Description: 

Stobaeus recounts, in his book Florilegium, an anecdote that took place with Euclid on the first day of class. The Greek mathematician had just finished explaining the first theorem when a student interrupted him, asking: “What benefit will I get from this?” Euclid called for a slave and ordered: “Give him a coin, since he needs to profit from what he learns.”


To learn to appreciate mathematics, as Abraham Flexner would say, we must abolish the word utility and thus free the human spirit. Learning and doing mathematics, first and foremost, is a personal experience. Mathematics connects two faculties: intuition and reason. It is an intimate, silent yet vibrant dialogue between immediacy and reflection. Every small step we take is guided by intuition, but it is not a firm step until reason validates it.

 

David Bessis, in his book Mathematica: Une aventure au cœur de nous-mêmes, presents an interesting example related to this idea. We draw a circle on a sheet of paper and ask ourselves: at most, how many points will a straight line crossing the page intersect the circle? Intuition gives us an immediate answer: a single straight line will intersect the circle at most at two points. This intuitive solution is as certain as it is simple to reach. However, if we want to prove it formally, we’ll need the equation of the line, the equation of the circle, and we’ll have to solve a system to find the points of intersection. The algebraic solution gives us three possibilities: the line intersects the circle at two points, at no point, or at a single point (a tangent line). Thus, the reasoned answer confirms our initial intuition: at most, the line intersects the circle at two points. To validate this, we’ve used mathematical language. Moreover, in every step (formulating the proof, setting up the system, solving it, etc.) no matter how small each step may be, we constantly rely on intuition.

These continuous validations lead to an improvement in our mathematical intuition, which we can clearly observe evolving over time. In this sense—and also in other, more mundane ways that we won’t delve into now—mathematics invites us, above all, to experience its practice as a journey of personal growth.

 

Everything discussed so far is independent of the specific mathematics syllabus you may be working on. It applies equally to the most basic exercises in primary school and to the more complex topics in Algebra or Calculus at university level. Every course is a new opportunity to discover and deepen this understanding. The course in Statistics and Mathematical Analysis is one such opportunity. The Mathematical Analysis part is an extension of differential calculus to functions of more than one variable. In the second semester, we will focus on Probability and Statistics, which will likely be new to everyone.


If you’ve made it this far and are still unsure of what benefit you might gain from all this, let me add that this course also lays the mathematical foundations for many subjects you’ll encounter later on, such as: Signals and Transmission Systems, Knowledge-Based Systems, Electromagnetic Propagation, Acoustic Engineering, Audio and Speech Processing, Digital Signal Processing, Data Mining, Physical Simulation, Communications in Hostile Environments, Data Network Interconnection, Robotics, Optical Communications, Digital Image Processing, and more.


However, as mentioned earlier, we invite you to free yourselves from the need to find a practical use for mathematics, so that you can instead focus on the discipline itself, which is, in truth, a way of focusing on yourselves.



Type Subject
Tercer - Obligatoria
Semester
Annual
Course
2
Credits
8.00

Titular Professors

Previous Knowledge: 

Differential and Integral one variable functions calculus. Vectors spaces and their basic properties.

Objectives: 

Learning Outcomes of this subject are: LO.1 Mathematical knowledge to face the degree. This is a very generic learning outcome, which is also shared with other subjects. In this subject, this result of general learning is seggregated in these other two: LO1.1: Knowledge of multivariate mathematical analysis, probability and statistics to face the degree. LO1.2: Practical application of the acquired knowledge to problem solving.

Contents: 

