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Statistics

Code: 103816 ECTS Credits: 6
2025/2026
Degree Type Year
Aeronautical Management FB 1

Contact

Name:
Anna Lopez Ratera
Email:
anna.lopez.ratera@uab.cat

Teachers

Queralt Miro Catalina

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

There are no official prerequisites.

Objectives and Contextualisation

Statistical analysis is one of the subjects taken in the first year of the basic formation course that enhance the foundations of modern scientific thinking. It serves as a base in order to understand the acquisition of knowledge through experience and to give scientific foundations in making different decisions.

The aim of this subject is introducing the basic tools of probability and statistics in order to analyze the data generated from the description of natural, social, economical or experimental phenomena, putting an accent on their correct use and the interpretation of the results. The theoretical sessions will be complemented with problem-solving sessions and classes dedicated to experiments, in which the problem-solving ones will have as their main objective the reinforcement of the contents treated in the theoretical sessions, and as for the experimental sessions, the consolidation of descriptive statistics: techniques such as the use of Excel as a calculating system for data treatment and statistical simulation will be taught and discussed.


Competences

  • Apply specific software for solving problems in the aeronautical sector.
  • Communication.
  • Personal attitude.
  • Personal work habits.
  • Thinking skills.
  • Use knowledge of the fundamental principles of mathematics, economics, information technologies and psychology of organisations and work to understand, develop and evaluate the management processes of the different systems in the aeronautical sector.
  • Work in teams.

Learning Outcomes

  1. Communicate knowledge and findings efficiently, both orally and in writing, both in professional situations and with a non-expert audience.
  2. Critically assess the work done.
  3. Develop critical thought and reasoning.
  4. Develop curiosity and creativity.
  5. Develop independent learning strategies.
  6. Develop scientific thinking skills.
  7. Develop systemic thinking.
  8. Develop the ability to analyse, synthesise and plan ahead.
  9. Formulate and solve problems of probability calculation.
  10. Infer properties of a population from a sample.
  11. Manage time and available resources. Work in an organised manner.
  12. Use statistical software.
  13. Work cooperatively.
  14. Work independently.

Content

1. Descriptive statistics

    1.1. Descriptive study of a variable

          1.1.1. Qualitative: Frequency table; and graphs: pie chart, bar chart and others

          1.1.2. Quantitative: Statistical measurement table: mean, deviation; and thanks: bar chart, histogram and boxplot

    1.2. Descriptive study of two variables

          1.2.1. Qualitative: Contingency table and graph of bars grouped by category

          1.2.2. Quantitative: Correlation coefficient and scatter graph

2. Probability

    2.1. Definition

    2.2. Conditional probability

    2.3. Event Independence

 3. Random variables

    3.1. Discrete and continuous random variables

    3.2. Probability and distribution functions

    3.3. Position, dispersion and shape measures: expected value, moments, variance and others

    3.4. Notable distributions: Binomial, Poisson, Uniform, Exponential and Normal.

    3.5. Bivariate distributions and independence between random variables

 4. Random variable function distributions and approximations

    4.1. Sample and population

    4.2. Distribution of the mean, variance and quasivariance in normal random variables

    4.3. Random variable function approaches

          4.3.1. Convergence in probability and distribution

          4.3.2. Law of large numbers and Monte Carlo Method

          4.3.3. Central Limit Theorem

          4.3.4. Monte Carlo simulation

 5. Statistical inference

    5.1. Point estimator and confidence intervals.

   5.2. Hypothesis contrasts for a population: average, variance and proportion

    5.3. Hypothesis contracts for two populations: paired or independent populations

    5.4. Xi-Square Contrasts

    5.5. Variance Analysis

 6. Linear Regression Model

    6.1. Ordinary Minimum Square Estimator

    6.2. Goodness of fit

    6.3. Prediction with regression model


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Experimental classes 12 0.48 2, 6, 7, 8, 4, 3, 10, 9, 13, 12
Problem-solving classes 15 0.6 2, 6, 7, 8, 4, 3, 10, 9
Theoretical classes 30 1.2 2, 6, 7, 8, 4, 3, 10, 9
Type: Supervised      
Individual tutoring 8 0.32 4, 3, 10, 9
Type: Autonomous      
Preparation for experimental works 10 0.4 2, 1, 6, 7, 5, 8, 4, 3, 11, 10, 9, 13, 14, 12
Study and resolution of problems 67 2.68 2, 6, 7, 5, 8, 4, 3, 11, 10, 9, 14

The core of the learning process is the student’s work. The student learns while working, and the mission of the teachers is to help her/him in these tasks by providing proper information and/or sources, guiding his/her orientation so that the learning process could be performed efficiently. In this context, and following the original objectives of the subject, the course will be structured with the following activities:

Theoretical sessions

The student acquires the scientific/technical knowledge unique to the subject by assisting the theoretical sessions and by complimenting them with individual studies of the topics that are already explained in class.

