TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Basic

Coordinator: Joan Triadó Aymerich

Trimester: Third term

Credits: 6

Teaching staff: 

Cristina Steegmann Pascual

Academic year: 2025

Teaching course: 2

Languages ​​of instruction

  • Catalan

Competencies / Learning Outcomes

Specific skills

  • K2. Identify the basic methodologies of linear algebra; geometry; differential geometry; differential and integral calculus; differential and partial differential equations; numerical methods; numerical algorithm; statistics and optimization that are applied in engineering.

  • S1. Solve, through the use of mathematics and statistics, the possible problems that may arise in engineering.

  • S32. Apply critical thinking using different strategies depending on what needs to be learned and in the context in which it needs to be learned.

  • S44. Use the main sources of information in industrial technical engineering and the criteria to discriminate their veracity and usefulness. Likewise, they will be able to use the main basic ICT tools of a transversal nature and those specific to industrial technical engineering depending on the objective.

  • C19. Develop teamwork in a cooperative manner, planning the work to be executed and respecting and integrating different points of view when working in a team.

Presentation of the subject

The subject as a discipline of science responsible for learning from data and analyzing phenomena with uncertainty provides the basis for: synthesizing information, analyzing random phenomena with the application of probability theory and the study of different probability distributions. Applied examples of sampling and statistical inference applied in areas close to the degree areas and an introduction to linear models will be given.

The classroom (physical or virtual) is a safe space, free of sexist, racist, homophobic, transphobic and discriminatory attitudes, either towards students or towards teachers. We trust that together we can create a safe space where we can make mistakes and learn without having to suffer prejudice from others. 

Contents

SUBJECT 1. DESCRIPTIVE STATISTICS

1. Types of data and their graphic representation

1.1. Types of variables

1.2. Qualitative variables and discrete quantitative variables

1.3. Continuous quantitative variables

1.4. Continuous quantitative variables and histogram

2. Center measures and properties

2.1. fashion

2.2. Median

2.3. Average

2.4. Mean - median comparison

2.5. Center measurements and tabular data

3. Dispersion measures

3.1. The quartiles and the median

3.2. Standard deviation and variance (and mean)

3.3. Uses of the mean and standard deviation or the median and the five summary numbers

3.4. Variance and tabulated data

SUBJECT 2. PROBABILITY

1. Introduction to probability

1.1. Introduction

1.2. Random event or happening

1.3. Successful operations

2. Combinatorics and counting techniques

2.1. Variations

2.2. Variations with repetition

2.3. permutations

2.4. Combinations

3. Probability

3.1. Introduction and relative frequency

3.2. Probability theory

3.3. Properties that derive from the definition of probability

3.4. Laplace's rule

3.5. Probabilities in non-uniform sample spaces and relative frequency

3.6. Conditional probability

3.7. Independence of events

4. Bayes' theorem

4.1. Partitions

4.2. Theorem of total probabilities

4.3. Probability trees and conditional probability

4.4. Contingency tables

4.5. Bayes theorem

SUBJECT 3. DISCRETE RANDOM VARIABLES

1. Introduction to discrete random variables

1.1. Introduction to random variables

1.2. Discrete random variables

2. Expectation and variance

2.1. Definitions

2.2. Properties of hope

2.3. Properties of variance

2.4. Chebyshev's inequality

3. Discrete distributions

3.1. Bernoulli distribution

3.2. Binomial distribution

3.3. Geometric distribution

3.4. Poisson distribution

SUBJECT 4. CONTINUOUS RANDOM VARIABLES

1. Continuous variables

1.1. Density function

1.2. Relationship between distribution and density functions. Calculation of probabilities.

1.3. independence

1.4. Hope and variance

2. Continuous laws. normal law

2.1. Uniform distribution

2.2. Exponential distribution

2.3. Normal distribution

SUBJECT 5. CENTRAL LIMIT THEOREM

1. The distribution of the sample mean

1.1. Distribution of the sample mean for normal variables

2. The central limit theorem

2.1. Approximation of the binomial to the normal

2.2. The central limit theorem

 

Activities and evaluation system

The final grade is the weighted sum of the grades for the learning activities:

Q = 0.60 (PT + PP) + 0.20 PLab + 0.20 Proj

PT: Theoretical part of the subject

PP: Practical part of the subject (syllabus exercises)

PLab: Deliverable laboratory practices, in groups

Proj: Deliverable project, individual

 

The theory part of the subject (PT) + the practice part (PP) must be completed and a minimum of 5 points must be taken in order to be able to choose to count the other scores.

In the event of a match in final grades, the final exam grade prevails to qualify for the MH.

 

 

 

 

 

Observations relating to Recovery:

The theory part of the subject (PT) + practical part (PP) is indeed recoverable. The rest of the parts are not recoverable. For students who attend the make-up exam, their grade will be the one obtained in this test and their final grade (Q) will be calculated using the formulas detailed above and in no case will it be higher than 6.

 

Bibliography

Basic

MENDENHALL, William and SINCICH, Terry. Statistics for Engineering and the Sciences. 5. Prentice Hall, 2006.

Sanchís, C .; Salillas, J .; Riera, T .; Fontanet, G. (1987): Making statistics. Madrid (Spain), Alhambra

Complementary

 Max Kuhn and Kjell Johnson, Applied Predictive Modeling. Sringer 2013