Guías Docentes Electrónicas
1. General information
Course:
STATISTICAL INFERENCE
Code:
53315
Type:
CORE COURSE
ECTS credits:
6
Degree:
316 - UNDERGRADUATE DEGREE IN ECONOMICS
Academic year:
2022-23
Center:
5 - FACULTY OF ECONOMICS AND BUSINESS
Group(s):
10  17 
Year:
2
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: JOSE LUIS ALFARO NAVARRO - Group(s): 10  17 
Building/Office
Department
Phone number
Email
Office hours
Facultad de Ciencias Económicas y Empresariales. Despacho 3.14
ECONOMÍA APLICADA I
926053274
joseluis.alfaro@uclm.es
The tutorial schedule will be available in Campus Virtual.

2. Pre-Requisites

It is recommended to have coursed the subject on Statistics for Economics

3. Justification in the curriculum, relation to other subjects and to the profession

In the economic field, a basic management of the fundamental techniques for the treatment of quantitative information is essential. This need is translated into a knowledge of the main sources of statistical information, the basic rules for its interpretation and Analysis, and a mastery of the most relevant analytical-quantitative instruments. Therefore, the curriculum, within module 4 "Methods Quantitative for the Economy” devotes a section to the matter of Statistics, structured in two subjects: Statistics for Economics and Statistical Inference. The fundamental mission of the subject Inference Statistics is to deduce properties (make inferences) from a population, from a small part of it (sample). The goodness of these deductions is measured in probabilistic terms, that is, all inference is accompanied by its probability of success. Inferential statistics include: sample theory, estimation of parameters, hypothesis testing, experimental design and Bayesian Inference.


4. Degree competences achieved in this course
Course competences
Code Description
E03 Ability to find economic data and select relevant facts.
E06 Application of profesional criteria to the analysis of problems, based on the use of technical tools.
E16 Identify relevant sources of financial information and its content, as well as the ability to derive the important information from the data, otherwise completely unknown to non-professionals.
G01 Possession of the skills needed for continuous, self-led, independent learning, which will allow students to develop the learning abilities needed to undertake further study with a high degree of independence.
G03 Develop oral and written communication skills in order to prepare reports, research projects and business projects and defend them before any commission or group of professionals (specialised or non-specialised) in more than one language, by collecting relevant evidence and interpreting it appropriately so as to reach conclusions.
G04 Ability for the use and development of information and communication technology in the development of professional activity.
G05 Capacity for teamwork, to lead, direct, plan and supervise multidisciplinary and multicultural teams in both national and international environments.
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Train the student to work out problems in creative and innovative ways.
Enable students to know the sources of official statistics and their treatment for the analysis of economic reality
To know the tools and methods for quantitative analysis of markets, sectors and companies, including models for decision-making and economic forecasting models.
Enable student for autonomous work and learning, as well as for personal initiative
Train the student to search for information in order to analyze it, interpret is meaning, synthesize it and communicate it to others.
Additional outcomes
Description
The student will be able to: a) Access the relevant statistical-economic information. B) Understand and apply the approximation of random variables, as a powerful tool to solve problems formed by the accumulation of infinity of small phenomena random. C) Extract a sample from a population with a level of randomness and representativeness sufficient to ensure the validity of the conclusions drawn. D) Identify the distribution of the phenomenon under study in a population and estimate their parameters (or characteristics) by the best possible procedures. E) Know how to identify a problem with the appropriate hypotheses and handle the corresponding techniques to test them. F) Recognize a problem, analyze it and solve it using the method scientific. G) Use basic software for statistical analysis (Excel and R) h) Solve problems in a creative and innovative way. I) Work and learn autonomously and with personal initiative. J) Collaborate with other students to achieve group work. K) Listen and defend oral and written arguments.
6. Units / Contents
  • Unit 1: DISTRIBUTIONS DERIVED FROM THE NORMAL AND THE CENTRAL LIMIT THEOREM
    • Unit 1.1: CONVERGENCE OF SUCCESSIONS OF RANDOM VARIABLES: THEORY OF THE CENTRAL LIMIT
    • Unit 1.2: DISTRIBUTIONS FROM NORMAL
  • Unit 2: DISTRIBUTIONS IN SAMPLING
    • Unit 2.1: SAMPLING: STATISTICS AND THEIR DISTRIBUTIONS
    • Unit 2.2: SAMPLING IN NORMAL POPULATIONS
  • Unit 3: ESTIMATORS AND THEIR PROPERTIES
    • Unit 3.1: POINT ESTIMATION: CONCEPT AND PROPERTIES OF ESTIMATORS
    • Unit 3.2: METHODS OF PONIT ESTIMATION
    • Unit 3.3: CONFIDENCE INTERVALS ESTIMATION
  • Unit 4: HYPOTHESES TESTING
    • Unit 4.1: INTRODUCTION TO HYPOTHESES TESTING
    • Unit 4.2: PARAMETRIC HYPOTHESES TESTING
    • Unit 4.3: NON-PARAMETRIC HYPOTHESES TESTING
  • Unit 5: ANALYSIS OF THE VARIANCE (ANOVA)
    • Unit 5.1: INTRODUCTION TO ANOVA
    • Unit 5.2: ANALYSIS OF THE ONE-WAY ANOVA
    • Unit 5.3: ANALYSIS OF THE MULTI-FACTOR ANOVA
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences (only degrees before RD 822/2021) ECTS Hours As Com Description
Class Attendance (theory) [ON-SITE] Lectures E03 E06 E16 G01 G04 1.33 33.25 N N The teacher will focus on the matter and the fundamental concepts. Time will also be dedicated for examples.
Class Attendance (practical) [ON-SITE] Combination of methods E03 E06 E16 G01 G03 G04 G05 0.67 16.75 Y N Participation is valued
Study and Exam Preparation [OFF-SITE] Self-study E03 E06 E16 G01 G04 2.08 52 Y N Independent work of student tutored by the teacher.
Writing of reports or projects [OFF-SITE] Group Work E03 E06 E16 G01 G03 G04 G05 0.72 18 Y N At the beginning of the course working groups will be created and they handle a project that will develop along the course. These projects will be supervised by the teacher and may need to be exposed at the end of the course.
Other off-site activity [OFF-SITE] Self-study E16 G01 G03 G04 0.8 20 Y N Individual practice. The teacher will provide the student some tasks which will have to be solved and delivered at the end of each theme.
Progress test [ON-SITE] Assessment tests E03 E06 E16 G01 G03 G04 0.04 1 Y N Self evaluation tests
Final test [ON-SITE] Assessment tests E03 E06 E16 G01 G03 G04 0.08 2 Y Y Test preparation and conduct written questionnaire and exercises to solve
Other on-site activities [ON-SITE] Combination of methods E03 E06 E16 G04 0.28 7 N N Seminars or group tutorials
Total: 6 150
Total credits of in-class work: 2.4 Total class time hours: 60
Total credits of out of class work: 3.6 Total hours of out of class work: 90

