Guide in PDF format

Probability and Statistics

Code: 107623
Credits: 6

2026/2027

Degree programme	Type	Course
Physics	OB	2

Contact lecturer

Name :: Alvaro Corral Cano
Email :: alvaro.corral@uab.cat

Teaching staff

: Daniel Campos Moreno

Group languages

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

Prerequisites

Calculus in One Variable; Multivariable Calculus; Fundamentals of Computer Programming.

Objectives

The course must provide students with the mathematical and conceptual tools required for dealing with uncertainty, analyzing experimental data, and quantitatively interpreting physical phenomena.

All of this should prepare students to develop their ability to model random phenomena, analyze experimental data, and/or estimate physical parameters.

Learning outcomes

CM22 (Solve probability and statistical problems of data analysis for the resolution of general problems in a professional context.) Solve probability and statistical problems of data analysis for the resolution of general problems in a professional context.
CM23 (Adapt the advanced mathematical strategy when addressing a complex problem determined from an analytical point of view.) Adapt the advanced mathematical strategy when addressing a complex problem determined from an analytical point of view.
KM23 (Describe the basic concepts of calculus and analysis and the different methods of solving differential equations in their different typologies.) Describe the basic concepts of calculus and analysis and the different methods of solving differential equations in their different typologies.
KM24 (Identify the different types of integral transformations, the probability spaces of events, the foundations of probability theory and the basic concepts of statistical data analysis.) Identify the different types of integral transformations, the probability spaces of events, the foundations of probability theory and the basic concepts of statistical data analysis.
SM19 (Apply the knowledge acquired in advanced mathematics to the resolution of mathematical problems, as well as to physical problems with mathematical representation.) Apply the knowledge acquired in advanced mathematics to the resolution of mathematical problems, as well as to physical problems with mathematical representation.

- Fundamental Concepts

- Conditional Probability

- Discrete Probability Distributions

- Continuous Distributions

- Multivariate Distributions

- Sum of Random Variables

- Populations and Sampling

- Point Estimation

- Confidence Intervals

- Hypothesis Testing and p-values

- Linear Regression

- Measurement of Errors

Learning activities and methodology

Title	Hours	ECTS	Learning outcomes
Theory	28	1.12	KM23, KM24
Autonomous work and tutorials	92	3.68	CM22, CM23, KM23, KM24, SM19
Seminars	8	0.32	CM22, CM23, SM19
Problems	14	0.56	CM22, CM23, SM19

The teaching methodologies for this course involve a combination of:

Lectures and in-class discussions.

Solving basic problems and exercises, some of which will be worked through in class, while others will be assigned for independent student work.

Discussion and development of exercises and case studies using a computer, utilizing open-source software or online applications.

Consultations with the course instructors.

Note: 15 minutes of a class session—within the schedule established by the faculty or degree program—will be set aside for students to complete surveys evaluating the teaching staff and the course/module.

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
Practical problems	20	4	0.16	CM22, CM23, SM19
First exam	40	2	0.08	CM22, CM23, KM23, KM24
Second exam	40	2	0.08	CM22, CM23, KM23, KM24

The course will include three assessment activities:

1st midterm exam: 40% of the final grade

2nd midterm exam: 40% of the final grade

Practical assignment submissions: 20% of the final grade

The "Practical assignment submissions" activity will be based on a selection of exercises worked on in class and subsequently completed through the student's independent work.

Attendance at seminar sessions is mandatory and will be taken into account when evaluating the submission activity.

To pass the course, a minimum grade of 3.5 must be achieved in each of the three assessment components.

Use of AI. The use of Artificial Intelligence (AI) technologies is permitted exclusively for the "Practical assignment submissions" activity for support tasks (literature or information searches, text correction, or code generation). Students must clearly identify which parts were generated using this technology, specify the tools used, and include a critical reflection on how these tools influenced the process and the final outcome of the activity. Failure to be transparent about the use of AI in this assessable activity will be considered a lack of academic honesty and may result in a partial or total grade penalty for the activity.

Retake. If the minimum grade is not achieved in either of the two midterm exams, the student may take a retake exam for the corresponding midterm. The "practical assignment submission" activity is considered NON-RETAKEABLE.

Single assessment. Students who request the single assessment option will take a single exam covering the content of both midterm exams; this exam will account for 80% of the final grade. On the same date, the student must submit the practical assignment, which will account for the remaining 20% of the grade. This assessment method does not exempt students from mandatory attendance at seminar sessions.

Bibliography

Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P., & Meester, L. E. (2005). A modern introduction to probability and statistics: Understanding why and how. Springer Science & Business Media.

Mathai, AM, Haubold, HJ. (2017) Probability and statistics: A course for physicists and engineers. Walter de Gruyter GmbH & Co KG.

Ross, S. (2013). A First Course in Probability, 9^aedició. Addison Wesley.

Bohm, G, Zech, G. (2017). Introduction to Statistics and Data Analysis for Physicists. Verlag Deutsches Elektronen-Synchrotron Hamburg.

Delgado de la Torre, R. (2018). Probabilidad y Estadística para Ciencias e Ingeniería. Indepedent Pub.

Gonick, L, Smith, W. (1999). La estadística en còmic. Zendrera Zariquey.

Software

We will use free-access Python and R tools in the course

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	1	Catalan/Spanish	first semester	morning-mixed
(PAUL) Classroom practices	1	Catalan	first semester	morning-mixed
(TE) Theory	2	Catalan	first semester	morning-mixed
(PAUL) Classroom practices	2	Catalan	first semester	morning-mixed
(SEM) Seminars	11	Catalan	first semester	morning-mixed
(SEM) Seminars	12	Catalan	first semester	morning-mixed
(SEM) Seminars	21	Catalan	first semester	morning-mixed
(SEM) Seminars	22	Catalan	first semester	morning-mixed