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Type A
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Code |
Competences Specific | | | A2 |
Find, analyze and interpret quantitative and qualitative information of a financial, accounting, economic, social and legal nature that is relevant to the taking of business decisions.
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Type B
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Code |
Competences Transversal | | | B1 |
Learning to learn |
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Type C
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Code |
Competences Nuclear | | | C3 |
Be able to manage information and knowledge |
| | C4 |
Be able to express themselves correctly both orally and in writing in one of the two official languages of the URV |
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Type A
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Code |
Learning outcomes |
| | A2 |
Use and correctly define the concepts and tools of probability (random variables, the population-sample distinction, etc.)
Apply probability-derived tools appropriate to the problem being dealt with.
Solve problems without errors.
Understand and correctly interpret the results of probability and statistical inference.
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Type B
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Code |
Learning outcomes |
| | B1 |
Put into practice the approaches, methods and experiments put forward by the teaching staff in a disciplined fashion.
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Type C
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Code |
Learning outcomes |
| | C3 |
Locate and access information effectively and efficiently.
| | | C4 |
Produce written texts that are appropriate to the communicative situation
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| Topic |
Sub-topic |
| 0. INTRODUCCION TO PROBABILITY |
0.1. Random experiments, sample spaces and events
0.2. Algebra of events
0.3. Probability spaces
0.4. Conditional probability
0.5. Independent events
0.6. Total probability theorem
0.7. Bayes' theorem |
| 1. ONE-DIMENSIONAL RANDOM VARIABLES |
1.1. Discrete random variables
1.1.1. Probability function (quantity). Properties
1.1.2. Distribution function. Properties
1.2. Continuous random variables
1.2.1. Distribution function. Properties
1.2.2. Density function. Properties
CHARACTERISTICS OF RANDOM VARIABLES
1.3. Expectation of a random variable. Properties
1.4. Moments about origin and about mean
1.5. Variance of a random variable. Properties
1.6. Chebitchev inequality |
| 2. PROBABILITY DISTRIBUTION MODELS |
2.1. DISCRETE DISTRIBUTIONS
2.1.1. Discrete uniform distribution
2.1.2. Bernoulli distribution
2.1.3. Binomial distribution
2.1.4. Hypergeometric distribution
2.1.5. Poisson distribution
2.2. CONTINUOUS DISTRIBUTIONS
2.2.1. Uniform distribution
2.2.2. Exponential distribution
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| 3. NORMAL DISTRIBUTION AND RELATED DISTRIBUTIONS |
3.1. Definition. Standardized normal
3.2. Properties. Use of tables
3.3. Approximation of the binomial by the normal
3.4. Importance of the normal distribution: central limit theorem
3.5. Distributions related to normal
3.5.1. Student's t-distribution
3.5.2. Chi square distribution
3.5.3. F de Snedecor distribution |
| 4. SAMPLING. SAMPLE DISTRIBUTIONS |
4.1. Sampling. Types of sampling
4.2. Population parameters and sample statistics
4.3. Sampling error
4.4. Sample distribution of the average sample statistic
4.5. Distribution of variance and sample quasivariance
4.6. Distribution of other sample statistics |
| 5. ESTIMATION THEORY |
5.1. Classical conception of estimation
PUNCTUAL ESTIMATION
5.2. Concept of estimator
5.3. Desirable properties of estimators
5.3.1. Non-biased estimators
5.3.2. Efficient estimators
5.3.3. Asymptotic properties
INTERVAL ESTIMATION
5.4. Concept of confidence interval
5.5. Determination of some confidence intervals |
| 6. PARAMETRIC HYPOTHESIS TESTING |
6.1. General approach. Basics
6.2. Types of statistical hypotheses
6.3. Test statistic. Critical region
6.4. Types of error
6.5. Significance level. p-value
6.6. Contrasts between mean and difference in averages
6.7. Contrasts between variance and difference in variances
6.8. Contrasts over proportions and differences in proportions |
| 7. INTRODUCTION TO ECONOMETRICS |
7.1 Simple linear regression model. Adjustment for ordinary least squares
7.2 Parameter hypothesis contracts |
| Methodologies :: Tests |
| |
Competences |
(*) Class hours
