A background on probability is assumed. This includes elementary probability and random variables.
Students are expected to acquire and develop the following habilities:
1. Basic general knowledge on the subject.
2. Capacity to analyse specific problems and synthesise acquired knowledge.
3. Specific problem solving.
4. Capacity to apply knowledge on practical situation.
5. Capacity to learn and individually broaden knowledge.
1. Data, data representation and probability review
2. Random variables and probability distributions
3. Central Limit Theorem and Gaussian random variables
4. Law of Large Numbers and basic parametric estimation
5. Statistical tests
6. How to construct a test statistic
7. Goodness of fit tests
8. More on tests: z and t tests among others
9. Anova Tests
10. Non Parametric Statistic Tests
11. Parameter estimation - Maximum Likelyhood
12. Least Squares methods
The subject is structured into one weekly session of 1.5h of theory thought in the traditional classroom lesson method plus an additional 1.5h session in which examples, cases and topic extensions will be developed and studied. These sessions will also be used to introduce students to statistical software.
Evaluation shall rely on class attendance and participation and a set of written assignments to be timely delivered. Students with low class attendance or not delivering the assignments shall undergo a final exam.
The final mark will depend on the quality of the delivered papers, a minimal amount of class attendance and participation.
- R.A.Jonhson, G.K.Bhattacharyya, Statistics, Principles and Methods, Wiley&Sons.
- G.Cowan, Statistical Data Analysis, Oxford Science Publications.
- F.James, Statistical Methods in Experimental Physics, World Scientific.
No text is proposed.