As: Assessable training activity
Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).

8. Evaluation criteria and Grading System
Evaluation System Continuous assessment Non-continuous evaluation * Description
Assessment of active participation 5.00% 0.00% The active attitude of the student will be assessed in the classroom.
Assessment of problem solving and/or case studies 20.00% 0.00% Throughout the course, three practices will be proposed for the resolution or, alternatively, the realization of a poject.
Progress Tests 10.00% 0.00% Written choice test with 10 questions. Each question
has three alternative answers, one correct and two
incorrect. Each correct answer adds 1 point and each
failed subtract 0.5, questions left blank unscored.
Final test 65.00% 100.00% Written test with some practical questions to be solved
Total: 100.00% 100.00%  
According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).

Evaluation criteria for the final exam:
  • Continuous assessment:
    The subject follows an evaluation system based on the assessment of various training activities and an exam. The student is required to obtain a 4 (out of 10) in the final evaluation test to make an average with the grade obtained in the rest of the proposed training activities. Those students who, even having carried out assessable activities, wish to be evaluated with the non-continuous evaluation criteria must notify the teacher before the end of the class period.

    Regarding the evaluation in case of illness or other special circumstances (mitigating rules), see article 6 of the Student Evaluation Regulation of the University of Castilla-La Mancha.
  • Non-continuous evaluation:
    Regarding the non-continuous evaluation, see section b of point 4.2. of the UCLM Student Regulations approved on May 23, 2022. The evaluation will be carried out with a final test that will include the specific tests that are considered necessary to evaluate all the competencies of the subject.
    In case of illness or other special circumstances (mitigating rules), see article 6 of the Student Evaluation Regulation of the University of Castilla-La Mancha.

