1. Knowledge and understanding. Students will achieve a solid understanding of descriptive statistics and of its use in basic and applied research.
2. Application of knowlege and understanding. Students will be able to use the R programming environment to describe simple data structures and to create graphical presentations.
3. Autonomy of judgment. Students will refine critical thinking and autonomy of judgment in relation to data description in technical reports.
4.Communication skills. Students will be able to communicate the results of descriptive data analyses, both by numeric summary statistics and by graphical tools.
5. Learning skills. Students will develop the ability to learn new techniques for data description especially within the R programming environment.
Course contents summary
The course will present basic notions of measurement theory and univariate and bivariate descriptive statistics, with applications to research in psychobiology and cognitive neurosciences. The course also introduces to the R programming environment for statistical analysis and data presentation.
Measurement theory. Precision and accuracy. Data. Univariate distribution. Central tendency and dispersion. Histograms and box-plots. Linear correlation. Regression. Scatterplots and bag-plots. Smoothers. Contingency tables. Association with categories. Multidimensional data structures. Central Limit theorem and the law of large numbers, sampling, confidence intervals. Contemporary debate on statistical testing.
Chiorri, C. (2010). Fondamenti di psicometria. McGraw-HIll. (pp.1-250 e 387-460).
Bruno, N. (2013). Introduzione alla statistica descrittiva con R - Dispensa per il corso di Tecniche di Analisi di Dati I. see personal website of the instructor. (pp. 50).
Venables, W.N., Smith, D.M. and the R Core Team (2012). An introduction to R. http://www.r-project.org/ (optional, recommended for students that do not come to class)
lectures and in-class problems
Assessment methods and criteria
For students that come to class, two written partial take-home exams (short answers), and a brief final exam (oral). Students will solve problems using the R environment and hand in their work during the course. All and only students that have scores on the take-home will be given a grade by this method. Students who do no receive a grade by this method will take an oral exam on the whole program.