Degree | Type | Year | Semester |
---|---|---|---|
2503852 Applied Statistics | OB | 2 | 1 |
It is recommended that you have passed the Calculation, Probability and Inference-1 subjects.
In this course, the concept of Inference must be based fundamentally, that is, how the Statistics quantifies the uncertainty of the information extracted from the data. The concepts of Modeling, Estimate and Swing of Adjustment will be introduced.
The different methods of estimation will be studied, especially the maximum veracity method, and the fundamental properties of the estimators: Invariantness, sufficiency, efficiency, bias, quadratic mean error and asymptotic properties.
Topic 1: Modeling and Inference.
• Estimation methods: moments, maximum likelihood, minimum squares. Principle of invariance. Calculation of moments and generating functions.
• Comparison of estimators: Bias and half quadratic error. Consistency and asymptotic normality.
• Cramér-Rao Fisher and Cota information. Sufficiency and Efficiency.
• Exponential models. Numerical methods for estimation.
Topic 2: Contrasts of hypotheses
• Fisher's exact theory. Type of error Null and alternative hypothesis.
• Motto by Neyman and Pearson. Feasibility tests reasonably.
• Validation of the model and goodness of adjustment. Pearson Tests, Kolmogorov-Smirnov, Jarque Bera. Graphics tools: PP-plot and QQ-plot.
Topic 3: Asymptotic theory.
• Properties of convergence in probability and distribution.
• Slutsky theorem. The delta method.
• Asymptotic distribution of the maximum veracity estimator.
• Asymptotic distribution of the scoring, verifiability and Wald test results.
Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
The subject is structured from theoretical classes, problems and practices. The follow-up of the subject must be present, but it will be necessary to extend the teacher's explanations with the student's autonomous study, with the support of the reference bibliography.
The problem class will be devoted to the resolution oriented of some proposed problems. Students' participation in problem classes will be especially valued. In practical classes, Excel and R. software tools will be introduced. You will have to deliver some practical work.
The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Practical work with computer tools | 30 | 1.2 | 1, 2, 5, 6, 12 |
Practices | 12 | 0.48 | 1, 2, 6, 12 |
Problems | 14 | 0.56 | 2, 3, 5, 6, 7 |
Theory | 26 | 1.04 | 1, 2, 3, 11, 4, 5, 6, 10, 9, 7, 13, 12, 14 |
Type: Supervised | |||
Tutorials | 5 | 0.2 | 10, 9 |
Type: Autonomous | |||
Study and think problems | 30 | 1.2 | 11, 5, 6, 13 |
The subject will be assessed with practice assignments, problem controls, practices, and exams. The examinations can only be recovered together at the end, as long as the student has previously passed a 3 in each partial. Work in general is not recovered.
Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Final exam | 40% | 8 | 0.32 | 2, 3, 11, 8, 4, 5, 6, 10, 9, 13, 14 |
Partial exam | 35% | 5 | 0.2 | 2, 8, 4, 6, 10, 9, 13, 14 |
Practices (deliveries, controls) | 25% | 20 | 0.8 | 1, 2, 3, 11, 8, 4, 5, 6, 10, 9, 7, 13, 12, 14 |
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