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Hours outside the classroom
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(**) Total hours |
| Introductory activities |
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1 |
0 |
1 |
| Lecture |
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30 |
45 |
75 |
| Problem solving, exercises in the classroom |
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25 |
45 |
70 |
| Personal attention |
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0 |
0 |
0 |
| |
| Mixed tests |
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4 |
0 |
4 |
| Mixed tests |
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2 |
0 |
2 |
| Short-answer objective tests |
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0 |
0 |
0 |
| |
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
| |
Description |
| Introductory activities |
Presentació assignatura |
| Lecture |
Classe expositiva |
| Problem solving, exercises in the classroom |
Realització explicativa de problemes |
| Personal attention |
Utilització de les hores d'atenció a l'estudiant per resoldre qüestions i dubtes concrets |
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Description |
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<p> Resoldre dubtes concrets de l'assignatura al despatx o bé online</p> |
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Methodologies |
Competences
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Description |
Weight |
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| Short-answer objective tests |
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Moodle quizzes to do at home. |
10% de la nota final de la asignatura |
| Mixed tests |
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There will be two tests throughout the course in which it may be necessary to use Excel
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30% de la nota final (15% cada parcial) |
| Mixed tests |
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The exam in official call will consist of objective questions (short questions or test type, in this case the erroneous answers subtract points) and problem solving in this case with a maximum form of 2 pages. The resolution of the exam may require the use of Excel. |
60% de la nota final
(Para poder presentarse la nota media de las pruebas de evaluación continuada ha de ser superior a 3)
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| Others |
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Other comments and second exam session |
In order to be able to take the final exam and be able to pass the subject, it is necessary to have obtained an average grade in the continuous assessment tests (the two partials carried out throughout the course and the questionnaires, weighted by their respective weights) greater than 3. The second call: The structure of the exam is identical to that of the first call but the mark of this test is 100% of the final grade of the subject. In all mixed tests it may be necessary to know and use Excel, and therefore the use of a computer, to carry out the exercises. In this case the use of any messaging application is prohibited. Likewise, it is strictly forbidden in the classroom to use telephones and other mobile devices that can connect to the outside. The use of these applications or devices will mean the student's expulsion from the classroom |
| Basic |
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TEORIA
ALEA, M.V. et al. Estadística aplicada a les ciències econòmiques i socials. Ed. McGraw-Hill, 1999. RUIZ-MAYA, L.; MARTÍN, F.J. Estadística II: Inferencia. Ed. Thomson, 2001. MARTíN PLIEGO, F. J.; RUIZ-MAYA, L. Estadística I: . Ed. Thomson, 2004.
PROBLEMES
ALLEPÚS et al. Exercicis d’inferència estadística. Cossetània Edicions, 2002. SARABIA, J. Curso práctico de Estadística. Ed. Civitas, 1993 |
| Complementary |
|
TEORIA ARANDA, J.; GOMEZ, J. Fundamentos de Estadística para Economía y Administración de empresas. Ed. PPU, 1992. Douglas, Lind; Marchal, William y Masón, Robert. Estadística aplicada a los negocios y a la economía, McGraw-Hill, México D.F., 2012. LÓPEZ CACHERO, M. Fundamentos y métodos de Estadística. Ed. Pirámide, 1990. MARTÍN, F.J.; RUIZ-MAYA, L. Estadística I: Probabilidad. Ed. Thomson, 2004. NOVALES, A. Estadística y econometría. Ed. Mc Graw-Hill, 1997. |
| Subjects that it is recommended to have taken before |
| MATHEMATICS II/16214009 | | STATISTICS I/16214007 |
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(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. |
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