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 
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 
Topic 
Subtopic 
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: khisquared, Student’s t and Snedecor’s F.
3.5. Convergence to the normal law: central limit theorem.
3.6. Use of statistical tables.

4. Theory of estimation. 
4.1. Concept of estimator and parameter. Point estimation and interval estimation.
4.2. Properties of estimators: bias, efficiency and consistency.
4.3. Some methods of estimation: method of moments and method of maximum likelihood.
4.4. Notion of confidence interval. Confidence coefficient.
4.5. 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. Oneway design.
6.3. Twoway design without interaction. Random blocks.
6.4. Twoway 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. Goodnessoffit 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.
8.2. Zeros of functions.
8.3. Solving systems of linear equations.
8.4. Numerical integration.
8.5. Numerical solution of differential equations.

Methodologies :: Tests 

Competences 
(*) Class hours

Hours outside the classroom

(**) Total hours 
Introductory activities 

1.2 
0 
1.2 
Lecture 

28 
44.8 
72.8 
ITbased practicals in computer rooms 

28 
42 
70 
Personal attention 

0 
0 
0 

Shortanswer objective tests 

3 
3 
6 

(*) On elearning, 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. 
ITbased 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 enjoy personalized attention for any aspect of the course during the hours of personal tuition and the hours of problem solving and practical classes. 
Description 
Students can enjoy personalized attention for any aspect of the course during the hours of personal tuition and the hours of problem solving and practical classes. 
Methodologies 
Competences

Description 
Weight 




ITbased 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% 
Shortanswer objective tests 

Individual final exam of synthetic type. The only material allowed to be used will be the following: a scientific calculator, statistical tables and a form with a maximum of 3 sheets. 
50% 
Others 




Other comments and second call 
In the second call students can choose between two types of exam of different difficulty. In both cases it is a final individual examination of a synthetic nature where you can only take and consult the following material: scientific calculator, statistical tables and a form of a maximum of 3 sheets. The internship note is saved if it is higher than 5 (in this case, the internship mark and the exam note weigh 50% each). If the practical note is less than 5 this note is not saved and the exam weighs 100%. Given the different difficulty between the two types of final exam, the final grade of the 2nd call will be a maximum of 10 with one type of exam and a maximum of 5 with the other type of exam. During the evaluation tests, mobile phones, tablets and other devices that are not expressly authorized for the test, must be off and out of sight. The demonstrable fraudulent realization of some evaluation activity of a subject both in material and virtual and electronic support entails the student's suspense note of this evaluation activity. Regardless of this, given the seriousness of the events, the center may propose the initiation of a disciplinary file, which will be opened by resolution of the rector. 
Basic 
Mateo, J.M., Estadística pràctica pas a pas, , URV


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