To pass the course, the student is required to have certain conceptual and argumentative skills, and the equivalent of an introductory course in Calculus and Algebra.

The statistics course is the only course where students learn statistical techniques in the degree. The student must learn to make decisions based on data and how to represent them.

This course aims to:

-Describe and represent large amounts of data through the main measures of location and dispersion and be able to use graphs.

-Help students acquire the necessary skills for modeling situations with "Variability" techniques.

-Basing the decision-making process in general situations on the basis of incomplete information.

-To familiarize the future with computer techniques that directly reflect key statistics related to computer systems situations, and to use in the exercise of their profession.

In addition you will learn to use a SPSS software and a very powerful language such as R, available for free download specific packages and allows for a multitude of tasks statistical program.

Relationship to other subjects.

This is a subject of vital importance that students acquire a working method and a way of thinking and dealing with the difficulties of logic and rigorous manner. The course will take an interdisciplinary sense connecting problems and proposed materials and examples with other subjects of the curriculum. The concepts studied are used in almost all subjects of enhanced smart systems as well as in matters relating to the study of large amounts of data.

The student will describe tools for models with uncertainty and make decisions in the presence of this uncertainty.

Relationship between the profession

Statistics is a transverse field in a wide variety of disciplines, from physics, chemistry to social sciences. In recent decades, the quality control has approached statistical virtually all businesses and is used for decision making in almost all business areas.

In computing, it is common use for reporting and is also frequently used in areas such as data mining where there is an increasing number of computer professionals working. A level consultants, any consultant should have basic knowledge of statistics, like any computer analyst must know based inference techniques.

 

 

Course competences
Code	Description
BA01	Ability to solve mathematical problems which can occur in engineering. Skills to apply knowledge about: lineal algebra; integral and differential calculus; numerical methods, numerical algorithms, statistics, and optimization.
INS04	Problem solving skills by the application of engineering techniques.
PER01	Team work abilities.
PER04	Interpersonal relationship skills.
SIS04	Adaptation to new scenarios.
SIS05	Creativity.
UCLM03	Accurate speaking and writing skills.

Course learning outcomes
Description
Selection of appropriate statistics tools for the analysis of several types of data depending on their type and source.
Use of proper terms in statistics, as well as resoning methods in several real situations.
Use of statistics software for data analysis and extraction of numerical and graphical signs which summarize relevant information.
Additional outcomes
Not established.

• Unit 1: Descriptive Statistics
  • Unit 1.1: Measures of central Tendency
  • Unit 1.2: Measures of central Tendency
  • Unit 1.3: Measures of spread
  • Unit 1.4: Graphing
• Unit 2: Probability
  • Unit 2.1: Conditional probability Theme
  • Unit 2.2: Subject Rule bayes
• Unit 3: Random Variable
  • Unit 3.1: Continuous Random Variables
  • Unit 3.2: Discrete Random Variables
• Unit 4: Foundations for inference
  • Unit 4.1: Sample in random distributions
• Unit 5: Statistical inference
  • Unit 5.1: Puntual estimation
  • Unit 5.2: Interval estimation
• Unit 6: Hypothesis testing
  • Unit 6.1: Parametric testing
  • Unit 6.2: Non parametric testing
• Unit 7: ANOVA
• Unit 8: Regression and Correlation

Training Activity	Methodology	Related Competences (only degrees before RD 822/2021)	ECTS	Hours	As	Com	Description
Progress test [ON-SITE]	Lectures	BA01 INS04 PER01	0.16	4	Y	N
Laboratory practice or sessions [ON-SITE]	Assessment tests	INS04 PER01 PER04 UCLM03	0.6	15	Y	Y
Problem solving and/or case studies [ON-SITE]	Cooperative / Collaborative Learning	BA01 INS04 PER01 SIS04 SIS05 UCLM03	0.32	8	Y	Y
Project or Topic Presentations [ON-SITE]	Lectures	SIS04 SIS05 UCLM03	0.24	6	Y	N
Class Attendance (theory) [ON-SITE]	Lectures	BA01 INS04	1.28	32	N	N
Study and Exam Preparation [OFF-SITE]	Self-study	BA01 INS04	1.76	44	N	N
Writing of reports or projects [OFF-SITE]	Combination of methods	BA01 INS04 SIS04 SIS05 UCLM03	0.84	21	N	N
Other off-site activity [OFF-SITE]	Case Studies	BA01 INS04 PER01	0.8	20	N	N
Total:			6	150
Total credits of in-class work: 2.6			Total class time hours: 65
Total credits of out of class work: 3.4			Total hours of out of class work: 85

As: Assessable training activity
Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).