Specifications for the resit/retake exam:
There are no particularities.
Specifications for the second resit / retake exam:
The evaluation will be carried out on a single written test, being necessary to pass the subject a minimum score of 5 out of 10.
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Class Attendance (theory) [PRESENCIAL][Lectures] 3.25
Class Attendance (practical) [PRESENCIAL][Combination of methods] 1.75
Progress test [PRESENCIAL][Assessment tests] 1
Final test [PRESENCIAL][Assessment tests] 2
Other on-site activities [PRESENCIAL][Combination of methods] 7

Unit 1 (de 5): DISTRIBUTIONS DERIVED FROM THE NORMAL AND THE CENTRAL LIMIT THEOREM
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 5
Class Attendance (practical) [PRESENCIAL][Combination of methods] 2.5
Study and Exam Preparation [AUTÓNOMA][Self-study] 8
Other off-site activity [AUTÓNOMA][Self-study] 3
Teaching period: 2.5 weeks

Unit 2 (de 5): DISTRIBUTIONS IN SAMPLING
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 5
Class Attendance (practical) [PRESENCIAL][Combination of methods] 2.5
Study and Exam Preparation [AUTÓNOMA][Self-study] 8
Other off-site activity [AUTÓNOMA][Self-study] 3
Teaching period: 2.5 weeks

Unit 3 (de 5): ESTIMATORS AND THEIR PROPERTIES
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 8
Class Attendance (practical) [PRESENCIAL][Combination of methods] 4
Study and Exam Preparation [AUTÓNOMA][Self-study] 15
Writing of reports or projects [AUTÓNOMA][Group Work] 2
Other off-site activity [AUTÓNOMA][Self-study] 6
Teaching period: 4 weeks

Unit 4 (de 5): HYPOTHESES TESTING
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 8
Class Attendance (practical) [PRESENCIAL][Combination of methods] 4
Study and Exam Preparation [AUTÓNOMA][Self-study] 15
Writing of reports or projects [AUTÓNOMA][Group Work] 8
Other off-site activity [AUTÓNOMA][Self-study] 6
Teaching period: 4 weeks

Unit 5 (de 5): ANALYSIS OF THE VARIANCE (ANOVA)
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 4
Class Attendance (practical) [PRESENCIAL][Combination of methods] 2
Study and Exam Preparation [AUTÓNOMA][Self-study] 6
Writing of reports or projects [AUTÓNOMA][Group Work] 8
Other off-site activity [AUTÓNOMA][Self-study] 2
Teaching period: 2 weeks

Global activity
Activities hours
General comments about the planning: Planning may vary depending on the schedule and teaching needs.
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
Canavos, G.C. & Miller D.M. Modern Business Statistics Duxbury Resource Center 978-0534168360 1994  
Canavos, George C. Probabilidad y estadística :aplicaciones y métodos McGraw-Hill 84-481-0038-7 2003 Ficha de la biblioteca
Casas Sánchez, José M. Estadística. II, Inferencia estadística Editorial Centro de Estudios Ramón Areces, S.A. 978-84-9961-024-5 2011 Ficha de la biblioteca
Casas Sánchez, José M. Inferencia estadística : (incluye ejercicios resueltos) Centro de Estudios Ramón Areces 9788480042635 2009 Ficha de la biblioteca
Hand,Diamond J. Statistics: A very short introduction Oxford U.P. 978-0199233564 2008  
Martín-Pliego López, Fco. Javier Problemas de inferencia estadística Thompson 84-9732-355-6 2005 Ficha de la biblioteca
Pérez, R. Análisis de datos económicos Pirámide 84-368-0728-6(o.c.) 1997 Ficha de la biblioteca
Rohatgi, Vijay K. An introduction to probability theory and Mathematical Stati John Wiley 0-471-73135-8 1976 Ficha de la biblioteca
Rohatgi, Vijay K. Statistical inference Dover 0-486-42812-5 (pbk.) 2003 Ficha de la biblioteca
Ruiz-Maya, Luis Fundamentos de inferencia estadística AC Thomson Paraninfo 84-9732-354-8 2004 Ficha de la biblioteca
Wasserman, Larry A. All of Statistics: A concise course in Statistical Inference Springer 978-0387402727 2004  
Webster, Allen L. Estadística aplicada a los negocios y la economia McGraw-Hill 958-410-072-6 2000 Ficha de la biblioteca



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