Identifying Data 2020/21
Subject (*) Statistics Code 610G02005
Study programme
Grao en Bioloxía
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
 First Basic training 6
Language
Spanish
English
Teaching method Face-to-face
Prerequisites
Department Matemáticas
Coordinador
Jacome Pumar, Maria Amalia
 E-mail
maria.amalia.jacome@udc.es
Lecturers
,
Jacome Pumar, Maria Amalia
López Cheda, Ana
Novo Díaz, Silvia
 E-mail
rebeca.pelaez@udc.es
maria.amalia.jacome@udc.es
ana.lopez.cheda@udc.es
s.novo@udc.es
Web
General description Esta materia proporciona un primeiro contacto do alumnado coas técnicas estatísticas: modelización estatística, ferramentas estatísticas para a análise de datos, procedementos de crítica e diagnose dos resultados e interpretación dos resultados en termos do problema proposto.
Contingency plan 1. Modificacións nos contidos
Non se realizarán cambios

2. Metodoloxías
*Metodoloxías docentes que se manteñen
Proba resposta breve: computa na avaliación (10%), e farase online mediante a plataforma Moodle.udc.es
Traballo tutelado: traballo práctico da parte de Estatística Descritiva (ED), que computa na avaliación (15%).

*Metodoloxías docentes que se modifican
As sesións maxistrais: non computan na avaliación. Impartiranse usando TEAMS na franxa horaria que ten asignada a materia no calendario de aulas da facultade. Ademais, estas sesión por TEAMS poderanse complementar con explicacións con vídeos en formato SCORM, así como cuestionarios de autoavaliación (non avaliables), para que os alumnos poidan comprobar se entenderon ben os conceptos, e vexan o seu progreso.

As prácticas TIC: non computan na avaliación. Na modalidade presencial consistían en análises de datos usando software estatístico (R) nas aulas de informática. Substitúense en moodle.udc.es por vídeos en formato SCORM onde se explica con detalle o desenvolvemento da práctica. Como axuda para o traballo tutelado, contarán en moodle.udc.es cun exemplo de traballo resolto completamente. Ademais, realizaranse titorías grupais semanais por TEAMS (ou mais segundo o demande o alumnado) para seguimento e apoio dos alumnos.

Os seminarios de solución de problemas: non computan na avaliación. Na modalidade presencial impartíanse en grupos reducidos, substitúense en moodle.udc.es por arquivos PDF coa solución detallada dos exercicios, así como un exame de anos anteriores resolto. Ademais, realizaranse titorías grupais semanais por TEAMS (ou mais segundo o demande o alumnado) para seguimento e apoio dos alumnos.

As probas obxectivas: computan na avaliación (75%). Pasan de se realizar presencialmente e modifícanse da seguinte maneira:
(a) Bloque PROBABILIDADE: segue tendo un peso do 40% e realizarase de forma online usando moodle.udc.es
(b) Bloque INFERENCIA (INF): a proba obxectiva presencial, que tiña un peso do 35%, pasaría a unha proba online en moodle.udc.es cun peso do 20% e mais un traballo tutelado da parte de INF cun peso do 15%.


3. Mecanismos de atención personalizada ao alumnado
Correo Electrónico: Diariamente. De uso para facer consultas, solicitar encontros virtuais para resolver dúbidas e facer o seguimento dos traballos tutelados.

Vídeo conferencia (Teams) Unha sesión semanal para tutoría grupal, na que se comprobará o avance dos contidos teóricos e dos traballos tutelados. Realizarase dentro da franxa horaria que ten asignada a materia no calendario de aulas da facultade. Tamén se realizarán titorías individuais e grupais a demanda dos alumnos, fixadas previamente mediante correo electrónico.

Moodle: Diariamente, segundo a necesidade do alumnado. Dispoñen de “foros temáticos” asociados aos módulos da materia, para formular as consultas necesarias


4. Modificacións na avaliación
Proba final: 40% (Probabilidade) + 20% (Inferencia Estatística) Para cada parte, será un pequeno cuestionario test a realizar o día fixado para o exame. Na parte de Probabilidade será preciso achegar por Moodle copia da resolución dos exercicios. Para aqueles estudantes que teñan dificultades técnicas na realización da proba final mediante Moodle, existe a posibilidade de realizar a proba noutra hora ou día.

