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 |
| BA1 | 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. |
| INS4 | Problem solving skills by the application of engineering techniques. |
| PER1 | Team work abilities. |
| PER4 | Interpersonal relationship skills. |
| SIS4 | Adaptation to new scenarios. |
| SIS5 | Creativity. |
| UCLM3 | Accurate speaking and writing skills. |
| Course learning outcomes | |
|---|---|
| Description | |
| 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. | |
| Selection of appropriate statistics tools for the analysis of several types of data depending on their type and source. | |
| Additional outcomes | |
| Not established. |
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Unit 1: Descriptive Statistics
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Unit 1.1: Measures of central Tendency
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Unit 1.2: Measures of central Tendency
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Unit 1.3: Measures of spread
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Unit 1.4: Graphing
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Unit 1.5: Some basic concepts
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Unit 2: Probability
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Unit 2.1: Conditional probability Theme
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Unit 2.2: Subject Rule bayes
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Unit 3: Random Variable
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Unit 3.1: Continuous Random Variables
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Unit 3.2: Discrete Random Variables
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Unit 4: Foundations for inference
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Unit 4.1: Sample in random distributions
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Unit 5: Statistical inference
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Unit 5.1: Puntual estimation
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Unit 5.2: Interval estimation
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Unit 6: Hypothesis testing
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Unit 6.1: Parametric testing
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Unit 6.2: Non parametric testing
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Unit 7: ANOVA
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Unit 8: Regression and Correlation
| Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | R | Description * |
| Progress test [ON-SITE] | Lectures | BA1 INS4 PER1 | 0.16 | 4 | Y | N | Y | |
| Laboratory practice or sessions [ON-SITE] | Assessment tests | INS4 PER1 PER4 UCLM3 | 0.6 | 15 | Y | Y | N | |
| Problem solving and/or case studies [ON-SITE] | Cooperative / Collaborative Learning | BA1 INS4 PER1 SIS4 SIS5 UCLM3 | 0.32 | 8 | Y | N | Y | |
| Project or Topic Presentations [ON-SITE] | Lectures | SIS4 SIS5 UCLM3 | 0.24 | 6 | Y | Y | N | |
| Class Attendance (theory) [ON-SITE] | Lectures | BA1 INS4 | 1.28 | 32 | N | N | N | |
| Study and Exam Preparation [OFF-SITE] | Self-study | BA1 INS4 | 1.76 | 44 | N | N | N | |
| Writing of reports or projects [OFF-SITE] | Combination of methods | BA1 INS4 SIS4 SIS5 UCLM3 | 0.84 | 21 | N | N | N | |
| Other off-site activity [OFF-SITE] | Case Studies | BA1 INS4 PER1 | 0.8 | 20 | N | 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 R: Rescheduling training activity
| Grading System | |||
| Evaluation System | Face-to-Face | Self-Study Student | Description |
| Progress Tests | 50.00% | 0.00% | |
| Assessment of problem solving and/or case studies | 15.00% | 0.00% | |
| Oral presentations assessment | 10.00% | 0.00% | |
| Laboratory sessions | 25.00% | 0.00% | |
| Total: | 100.00% | 0.00% | |
The activities of evaluation or recovery of classes could be planned, exceptionally, in the afternoon.
Note the superseded parts is saved.
In the case of not having passed the theoretical part, an examination which must be overcome with a minimum grade of 4 out of 10, counting 50% of the note is held.
The practices and problem will not be recovered in the regular exam session.
The rest is assessed with an exam counting 75% of the grade.
A Problem and theoretical exam, counting 75% of this note.
| 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. The assessment activities could be performed in the afternoon, in case of necessity. |
| 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 |
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| Montgomery, Douglas C. | Probabilidad y estadística aplicadas a la ingeniería | Limusa Wiley | 978-968-18-5915-2 | 2007 |
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| Walpole, Ronald E. | Probabilidad y estadística para ingenieros | Prentice-Hall Hispanoamericana | 970-17-0264-6 | 1999 |
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