Problems and experiments:

The problem-solving/experimental sessions are ones that incorporate a reduced number of students than the theoretical sessions- but with a bigger mission. On the one hand, the scientific-technological knowledge that is introduced in the theoretical sessions will be discussed and worked on in order to expand it and to complete its comprehension through various activities (ranging from the typical resolution of problems to the discussion of practical cases). On the other hand, the problem-solving sessions will work as natural forums that enable the discussions on the benchmarks treated in the experimental ones by supplying the students with the knowledge necessary to go on with the experiments, or by indicating where and how they may acquire it.

The more experimental part of this subject serves as a way to orient the student in a statistics assignment in each one of its stages. It will consist in putting into practice the various concepts that had been introduced during the course using Excel and the statistical packet that it incorporates.

Use of Artificial Intelligence:

This course allows the use of Intel•Artificial Ligència (AI) technologies to support experimental classes. Still, the final analysis and resolution must be of the student, as well as the critical reflection of the result obtained in the use of AI. In no case may AI be used in the in-person evaluable activities. Non-transparency of the use of AI in an evaluable activity will be considered academic lack of honesty and may result in a partial or total penalty in the activity note, or greater penalties in cases of severity.

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.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Delivery of solved problems 20% 4 0.16 1, 6, 7, 5, 8, 4, 3, 11, 10, 9, 14
Final exam / Reassessment (E) 50% 2 0.08 2, 1, 6, 7, 5, 8, 4, 3, 11, 10, 9, 14
Practicatl exam (P) 30% 2 0.08 2, 1, 6, 7, 5, 8, 4, 3, 11, 10, 9, 13, 14, 12

The Assessment will evaluate the scientific-technical knowledge of the subject achieved by the student, as well as their ability to analyze and synthesis, critical reasoning and apply their knowledge in the resolution of practical assumptions.

This course does not provide for the single assessment system.

The evaluation will be continued with several fundamental objectives: to monitor the teaching-learning process, allowing both the student and the teacher to know the degree of achievement of the competencies and to correct, if possible, the deviations that occur. Encourage the student’s continued effort in the face of last-minute, often useless, overexertion.

Continuous Assessment

The Continuing Assessment consists of delivery of issues that are resolved on three different days during the course, an internship exam on a date close to the end of the course, and a final exam on a date other than the internship exam date. 

The subject grade will be obtained from the delivery of the problems that will give a grade C, from an exam of the subject practices that will give a grade P, and the final exam grade (C). Score C is 20% in weight, Score P is 30% in weight, and Final Exam Score E1 is 50% in weight of Final Score.

Grades C, P, and E1 give the final grade for the subject (N) as follows:

N = 0.50 × E1 + 0.20 × C + 0.30 × P

Recovery and/or improvement of the exam note:

The student passes the course if N is larger than or equal to 5 and at the same time E1 is larger than 3.5 and P is larger than 3.5. Otherwise, or if the student wants to improve the final grade, there is a chance to improve the E1 exam grade by performing a recovery exam, the grade of which will be E2. Thus, from this recovery grade, the final grade of the subject is obtained:

NF = 0.50 × max(E1, E2) + 0.20 × C +0.30 × P

Observations: 

  • Continuous Assessment grades C and P are not recoverable.
  • The student is considered to have presented to the subject’s call if he/she is presented to either of the two examinations that give rise to grade E1 or E2. Otherwise, it will be a Not Presented, even if it has some ongoing assessment grade (C and/or P).
  • Going to the E2 Recovery Exam involves earning at most a grade of 7 on the subject.
  • In order to obtain an Honor Tuition, it is recommended that all three parties have an excellent one. 

Rating Not Evaluable:

Students who have only participated in assessment activities that together weigh less than or equal to 50% will be rated as ‘Not Evaluable’.

Without prejudice to other disciplinary measures that are deemed appropriate, and in accordance with current academic regulations, irregularities committed by the student that may lead to a variation of the rating of an evaluation act will be scored with a zero. Therefore, copying or copying a practice or any other assessment activity will involve suspending it with a zero, and if it is necessary to pass it, the entire course will be suspended. Assessment activities qualified in this way and by this procedure will not be recoverable, and therefore the subject will be suspended directly without the opportunity to recover it in the same academic course.


Exam dates and job submissions will be posted on the virtual campus and may be subject to potential schedule changes for reasons of adapting to potential issues. These changes will always be reported on the virtual campus as it is understood that this is the usual platform for information exchange between teachers and students.


Bibliography

Bardina, X., Farré, M.: Estadística descriptiva. Manuals UAB, 2009

Delgado, R.: Probabilidad y Estadística para ciencias e ingenierías. Delta, Publicaciones Universitarias. 2008.

Peña, D.: Estadística. Fundamentos de estadística. Alianza Universidad. 2001.

Silvey, S.D.: Statistical Inference. Chapman&Hall. 1975.

Novales, A.: Econometria. McGraw-Hill 2000


Software

The practical part of the subject is done using the Excel spreadsheet and the statistical package that this program incorporates.


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.

Name Group Language Semester Turn
(PAUL) Classroom practices 11 Catalan first semester afternoon
(PAUL) Classroom practices 12 Catalan first semester afternoon
(PLAB) Practical laboratories 21 Catalan first semester afternoon
(PLAB) Practical laboratories 22 Catalan first semester afternoon
(PLAB) Practical laboratories 23 Catalan first semester afternoon
(TE) Theory 11 Catalan first semester afternoon