Traballo en grupo: 15% (Estatística Descriptiva) + 15% (Inferencia Estatística) Traballo en grupo que consiste na análise descriptiva e inferencial dunha pequena base de datos con R.

Cuestionarios test en Moodle: 10% Realización de cuestionarios de tipo test en Moodle de cada un dos temas da materia.


*Observacións de avaliación:
Ao longo do curso realízanse dous parciais, un para a parte de Probabilidade e outro da parte de Estatística Descriptiva e Inferencial, que permite liberar a parte correspondente da materia.
Para aprobar a materia é necesario ter aprobada por separado cada unha das dúas partes das que consta. En caso contrario, de ter superado só un bloque ou ningún, a cualificación final será como máximo un 4.5.
(a) PROBABILIDADE: para superar este bloque a suma da cualificación da proba final e mais dos cuestionarios de avaliación debe ser como mínimo un 4.5 (sobre 10).
(b) ED - INF: para superar este bloque a suma da cualificación do traballo tutelado da parte de ED, da proba final e máis dos cuestionarios de avaliación debe ser como mínimo un 4.5 (sobre 10). Ademais, a cualificación na proba final no pode ser inferior a un 4 (sobre 10).


5. Modificacións da bibliografía ou webgrafía
Non se realizarán cambios. Xa dispoñen de tódolos materiais de traballo dixitalizados en Moodle.

Study programme competencies
Code Study programme competences
A21 Deseñar modelos de procesos biolóxicos.
A26 Deseñar experimentos, obter información e interpretar os resultados.
A30 Manexar adecuadamente instrumentación científica.
B2 Resolver problemas de forma efectiva.
B3 Aplicar un pensamento crítico, lóxico e creativo.
B6 Organizar e planificar o traballo.
B10 Exercer a crítica científica.

Learning aims
Learning outcomes Study programme competences
To design experiments, to get information and to explain the results A21
A26
A30
 B2
B3
B10
To apply an inquisitive, logical and creative reasoning to solving problems effectively. B2
B3
B6

Contents
Topic Sub-topic
Probability Theory Basic concepts on probability theory
Random variables
Basic probability distributions in Biology
Descriptive Statistics Describing univariate data
Describing bivariate data
Statistical Inference Introduction
Point estimation
Interval estimation
Parametric hypothesis testing of one and several samples
Nonparametric hypothesis testing of one and several samples

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Short answer questions A21 B2 B3 B6 2 0 2
ICT practicals A26 A30 B2 B3 B6 B10 13 26 39
Problem solving A21 B2 B3 B6 B10 8 19.2 27.2
Guest lecture / keynote speech A21 A26 B2 B3 B10 24 50.4 74.4
Supervised projects A26 0.5 1.9 2.4
Objective test A26 A30 B2 B3 B10 3 0 3
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Short answer questions Short answer and/or test questions with the aim of controlling the progress in the PROBABILITY contents block.
ICT practicals Practicals in the computer lab to introduce a statistical software helpful to solve problems.
Problem solving Seminars in small groups for the explanation and discussion of problems from the different contents blocks.
Guest lecture / keynote speech Face to face keynote speeches, where the lecturer will show the fundamental parts of the theoretical program, suitably illustrated with practical examples.
Supervised projects Practical project of analysis of a real data set with statistical software
Objective test Final exam, with short answer questions and/or reasoned solution of practical problems, of the DESCRIPTIVE STATISTICS and STATISTICAL INFERENCE theoretical and practical content blocks.

Personalized attention
Methodologies
ICT practicals
Description
Optionally, some academic work consisting on the solution of a practical problem using the statistical software introduced in the ICT practicals, could be carried out.

There will be personalized advice sessions during the development of the practical works. These sessions will take place by means of the interaction teacher/students at the moment of solving the different activities suggested in class: solving doubts, correcting mistakes, suggesting proper approaches to deal with the proposed problems and reviewing initial versions of the works. In addition, students will have the opportunity of receiving personalized advice face-to-face. Personalized advice may be also received via online, by means of e-mail, virtual platform,...