Evaluation System	Continuous assessment	Non-continuous evaluation *	Description
Oral presentations assessment	10.00%	0.00%	Oral presentation of work/problems ([PRES] 10%).
Progress Tests	50.00%	0.00%	The acquisition of the knowledge will be evaluated by means of two optional partial exams, which will be worth 20% of the grade, for the first partial exam and 80% of the grade for the second partial exam ([ESC] 50%) A minimum score of 3.5 out of 10 is required in each partial to average out the rest of the tests.
Laboratory sessions	25.00%	25.00%	Each practice will be evaluated by means of a questionnaire. And there will be a final questionnaire ([LAB] 25%). Performed individually.
Assessment of problem solving and/or case studies	15.00%	75.00%	A series of exercises proposed by the teacher will be given on the dates indicated ([INF] 15%). Carried out in groups.
Total:	100.00%	100.00%

Not related to the syllabus/contents
Hours	hours

Unit 1 (de 8): Descriptive Statistics
Activities	Hours
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Project or Topic Presentations [PRESENCIAL][Lectures]	2
Class Attendance (theory) [PRESENCIAL][Lectures]	6
Study and Exam Preparation [AUTÓNOMA][Self-study]	3
Writing of reports or projects [AUTÓNOMA][Combination of methods]	1
Other off-site activity [AUTÓNOMA][Case Studies]	3

Unit 2 (de 8): Probability
Activities	Hours
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Problem solving and/or case studies [PRESENCIAL][Cooperative / Collaborative Learning]	2
Class Attendance (theory) [PRESENCIAL][Lectures]	2
Study and Exam Preparation [AUTÓNOMA][Self-study]	4
Writing of reports or projects [AUTÓNOMA][Combination of methods]	2
Other off-site activity [AUTÓNOMA][Case Studies]	3

Unit 3 (de 8): Random Variable
Activities	Hours
Class Attendance (theory) [PRESENCIAL][Lectures]	2
Study and Exam Preparation [AUTÓNOMA][Self-study]	5
Writing of reports or projects [AUTÓNOMA][Combination of methods]	4
Other off-site activity [AUTÓNOMA][Case Studies]	2

Unit 4 (de 8): Foundations for inference
Activities	Hours
Progress test [PRESENCIAL][Lectures]	2
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Project or Topic Presentations [PRESENCIAL][Lectures]	1
Class Attendance (theory) [PRESENCIAL][Lectures]	5
Study and Exam Preparation [AUTÓNOMA][Self-study]	4
Writing of reports or projects [AUTÓNOMA][Combination of methods]	2
Other off-site activity [AUTÓNOMA][Case Studies]	2

Unit 5 (de 8): Statistical inference
Activities	Hours
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Problem solving and/or case studies [PRESENCIAL][Cooperative / Collaborative Learning]	2
Class Attendance (theory) [PRESENCIAL][Lectures]	4
Study and Exam Preparation [AUTÓNOMA][Self-study]	9
Writing of reports or projects [AUTÓNOMA][Combination of methods]	5
Other off-site activity [AUTÓNOMA][Case Studies]	3

Unit 6 (de 8): Hypothesis testing
Activities	Hours
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Project or Topic Presentations [PRESENCIAL][Lectures]	1
Class Attendance (theory) [PRESENCIAL][Lectures]	5
Study and Exam Preparation [AUTÓNOMA][Self-study]	7
Writing of reports or projects [AUTÓNOMA][Combination of methods]	2
Other off-site activity [AUTÓNOMA][Case Studies]	2

Unit 7 (de 8): ANOVA
Activities	Hours
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	2
Problem solving and/or case studies [PRESENCIAL][Cooperative / Collaborative Learning]	2
Project or Topic Presentations [PRESENCIAL][Lectures]	2
Class Attendance (theory) [PRESENCIAL][Lectures]	4
Study and Exam Preparation [AUTÓNOMA][Self-study]	4
Writing of reports or projects [AUTÓNOMA][Combination of methods]	2
Other off-site activity [AUTÓNOMA][Case Studies]	3

Unit 8 (de 8): Regression and Correlation
Activities	Hours
Progress test [PRESENCIAL][Lectures]	2
Laboratory practice or sessions [PRESENCIAL][Assessment tests]	3
Problem solving and/or case studies [PRESENCIAL][Cooperative / Collaborative Learning]	2
Class Attendance (theory) [PRESENCIAL][Lectures]	4
Study and Exam Preparation [AUTÓNOMA][Self-study]	8
Writing of reports or projects [AUTÓNOMA][Combination of methods]	3
Other off-site activity [AUTÓNOMA][Case Studies]	2

Global activity
Activities	hours

General comments about the planning:

This course schedule is APPROXIMATE. It could vary throughout the academic course due to teaching needs, bank holidays, etc. A weekly schedule will be properly detailed and updated on the online platform (Virtual Campus). Note that all the lectures, practice sessions, exams and related activities performed in the bilingual groups will be entirely taught and assessed in English. Classes will be scheduled in 3 sessions of one hour and a half per week.

Author(s)	Title	Book/Journal	Citv	Publishing house	ISBN	Year	Description	Link	Catálogo biblioteca
David M Diez,Christopher D Barr,Mine C etinkaya-Rundel	OpenIntro Statistics							http://www.openintro.org/stat/textbook.php
Devore, Jay L.	Probabilidad y estadística para ingeniería y ciencias			International Thomson	970-686-067-3	2001
Montgomery, Douglas C.	Probabilidad y estadística aplicadas a la ingeniería			Limusa Wiley	978-968-18-5915-2	2007
Walpole, Ronald E.	Probabilidad y estadística para ingenieros			Prentice-Hall Hispanoamericana	970-17-0264-6	1999