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

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