Part-time students may also perform these works and submit them to the teachers for their assessment. Part-time students can also receive personalized assistance using both face-to-face and virtual approaches.

Assessment
Methodologies Competencies Description Qualification
Short answer questions A21 B2 B3 B6 Achievement test to assess the progress in the PROBABILITY THEORY and ED-INF blocks. 10
Objective test A26 A30 B2 B3 B10 Achievement test to assess the knowledge in the PROBABILITY block and DESCRIPTIVE STATISTICS and STATISTICAL INFERENCE blocks.

 75
Supervised projects A26 Practical project to analyze real data set with a statistical software 15
 
Assessment comments

The subject is split in two blocks: 1- Probability Theory and 2-Descriptive Statistics-Statistical Inference. Each block will be assessed independently, so that passing one block will not affect the grade of the other. The score of block PROB is 45% (40% the objective test + 5% short answer questions), and the score of block ED-INF is 55% (supervised project 15% + objective test 35% + short answer questions 5%).

To pass the whole subject, it will be strictly necessary to pass each block separately. Otherwise, the final score will be 4.5 at most:

(a) PROBABILITY: to pass this block the final score (objective test + short answer questions) must be 4.5/10 at least.

(a) ED-INF: to pass this block the final score (objective test + short answer questions + supervised project) must be 4.5/10 at least. Besides, the score of the objective test must be 4/10 at least.

During the course, two exemption exams might be performed, each for any of the two blocks, so that the student who passes any of the blocks with these exemption exams, will have the corresponding block passed regarding the may/july final exams.

To get the grade/mark NP (No grade reported, absent) in may, the student should not have attended any exemption exams nor the official test, nor submitted the supervised project. To get the grade/mark NP in july, the student should not attend the final exam.

The attendance and participation to the seminars, practicals, personalized attention, etc. is not compulsory but it could be additionally with a maximum of one point over and above the final mark.

For the second chance in July, the criterion to pass the subject will be the same as in the  previous chance in June.

All previous observations are applicable to part-time students and/or with academic exemption.

Metodoloxía

Peso na cualificación

Descrición

Objective test

40% (PROB) + 20% (ED -INF)

Online in moodle.udc.es. For the PROB test, the problems solution must be scanned and uploaded. The questions without scanned solution will not be scored.

In case of technical problems, the test might be done in a different time or day.

Supervised projects

15% (ED) + 15% (INF)

P ractical project of analysis of a real data set with statistical software .

Short answer questions

10%

They will be online using Moodle.udc.es, one for each unit of the subject


Sources of information
Basic

• ARRIAZA GÓMEZ, A.J. (2008) Estadística básica con R y R-Commander . Servicio PublicacionesUCA.

• BEHAR GUTIÉRREZ, R. y GRIMA CINTAS, P. (2010). 55 respuestas a dudas típicas de estadística. 2a Ed. Díaz de Santos, Madrid.

• CAMPOS ARANDA, M. (2011). Más de 777 preguntas de Bioestadística y sus respuestas . Murcia, DM.

• CAO ABAD, R. y otros (2001). Introducción a la estadística y sus aplicaciones. Ed. Pirámide.

• DE LA HORRA NAVARRO, J. (2001). Estadística Aplicada. 2ª Edición. Díaz de Santos.

• GONICK, L. Y SMITH, W. (2001). A estatística ¡en caricaturas!. SGAPEIO.

• MARTÍN, A. A. Y LUNA, J. C. (1999). Bioestadística para las Ciencias de la Salud. 4ª Edición revisada. Ediciones Norma.

• MILTON, J. S. (2001). Estadística para Biología y Ciencias de la Salud.3ª edición. McGraw-Hill.

• RIUS DÍAZ, F. y otros. (1999). Bioestadística: Métodos y Aplicaciones. Universidad de Málaga.

