IDENTIFYING DATA 2019_20
Subject (*) STATISTICS Code 19224005
Study programme
Bachelor's Degree in Oenology (2014)
Cycle 1st
Descriptors Credits Type Year Period
6 Basic Course First 1Q
Language
Català
Department Chemical Engineering
Coordinator
MATEO SANZ, JOSEP MARIA
E-mail josepmaria.mateo@urv.cat
edilene.pereira@urv.cat
Lecturers
MATEO SANZ, JOSEP MARIA
PEREIRA ANDRADE, EDILENE
Web
Relevant information Learning to efficiently collect and analyze data: description and interpretation of data, sampling, estimation, hypothesis testing, one-way and two-way analysis of variance, regression models.

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. 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. 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.
8.2. Zeros of functions.
8.3. Solving systems of linear 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
CE1
1.2 0 1.2
Lecture
CE1
28 44.8 72.8
IT-based practicals in computer rooms
CE1
28 42 70
Personal attention
A1
0 0 0
 
Short-answer objective tests
A1
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
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 enjoy personalized attention for any aspect of the course during the hours of personal tuition and the hours of problem solving and practical classes.

Personalized attention
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.

Assessment
Methodologies Competences Description Weight        
IT-based practicals in computer rooms
CE1
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
A1
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.


Sources of information

Basic Mateo, J.M., Estadística pràctica pas a pas, , URV

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(*)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.