The subject is split in two blocks: 1- Probability Theory (PROB) and 2-Descriptive Statistics-Statistical Inference (DS-INF). 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 DS-INF is 55% (supervised project 20% + objective test 30% + 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 out of 10 at least. Besides, the score of the objective test must be 4 out of 10 at least.

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

During the course, one exemption exam for the PROB block might be performed, so that the student who passes the PROB block with this exemption exam, will have the corresponding block passed regarding the May/July final calls.

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

The attendance to the seminars, practical sessions, personalized attention, etc. and involvement along with the performance in the self-evaluation test (not evaluable) might be additionally scored with one extra point at most to be added to the final mark.

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

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

Fraud
in tests or evaluation activities will
directly involve the implementation of the current rules in the Assessment, review and complaint regulation of the UDC and the Student Statute of the UDC

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

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

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	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.

Methodologies	Competencies	Description	Qualification
Short answer questions	A21 B2 B3 B6	Achievement test to assess the progress in the PROB and ED-INF blocks.	10
Objective test	A26 A30 B2 B3 B10	Final test to assess the knowledge in the PROB block and DS-INF blocks.	70
Supervised projects	A26	Practical project in groups to analyze real data set with statistical software	20

Assessment comments
The subject is split in two blocks: 1- Probability Theory (PROB) and 2-Descriptive Statistics-Statistical Inference (DS-INF). 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 DS-INF is 55% (supervised project 20% + objective test 30% + 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 out of 10 at least. Besides, the score of the objective test must be 4 out of 10 at least. (a) ED-INF: to pass this block the final score (objective test + short answer questions + supervised projects) must be 4.5 out of 10 at least. Besides, the score of the objective test must be 4 out of 10 at least. During the course, one exemption exam for the PROB block might be performed, so that the student who passes the PROB block with this exemption exam, will have the corresponding block passed regarding the May/July final calls. To get the grade/mark NP (No grade reported, absent) in the first call (May), the student should not have attended any exemption exams nor the official test, nor submitted any supervised project. To get the grade/mark NP in the second call (July), the student should not attend any final exam. The attendance to the seminars, practical sessions, personalized attention, etc. and involvement along with the performance in the self-evaluation test (not evaluable) might be additionally scored with one extra point at most to be added to the final mark. For the second chance in July, the criterion to pass the subject will be the same as in the first call in June. All previous observations are applicable to part-time students and/or with academic exemption. Fraud in tests or evaluation activities will directly involve the implementation of the current rules in the Assessment, review and complaint regulation of the UDC and the Student Statute of the UDC

Basic
	• ARRIAZA GÓMEZ, A.J. (2008) Estadística básica con R y R-Commander . Servicio PublicacionesUCA.  Available at http://sestio.uca.es/repos/ebrcmdr/pdf/13marzo/ebrcmdr.pdf • 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