• SAMUELS, M. L.; WITMER, J.A. Y SCHAFFNER, A. (2012). Fundamentos de estadística para las ciencias de lavida. 4ª edición. Pearson España

• TOMEO PERUCHA V. y UÑA JUÁREZ I. (2003). Lecciones de Estadística Descriptiva. Paraninfo.

• RIUS DÍAZ, F. y BARÓN LÓPEZ, F.J. (2005). Bioestadística. Thomson.
Complementary

• BARÓ LLINAS, J. (1988). Estadística Descriptiva, Cálculo de probabilidades e Inferencia estadística (tres volúmenes). Ed. Parramón.

• CANAVOS, G.C. (1989). Probabilidad y Estadística. Aplicaciones y métodos. MacGraw-Hill.

• CUADRAS, C.M. y otros (1989). Ejercicios de Bioestadística. Editorial Universitaria de Barcelona.

• HERNÁNDEZ, V. RAMOS, E. y YÁNEZ, I. (1995). Estadística I. ITIS. UNED.

• DANIEL, W. W. (1991). Biostatistics. A Foundation for Analysis in the Health Sciences. J. Wiley.

•FISHER, L.D. Y VAN BELL, G. (1993). Biostatistics. A Methodology for the Health Sciences. John Wiley & Sons.

• JOHNSON, R. A. Y BAHTTACHARIYA, G. K. (1992). Statistical Principes and Methods. J. Wiley.

• MANN, P. S. (1995). Introductory Statistics. J. Wiley & Sons, INC.

• NAVIDI, W. (2006). Estadística para ingenieros y científicos. 1ª Edición, Mc Graw-Hill.

• PAGANO, M. Y GAUVREAU, K. (2001). Fundamentos de Bioestadística. 2ª Edición. Math Learning.

• PEÑA SÁNCHEZ DE RIVERA, D. (1991). Estadística. Modelos y Métodos, 1. Fundamentos. Alianza Universidad.

• QUESADA, V., ISIDORO, A. Y LÓPEZ, L. J. (1984). Curso y Ejercicios de Estadística. Alhambra Universidad.

• ROSNER, B. (1990). Fundamentals of Biostatistics. PWS-KENT Publishing Company.

• SOKAL, R.R. Y ROHLF, F.J. (1995). Biometry. The Principles and Practice of Statistics in Biological Research. 3ª Edición. W. H. Freeman and Company.

• VIEDMA, J. A. (1976). Bioestadística (Métodos Estadísticos Aplicados a la Biología y Medicina). Ed. del autor.

• ZAR, J.H. (1996). Biostatistical Analysis. Prentice Hall International Editions.

ONLINE BOOKS

• BARÓN LÓPEZ, F.J. Bioestadística. https://www.bioestadistica.uma.es/baron/apuntes/clase/apuntes/pdf/bioestadistica-libro.pdf

• SÁEZ CASTILLO, A.J. (2010). Métodos estadísticos con R y R Commander. https://cran.r-project.org/doc/contrib/Saez-Castillo-RRCmdrv21.pdf

• SEEFELD, K. Y LINDER, E. (2007). Statistics Using R with Biological Examples. https://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

BLOGS

• https://365datascience.com/explainer-videos/#statistics

• https://estadisticaorquestainstrumento.wordpress.com/

• https://www.cienciasinseso.com/estadistica/

• https://www.fisterra.com/formacion/metodologia-investigacion/

DATA SET RESOURSES

• https://vincentarelbundock.github.io/Rdatasets/datasets.html

• https://stats.idre.ucla.edu/other/dae/

• http://www.statsci.org/data/first.html

Recommendations
Subjects that it is recommended to have taken before
Mathematics/610G02003

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Data Analysis in Biology/610G02044

Other comments

Highly recommended:

1- Attendance and participation in the keynote sessions, practicals and seminars.

2- To solve every explained exercise, both with and without the statistical software.

3- To supplement the course material with the sources of information.

4- To study the course material and to solve the proposed problems frequently.

5- Active involvement in the practicals and seminars.

6- To become familiar with the statistical software by using it constantly and regularly.

7- To try to use the statistical techniques in other different subjects.

8- Attendance to and taking advantage of the personalized attention sessions.


(*)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.