Course of
Data Analysis and Exploration
Diary of the course 2010/11
17/9. Introduction to the course. Presentation of some statistical ideas (possibly, some notes available shortly).
20/9. Introduction to the use of R R code used.
23/9. Matrices and data frames. First graphical commands R code used.
28/9. Using datasets already within R. Some more graphical commands R code used. Linear models and maximum likelihood estimation.
30/9. Orthogonal projection. Application to the estimation in linear models. variance of the estimator; Gauss-Markov theorem: see slides shown in class (with some improvements). Introduction to linear models in R R code used.
5/10. Multiple regression in R.
7/10. Confidence intervals. Quick review of theory and some examples; computing confidence intervals in R.
12/10. Tests of hypotheses. Quick review of general ideas and theory. Example of the t-test. First tests of hypothesis in R.
14/10. Tests of hypotheses in the linear model. Interpretation of the output of lm() in R. How to perform other tests within the linear model.
26/10. lm() when predictor variables are qualitative. 1-way and 2-way analysis of variance.
28/10. Resolution of some problems in class. Multivariate normal distribution.
2/11. Theory of F tests in linear models. 2-way analysis of variance in R.
4/11. Analysis of covariance. Polynomial regression.
9/11. Regression diagnostics.
11/11. Model selection and cross-validation. See also the slides shown in class
16/11. Simulation of linear models.
18/11. An introduction to generalized linear models and logistic regression. An example of logistic regression in R. Notes by German Rodriguez (thanks) on logistic regression.
23/11. Other examples of logistic and Poisson regression in R.
25/11. Conclusion of examples on Poisson regression in R. Analysis of fit of different models.
30/11. Logistic regression applied to simulated data. Theory of principal component analysis (short summary of principal component analysis, for the moment in Italian) .
2/12. Examples of principal component analysis.
9/12. Linear discriminant analysis.
14/12. Linear discriminant analysis; other examples.
16/12. Discussion of problems, mainly on examples of anova.