

ContextTraining course at Forschungszentrum Juelich                                   
Organisation5 * 4 hours of lecture and exercice with R
Material

Slides   Exercices, data and R-scripts

    

Content

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: De finition and main properties (bias, convergence)
  • Punctual estimate (maximum likelihood estimation, Bayesian estimation)
  • Con fidence and credible intervals
  • Information criteria
  • Test of hypothesis
  • Parametric clustering

      

Books
TW Anderson and JD Finn. The statistical analysis of data, Springer, 1996.
ID Montgomery and G Runger. Applied Statistics and Probability for Engineers, Wiley, 2010.
P Congdon. Bayesian statistical modelling (2nd edition), Wiley, 2006.

           

Websites and videos