1. General information
Course:
STATISTICS AND COMPUTATIONAL METHODS
Code:
57306
Type:
BASIC
ECTS credits:
6
Degree:
398 - UNDERGRADUATE DEGREE PROGRAMME IN CHEMISTRY
Academic year:
2020-21
Center:
1 - FACULTY OF SCIENCE AND CHEMICAL TECHNOLOGY
Group(s):
23
20
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Virtual Campus
Bilingual:
N
Lecturer:
ELENA GAJATE PANIAGUA
- Group(s):
20
Building/Office
Department
Phone number
Email
Office hours
340 Margarita Salas
MATEMÁTICAS
Elena.Gajate@uclm.es
Lecturer:
HELIA DA CONCEICAO PEREIRA SERRANO
- Group(s):
23
20
Building/Office
Department
Phone number
Email
Office hours
Margarita Salas/Despacho 327
MATEMÁTICAS
926052237
heliac.pereira@uclm.es
Send an email to make an appointment.
2. Pre-Requisites
To achieve the learning objectives of the subject is required basic knowledge and skills in elementary mathematical operations (powers, logarithms, exponentials, fractions, ...), basic knowledge of derivation and integration of real functions of a real variable, and fundamentals of graphical representation of functions are necessary.
3. Justification in the curriculum, relation to other subjects and to the profession
In any branch of Chemistry, Statistics is an essential tool for data organization, data analysis and interpretation of results in any chemical, academic and professional experimental activity. Likewise, the mathematical concepts studied in the subject of Statistics provide a precise language and help to enhance the capacity for abstraction, rigor, analysis and synthesis that are characteristic of Mathematics and necessary in any other scientific discipline.
4. Degree competences achieved in this course
| Course competences | |
|---|---|
| Code | Description |
| CB01 | Prove that they have acquired and understood knowledge in a subject area that derives from general secondary education and is appropriate to a level based on advanced course books, and includes updated and cutting-edge aspects of their field of knowledge. |
| E17 | Develop the ability to relate to each other the different specialties of Chemistry, as well as this one with other disciplines (interdisciplinary character) |
| G01 | Know the principles and theories of Chemistry, as well as the methodologies and applications characteristic of analytical chemistry, physical chemistry, inorganic chemistry and organic chemistry, understanding the physical and mathematical bases that require |
| T02 | Domain of Information and Communication Technologies (ICT) |
| T03 | Proper oral and written communication |
| T05 | Organization and planning capacity |
| T07 | Ability to work as a team and, where appropriate, exercise leadership functions, fostering the entrepreneurial character |
| T08 | Skills in interpersonal relationships |
5. Objectives or Learning Outcomes
| Course learning outcomes | |
|---|---|
| Description | |
| Know the main approaches for the resolution of numerical methods, use at the user level some software packages for statistics, data processing, mathematical calculation and visualization, program using a high-level programming language, visualize functions and data, design experiments, analyze data and interpret results. | |
| Know and know how to calculate the fundamental parameters of descriptive statistics, approximate two-dimensional data through adjustments to functions, recognize different random variables and manage their tables, estimate statistical parameters, test hypotheses and make decisions. | |
| Get used to teamwork, express yourself orally and in writing, and behave respectfully. | |
| Additional outcomes | |
| Description | |
6. Units / Contents
-
Unit 1: Unidimensional descriptive statistics
-
Unit 1.1: Frequency distributions
-
Unit 1.2: Graphic representation
-
Unit 1.3: Measures of central tendency
-
Unit 1.4: Measures of variation
-
Unit 1.5: Introduction to the software R
-
-
Unit 2: Bidimensional descriptive statistics
-
Unit 2.1: Joint variable distribution
-
Unit 2.2: Simple linear regression
-
Unit 2.3: Correlation and simple regression analysis
-
Unit 2.4: ANOVA - Analysis of Variance
-
Unit 2.5: Nonlinear regression models
-
Unit 2.6: Applications with R
-
-
Unit 3: Introduction to Probability
-
Unit 3.1: Events. Sample space. Probability of an event.
