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Statistics

Code: 103803
Credits: 6
2026/2027
Degree programme Type Course
Computer Engineering FB 2

Contact lecturer

Name :
Joan Porti Pique
Email :
joan.porti@uab.cat

Teaching staff

Joan Porti Pique
David Moriña Soler
Ivan Montero Arias
Alan Morte Piferrer

Group languages

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

Prerequisites

There are no prerequisites. It is recommended to have followed the courses in Algebra and Calculus.

Objectives

The goal of the course is to introduce the basic tools of probability and statistics to analyze data from natural phenomena or experiments, focusing in its correct use and the interpretation of the results. The theory and problem sessions are going to be complemented with practice classes in the computer room, with the aim of using computer tools.

Learning outcomes

  • CM03 (Propose appropriate statistical models in problems that require an IT solution.) Propose appropriate statistical models in problems that require an IT solution.
  • KM03 (Identify statistical methods for solving computer engineering problems.) Identify statistical methods for solving computer engineering problems.
  • SM04 (Apply knowledge of probability and statistics to solve problems.) Apply knowledge of probability and statistics to solve problems.

Contents

0. Working Tools: R, RStudio, GitHub, and Reproducible Reports

1. Descriptive Statistics and Data Visualization

2. Basic Probability and Uncertainty

3. Discrete Random Variables

4. Continuous Random Variables and the Normal Distribution

5. Central Limit Theorem

6. Estimation and Confidence Intervals

7. Hypothesis Testing

8. Simple Linear Regression and Introduction to Statistical Modeling

Learning activities and methodology

Title Hours ECTS Learning outcomes
Tutoring and consultations 14 0.56
Practices in the computer room 12 0.48
Theoretical classes / lectures 26 1.04
Problem sessions 12 0.48
Independent study and preparation 60 2.4
  • There are theory classes (lectures), problem sessions, and practices sessions. In these sessions and with individual work the especific skills are achieved. All of them are online.
  • New material will be mainly introduced in the lectures, but explanations must be complemented with the autonomous study and personal work of the student, with the help of the references and the material imade available in the CV. There will be a partial test of theory and problems.
  • The problems sessions will be devoted to the oriented resolution of some proposed problems. Attention will be payed to corection and rigorousness, as well as to vocabulary, mathematical expression and clarity in writing.
  • In the practice sessions we shall introduce software with applications to statistics (R). Descriptive and inferential methodologies are introduced. These tools will be used to solve problems and will be used to work (individually) with real data.
  • The Campus Virtual UAB is a key tool to follow the class: access to material, check information and following the course.
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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Written tests 55% 5 0.2 CM03, KM03, SM04
Practices in the computer room 25% 15 0.6 CM03, KM03, SM04
Deliverables at problems sessions 10% 6 0.24 CM03, KM03, SM04
Reproducible Final Mini-Project 10% 0 0 SM04

The assessment for this course consists of five components:

a) Three in-class problem-solving sessions, each concluding with the submission of an exercise at the end of the class. The dates will be announced at the beginning of the semester. Under no circumstances will changes of date or group be permitted. The two highest marks will be counted (10%).

b) One midterm examination (20%).

c) One final examination (35%).

d) Participation and practical assignment assessment (25%).

e) A reproducible final mini-project (10%).


The use of artificial intelligence (AI) is not permitted in any assessment activity. Any use of AI will be considered a serious academic misconduct.

Students who do not obtain a minimum weighted average of 4.0/10 across assessment components (b) and (c), or who do not achieve an overall course grade of at least 5.0/10, will be required to take a resit examination during the official resit period. The resit covers assessment components (a), (b), and (c).

To pass the course, students must obtain a minimum weighted average of 4.0/10 in components (b) and (c), or alternatively in the corresponding resit examination. There is no minimum required mark for the remaining assessment components.If these minimum requirements are met, the final course grade will be the weighted average of all assessment components. Otherwise, the final grade will be the lower of the weighted average and 4.5/10.

The dates for continuous assessment activities and assignment deadlines will be published on the Virtual Campus. These dates may be subject to change due to unforeseen circumstances. Any modifications will always be communicated through the Virtual Campus, which is considered the official communication channel between instructors and students. Please note that the School of Engineering has a protocol regulating requests for the rescheduling of assessment activities.

For each assessment activity, a date, time, and location for grade review will be announced. During this review session, students may discuss their work with the instructor and submit any appeals regarding their grade. Such appeals will be evaluated by the instructors responsible for the course.Students who do not complete assessment activities accounting for at least 50% of the course grade will receive the status "Not Assessed" (NA). The distinction of Honours (Matrícula d'Honor) may be awarded to the highest-performing students who achieve a final grade of at least 9.0/10 and who, in the opinion of the teaching staff, have demonstrated outstanding achievement of all course learning objectives.

Without prejudice to any additional disciplinary measures that may apply under current academic regulations, any irregularity committed by a student that may affect the grading of an assessment activity will result in a mark of zero (0) for the corresponding assessment component. This component will not be eligible for reassessment, and the student will fail the course with a final grade not exceeding 4.5/10. Such irregularities include, but are not limited to, plagiarism, copying, or allowing another student to copy. Having communication devices accessible during assessment activities will also be considered a serious academic misconduct, regardless of whether they are actually used.

No grades from previous academic years will be carried forward.

For students opting for single assessment, all assessment activities will take place on the same day as the second midterm examination. The assessment will consist of: the in-person completion of assessment components (b), (c), and (d);

the submission of the previously assigned tasks corresponding to components (a) and (e). Assessment components (b) and (c) will be combined into a single written examination, while component (d) will take place in a computer laboratory.

The same minimum grade requirements and reassessment criteria described for the continuous assessment scheme will apply, taking into account that components (b) and (c) are combined into a single examination.


This document has been translated from the original Catalan version to the best of our ability. In the event of any discrepancy or ambiguity between the English and Catalan versions, the Catalan version shall prevail.

Bibliography

  1.  Arnold O. Allen, Probability, Statistics, and Queueing Theory with Computer Science Applications, Academic Press, Inc. 1990
  2. Jay L. Devore. Probabilidad y estadística para ingeniería y ciencias. Thomson. 2005
  3. Rosa Millones, Emma Barreno, Félix Vásquez y Carlos Castillo, Estadística aplicada a la ingeniería y los negocios. fondo Editorial, Universidad de Lima.   2015.
  4. Douglas C. Montgomery y George C. Runger, Probabilidad y estadística aplicadas a la ingeniería. Limusa Wiley. 2002
  5. Ronald E. Walpole, Raymond H. Myers y Sharon L. Myers. Probabilidad y estadística para ingenieros. Prentice Hall. 1999
  6. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

Software

R language for statistics and RStudio

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 41 Catalan/Spanish first semester morning-mixed
(TE) Theory 43 Catalan/Spanish first semester morning-mixed
(TE) Theory 45 Catalan/Spanish first semester afternoon
(PAUL) Classroom practices 411 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 411 Catalan/Spanish first semester morning-mixed
(PAUL) Classroom practices 412 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 412 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 413 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 414 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 415 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 416 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 417 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 418 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 419 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 420 Catalan/Spanish first semester morning-mixed
(PLAB) Practical laboratories 421 Catalan/Spanish first semester morning-mixed
(PAUL) Classroom practices 431 Catalan/Spanish first semester morning-mixed
(PAUL) Classroom practices 432 Catalan/Spanish first semester morning-mixed
(PAUL) Classroom practices 451 Catalan/Spanish first semester afternoon
(PAUL) Classroom practices 452 Catalan/Spanish first semester afternoon