| Competences |
| Type A | Code | Competences Specific |
| A1.1 | Consistently apply knowledge of basic scientific and technological subjects pertaining to engineering | |
| A1.2 | Design, execute and analyse experiments related to engineering | |
| A3.1 | Ability to solve a wide range of mathematical problems in engineering. Ability to apply the knowledge of linear algebra, geometry, differential geometry, differential and integral calculus, differential equations and partial differential equations, numerical methods, numerical algorithms, statistics and optimisation (FB1) | |
| Type B | Code | Competences Transversal |
| B4.1 | Learn effective ways to assimilate knowledge and behaviour. | |
| B4.4 | Knowledge in basic and technological subjects that enables the acquisition of new methods and theories and provides the versatility needed to adapt to new situations. (G3) | |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A1.1 |
Aplica correctament els principis matemàtics que puguin plantejar-se en l’enginyeria, àlgebra lineal, geometria, geometria diferencial, càlcul diferencial i integral, equacions diferencials i en derivades parcials, mètodes numèrics, algorítmica numèrica, estadística i optimització. | |
| A1.2 |
Coneix les tècniques de disseny d'experiments i anàlisis multivariant | |
| A3.1 |
Adquireix la capacitat d’utilització de les eines matemàtiques bàsiques en el modelat i resolució de situacions relacionades amb l’enginyeria. Les tècniques estudiades son les relacionades amb l’àlgebra lineal i l’anàlisi univariant i multivariant. Coneix els mecanismes estadísticament correctes per a un anàlisis eficient de dades: interpretació i presa de decisions sobre els valors de paràmetres físics o químics. Coneix els mètodes més freqüents d’optimització i saber utilitzar-los en la resolució de problemes de l’àmbit de l’enginyeria | |
| Type B | Code | Learning outcomes |
| B4.1 |
Desenvolupa estrategies pròpies de resoldre problemes i trobar solucions. Es capaç d’integrar paradigmes d’altres disciplines. | |
| B4.4 |
Té una visió de conjunt de les diferents teories o metodologies d’una assignatura. Fa aportacions significatives o certes innovacions. Transfereix l’aprenentatge de casos i exercicis de l’aula a situacions reals d’altres àmbits. | |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| 1. Introduction to data analysis | 1.1. Concept of Statistics. Contents of Statistics. 1.2. Concept of population, sample, individual and statistical variable. 1.3. Classification of statistical variables. 1.4. Distribution of frequences. Graphical representations. 1.5. Grouping data in intervals. 1.6. Position parameters. 1.7. Dispersion parameters. |
| 2. Random variables | 2.1. Concept of probability and properties. 2.2. Concept of random variable. 2.3. Discrete random variables: probability function and distribution function. 2.4. Continuous random variables: density function and distribution function. 2.5. Mathematical expectation. 2.6. Variance. |
| 3. Probability distribution models | 3.1. Discrete distributions: Weibull, Bernoulli, binomial, Poisson, uniform. 3.2. Continuous distributions: uniform, exponential, normal. 3.3. General normal law. Reduced normal law: N(0,1). 3.4. Distributions deduced from the normal distribution: chi-squared, Student's t and Fisher's F. 3.5. Convergence to the normal law: central limit theorem. 3.6. Use of statistical tables. |
| 4. Confidence intervals | 4.1. Notions of sample and sampling. 4.2. Concept of statistic and parameter. 4.3. Point estimation and interval estimation. 4.4. Notion of confidence interval. Confidence coefficient. 4.5. Determination of confidence intervals. 4.6. Applying confidence intervals to process control. |
| 5. Hipothesis testing | 5.1. Statistical hipotheses. Types of hipotheses. 5.2. Concept of critical region and acceptance region. 5.3. Types of errors. Significance level. 5.4. Applying hipothesis testing. 5.5. Reception control. |
| 6. Analysis of variance | 6.1. General concepts about analysis of variance. 6.2. One-way design. 6.3. Two-way design without interaction. Random blocks. 6.4. Two-way design with interaction. |
| 7. Linear regression | 7.1. Simple linear regression model. 7.2. Estimation of the regression line using the least squares method. 7.3. Goodness-of-fit measures. 7.4. Significance testing. 7.5. Prediction intervals. 7.6. Nonlinear regression. 7.7. Multiple linear regression. |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 2 | 3 | ||||||
| Lecture |
|
24 | 48 | 72 | ||||||
| IT-based practicals in computer rooms |
|
20 | 40 | 60 | ||||||
| Personal attention |
|
1 | 2 | 3 | ||||||
| Short-answer objective tests |
|
2 | 4 | 6 | ||||||
| Short-answer objective tests |
|
2 | 4 | 6 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | Introduction of the subject where the lecturer discusses the content to be worked on, the objectives to be evaluated, the methodology to be used, and the evaluation system. |
| Lecture | The lecturer explains the theoretical content of each topic. |
| IT-based practicals in computer rooms | Students are asked to do and deliver practicals, carried out with a computer, and related to the contents that are being worked on. These practicals are part of the continuous assessment of the subject. |
| Personal attention | Students can receive personal attention in person or telematically during the hours of attention to students, and during practical hours in classroom. |
| Personalized attention |
| Description |
|
Time that each lecturer has reserved to attend to and solve students' doubts. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||||
| IT-based practicals in computer rooms |
|
Students will have to solve, with a computer, problems about various contents of the subject. The practical exercises will be assessed. | 0-20% | ||||||
| Short-answer objective tests |
|
Individual test of a synthesis character on the contents developed during the first part of the subject. | 40-50% | ||||||
| Short-answer objective tests |
|
Individual test of a synthesis character on the contents developed during the second part of the subject. | 40-50% | ||||||
| Others |
| Other comments and second exam session | |||
|
Continuous assessment: The practice grade will only be taken into account when it is higher than the average grade of the two partial tests. In this case, the weights of the practice grade and the two partial tests will be 20%, 40% and 40%, respectively. Otherwise, these weights will be 0%, 50% and 50%, respectively. Second call: The final grade will consist of 100% for the grade of an individual objective test on the content of the entire subject. |
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| Sources of information |
| Basic |
Mateo, J.M., Estadística pràctica pas a pas, , URV |
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| Complementary | |||||||||
| Recommendations |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.