Guías Docentes Electrónicas
1. General information
Course:
ESTADÍSTICA
Code:
42315
Type:
BASIC
ECTS credits:
6
Degree:
346 - DEGREE IN COMPUTER SCIENCE AND ENGINEERING
Academic year:
2019-20
Center:
604 -
Group(s):
10  11  12 
Year:
2
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English group I
English Friendly:
N
Web site:
Bilingual:
Y
Lecturer: MARIA JOSE HARO DELICADO - Group(s): 12 
Building/Office
Department
Phone number
Email
Office hours
ESII/ 1. E. 14
MATEMÁTICAS
96263
mariajose.haro@uclm.es
Se anunciará en moodle

Lecturer: FRANCISCO PARREÑO TORRES - Group(s): 10  11 
Building/Office
Department
Phone number
Email
Office hours
ESII / 0.A.14
MATEMÁTICAS
Ext. 2289
francisco.parreno@uclm.es
https://www.esiiab.uclm.es/tutorias.php

2. Pre-Requisites

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.

3. Justification in the curriculum, relation to other subjects and to the profession

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.

 

 


4. Degree competences achieved in this course
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.
5. Objectives or Learning Outcomes
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.
6. Units / Contents
  • 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 1.5: Some basic concepts
  • 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
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences ECTS Hours As Com R Description
Progress test [ON-SITE] Lectures INS4 BA1 PER1 0.16 4 Y N Y
Laboratory practice or sessions [ON-SITE] Assessment tests INS4 PER4 PER1 UCLM3 0.6 15 Y Y N
Problem solving and/or case studies [ON-SITE] Cooperative / Collaborative Learning INS4 SIS5 SIS4 BA1 PER1 UCLM3 0.32 8 Y N Y
Project or Topic Presentations [ON-SITE] Lectures SIS5 SIS4 UCLM3 0.24 6 Y Y N
Class Attendance (theory) [ON-SITE] Lectures INS4 BA1 1.28 32 N N N
Study and Exam Preparation [OFF-SITE] Self-study INS4 BA1 1.76 44 N N N
Writing of reports or projects [OFF-SITE] Combination of methods INS4 SIS5 SIS4 BA1 UCLM3 0.84 21 N N N
Other off-site activity [OFF-SITE] Case Studies INS4 BA1 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
8. Evaluation criteria and Grading System
  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%  

Evaluation criteria for the final exam:
The student who does not pass all the minimum tests required in the subject will have a grade not higher than 4.00 even if the average obtained was another, including more than 5.00.
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.
Specifications for the resit/retake exam:
Note the practical parts is saved if the student do not want to repeat the exam to upgrade the note.
The rest is assessed with an exam counting 75% of the grade.
Specifications for the second resit / retake exam:
A practices exam, counting 25% of the grade.
A Problem and theoretical exam, counting 75% of this note.
9. Assignments, course calendar and important dates
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.
10. Bibliography and Sources
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 Ficha de la biblioteca
Montgomery, Douglas C. Probabilidad y estadística aplicadas a la ingeniería Limusa Wiley 978-968-18-5915-2 2007 Ficha de la biblioteca
Walpole, Ronald E. Probabilidad y estadística para ingenieros Prentice-Hall Hispanoamericana 970-17-0264-6 1999 Ficha de la biblioteca



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