Block 1. Functions of several variables 1. Previous definitions 2. Functions of several real variables 2.1. Definition and domain 2.2. Limits 2.3. Continuity 2.4. Graphs, level curves and surfaces 3. Total and partial increment of a function. Differential of a function 4. Partial derivatives 4.1. Definition 4.2. Geometric interpretation 4.3. Generalization to functions of more than two variables 4.4. Higher order partial derivatives 5. Differentiability 5.1. Errors and differentials 6. Directional derivative 6.1. Geometric definition and interpretation 6.2. Differentiability and directional derivative 6.3. Gradient: definition and properties 7. Tangent plane and line normal to a function 8. Derivation of implicit and composite functions 9. Maxima and minima 10. Constrained optimization. Lagrange multipliers method Block 2. Multiple integrals 1. Double integrals 1.1. Domain and properties 1.2. Calculation of double integrals 1.3. Change of variable. Jacobians. Polar coordinates 2. Triple integrals 2.1. Domain and properties 2.2. Calculation of triple integrals 2.3. Change of variable. Cylindrical and spherical coordinates Block 3. Probability and statistics 1. Combinatorics 1.1. Variations 1.2. Permutations 1.3. Combinations 2. Introduction to probability 2.1. Previous definitions 2.2. Operations between events 2.3. Definitions of probability 2.4. Conditional probability 2.5. Law of total probabilities 2.6. Bayes' theorem 2.7. Independent events 3. Random variable 3.1. Previous definitions 3.2. Discrete random variable 3.2.1. Distribution function 3.3. Continuous random variable 3.3.1. Distribution function 3.3.2. Density function 3.4. Mathematical expectation and moments 3.4.1. Expectation 3.4.2. Variance and standard deviation 3.5. Markov and Chebysev inequalities 4. Univariate distributions 4.1. Discrete distributions 4.1.1. Binomial 4.1.2. Poisson 4.2. Continuous distributions 4.2.1. Uniform 4.2.2. Normal 5. Bivariate distributions 5.1. Discrete distributions 5.2. Continuous distributions 5.3. Distribution functions (accumulated) 5.4. Marginal distributions 5.5. Independent random variables 5.6. Conditional distributions 5.7. Covariance and correlation 5.8. Linear regression between two random variables 6. Sample theory 6.1. Central limit theorem 6.2. Sampling 6.3. Hypothesis testing

Methodology: 

The course is taught in 2 weekly lessons lasting 100 minutes each. The usual dynamics of each class will consist of a combination of theoretical explanations always followed by exercises that exemplify what has just been explained. Applied methodologies: master class, problems and exercises class. Additionally, the eStudy provides resources for the student to carry out self-learning activities (by viewing videos indexed according to their content) and self-assessment (by conducting non-evaluable questionnaires on the content). Applied methodology: self-paced learning. Finally, in order to achieve an applied view of the mathematical concepts presented in class, two practical exercises using the Matlab software will be undertaken, one each semester. Applied methodology: challenge-based learning.

Evaluation: 

There are 2 final exams, one for each semester. There are written tests of continuous assessment. There are practices in Matlab.

Evaluation Criteria: 

Both semesters must be passed separately. If a semester is not passed, there is a retake exam in July. The provisional grade for each semester will be that of the June exam and will include an exercise related to the practice of the second semester. The final grade for the semester in the ordinary exam will be obtained by weighting the provisional grade by 70% and the continuous assessment grade by 30%, provided that the provisional grade is equal to or greater than 3.5 points and that applying the weighting does not result in a final grade lower than the provisional grade.

Basic Bibliography: 

All of the books listed below are available at the La Salle Library. Blocks 1 and 2: Functions of multiple variables, Multiple integrals N. Piskunov, “Cálculo diferencial e integral,” Ed. Montaner & Simon G.L. Bradley, K.J. Smith, “Cálculo de varias variables,” Ed. Prentice Hall G.B. Thomas, R.L. Finney, “Cálculo – varias variables,” Ed. Addison Wesley Longman J. De Burgos, “Cálculo infinitesimal de varias variables”, Ed. Mc Graw Hill Block 3: Probability and statistics L. Vicent, R. Villalbí, “Probabilitat”, available in PDF in the eStudy D.D. Wackerly, W. Mendenhall, R.L. Schaeffer, " Estadística matemática con aplicaciones." Ed. Math

Additional Material: 

1. Lliçons de Càlcul de Probabilitats. Marta Sanz. 1995.Publicacions Universitat de Barcelona. 2. Problemas de Probabilidades y Estadística. C.M. Cuadras. Ediciones PPU. 1990. Barcelona. 3. Problemas de Análisis Matemático. Bombal. Marín. Vera. Editorial AC, libros científicos y técnicos. Madrid.