| Competences |
| Type A | Code | Competences Specific |
| A1 | Apply basic knowledge of mathematics, physics, chemistry and biology to oenology. | |
| Type B | Code | Competences Transversal |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A1 |
Aplicar els conceptes i les tècniques estadístiques al tractament de resultats experimentals, que permetin estimar la fiabilitat dels valors finals Formular models d'ajust de resultats experimentals a les funcions teòriques fisicoquímiques Conèixer les bases dels models de distribució de probabilitat discrets i continus Aplicar l'estimació matemàtica i els tests estadístics, útils quan s'han de prendre decisions sobre els valors de paràmetres i els seus marges d'error Utilitzar eines informàtiques per fer el tractament estadístic de dades Utilitzar eines informàtiques per a resoldre equacions, sistemes d'equacions, integrals i equacions diferencials ordinàries | |
| Type B | Code | Learning outcomes |
| 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 random variable. 1.3. Classification of the statistical variables. 1.4. Position parameters. 1.5. 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. Expected value. 2.6. Variance. |
| 3. Models of probability distribution. | 3.1. Discrete distributions: 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: khi-squared, Student’s t and Snedecor’s F. 3.5. Convergence to the normal law: central limit theorem. 3.6. Calculation of probabilities with computer tools. |
| 4. Confidence intervals. | 4.1. Concept of estimator and parameter. Point estimation and interval estimation. 4.2. Notion of confidence interval. Confidence coefficient. 4.3. Determination of confidence intervals for: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions. |
| 5. Hypothesis testing. | 5.1. Statistical hypotheses. Types of hypotheses. 5.2. Concept of critical region and acceptance region. 5.3. Types of errors. Power of a test. Significance level. 5.4. Applying hypothesis testing to: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions. |
| 6. Analysis of variance. | 6.1. General concepts about the 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 by the least squares method. 7.3. Goodness-of-fit measures. 7.4. Significance testing. 7.5. Prediction intervals. 7.6. Non linear regression. 7.7. Multiple linear regression. |
| 8. Numerical methods. | 8.1. Error analysis. Precision and accuracy. 8.2. Zeros of functions. 8.3. Solving systems of linear and nonlinear equations. 8.4. Numerical integration. 8.5. Numerical solution of differential equations. |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1.2 | 0 | 1.2 | ||||||
| Lecture |
|
28 | 44.8 | 72.8 | ||||||
| IT-based practicals in computer rooms |
|
28 | 42 | 70 | ||||||
| Personal attention |
|
0 | 0 | 0 | ||||||
| Short-answer objective tests |
|
3 | 3 | 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 course explaining the contents to develop, the objectives to evaluate, the methodology used and the evaluation method. |
| Lecture | The professor explains the theoretical content of each subject. A whiteboard and the projection of notes are used. |
| IT-based practicals in computer rooms | Students are asked to solve and deliver practical exercises, using a computer, related to the content they are currently working on. These practical exercises are part of the ongoing evaluation of the course. |
| Personal attention | Students can receive personalized attention for any aspect of the course during the student attention hours and during the hours for solving exercises and practicals in the classroom and, ellectronically, at any other time of the semester. |
| Personalized attention |
| Description |
|
Students can receive personalized attention for any aspect of the course during the student attention hours and during the hours for solving exercises and practicals in the classroom and, ellectronically, at any other time of the semester. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| IT-based practicals in computer rooms |
|
Students, with the help of the professor, have to solve problems about several course contents. The practical exercises will be assessed. |
50% | ||||
| Short-answer objective tests |
|
Individual final summary exam. The exam will be carried out in the Moodle environment and all the material that the student considers necessary can be consulted, both on paper and in digital format and/or online, and use a calculator. | 50% | ||||
| Others |
| Other comments and second exam session | |||
|
For the second call there will be two options, of which the student will choose one previously: - The first option will consist of an exam of the entire content of the subject and that will be comparable to that of the first call, which will be valued at 100%. - The second option will consist, on the one hand, of an exam with a predominance of basic procedures (with a weight of 50%) and, on the other, of the note of the practicals (with a weight of 50%) that has been obtained in the first call (if it was equal to or greater than 5). If the practice grade is less than 5, then that grade is not saved and the exam is weighted 100%. The final grade for the subject of the student who chooses this second option will be a maximum of 5 points out of 10. Both in the first and in the second call, it is necessary to get a minimum grade of 2 in the final exam to pass the subject. During the evaluation tests, mobile phones and other devices that are not expressly authorized by the test will be turned off and out of sight. Communication between students within the classroom and students receiving outside help are not allowed. The demonstratively fraudulent performance of any evaluation activity of any subject, both in material and virtual and electronic support, entails the student a failing grade for this evaluation activity. Regardless of this, given the seriousness of the facts, the center may propose the initiation of a disciplinary proceeding, which will be initiated by resolution of the rector. |
|||
| Sources of information |
| Basic |
Mateo, J.M., Estadística pràctica pas a pas, , URV |
||||||||
| Complementary | |||||||||
| Recommendations |