-
Unit 3.2: Conditional probability and independence
-
Unit 3.3: Bayes' Theorem
-
-
Unit 4: Random variables and probability distributions
-
Unit 4.1: Notion of random variable
-
Unit 4.2: Functions of Random Variables
-
Unit 4.3: Mean and variance of a random variable. Chebyshev's theorem
-
Unit 4.4: Discrete probability distributions
-
Unit 4.5: Continuous probability distributions
-
-
Unit 5: Confidence Intervals
-
Unit 5.1: Mean and variance of a sample
-
Unit 5.2: Different estimating errors
-
Unit 5.3: One sample estimating confidence intervals
-
Unit 5.4: Two sample estimating confidence intervals
-
Unit 5.5: Applications with R
-
-
Unit 6: Hypothesis Testing
-
Unit 6.1: Testing a statistical hypothesis
-
Unit 6.2: Unilateral and bilateral hypothesis testing
-
Unit 6.3: Hypothesis testing for one population
-
Unit 6.4: Hypothesis testing for two populations
-
Unit 6.5: Nonparametric hypothesis testing
-
Unit 6.6: Applications with R
-
-
Unit 7: Advanced analysis of variance techniques
-
Unit 7.1: One way analysis of variance
-
Unit 7.2: Two-factor analysis of variance
-
Unit 7.3: Applications with R
-
7. Activities, Units/Modules and Methodology
| Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
| Class Attendance (theory) [ON-SITE] | Lectures | CB01 E17 G01 T02 T03 T05 T07 T08 | 1.16 | 29 | N | N | ||
| Problem solving and/or case studies [ON-SITE] | Guided or supervised work | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.56 | 14 | N | N | ||
| Computer room practice [ON-SITE] | Practical or hands-on activities | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.32 | 8 | Y | Y | ||
| Project or Topic Presentations [ON-SITE] | Group Work | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.04 | 1 | Y | Y | ||
| Progress test [ON-SITE] | Assessment tests | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.04 | 1 | Y | N | ||
| Progress test [ON-SITE] | Assessment tests | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.16 | 4 | Y | Y | ||
| Final test [ON-SITE] | Assessment tests | CB01 E17 G01 T02 T03 T05 T07 T08 | 0.12 | 3 | Y | Y | ||
| Study and Exam Preparation [OFF-SITE] | Self-study | CB01 E17 G01 T02 T03 T05 T07 T08 | 3.6 | 90 | N | N | ||
| Total: | 6 | 150 | ||||||
| Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
| Total credits of out of class work: 3.6 | Total hours of out of class work: 90 | |||||||
As: Assessable training activity Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
8. Evaluation criteria and Grading System
| Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
| Test | 70.00% | 80.00% | |
| Projects | 10.00% | 10.00% | |
| Progress Tests | 10.00% | 0.00% | |
| Assessment of activities done in the computer labs | 10.00% | 10.00% | |
| Total: | 100.00% | 100.00% |
According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).
Evaluation criteria for the final exam:
-
Continuous assessment:Evaluation criteria not defined
-
Non-continuous evaluation:Evaluation criteria not defined
Specifications for the resit/retake exam:
Evaluation criteria not defined
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
| Not related to the syllabus/contents | |
|---|---|
| Hours | hours |
| Project or Topic Presentations [PRESENCIAL][Group Work] | 1 |
| Progress test [PRESENCIAL][Assessment tests] | 1 |
| Progress test [PRESENCIAL][Assessment tests] | 4 |
| Final test [PRESENCIAL][Assessment tests] | 3 |
| Unit 1 (de 7): Unidimensional descriptive statistics | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 1 |
| Computer room practice [PRESENCIAL][Practical or hands-on activities] | 2 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 8 |
| Unit 2 (de 7): Bidimensional descriptive statistics | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 2 |
| Computer room practice [PRESENCIAL][Practical or hands-on activities] | 2 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 10 |
| Unit 3 (de 7): Introduction to Probability | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 3 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 14 |
| Unit 4 (de 7): Random variables and probability distributions | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 7 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 3 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 16 |
| Unit 5 (de 7): Confidence Intervals | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 5 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 2 |
| Computer room practice [PRESENCIAL][Practical or hands-on activities] | 1 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 15 |
| Unit 6 (de 7): Hypothesis Testing | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 2 |
| Computer room practice [PRESENCIAL][Practical or hands-on activities] | 2 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 15 |
| Unit 7 (de 7): Advanced analysis of variance techniques | |
|---|---|
| Activities | Hours |
| Class Attendance (theory) [PRESENCIAL][Lectures] | 4 |
| Problem solving and/or case studies [PRESENCIAL][Guided or supervised work] | 1 |
| Computer room practice [PRESENCIAL][Practical or hands-on activities] | 1 |
| Study and Exam Preparation [AUTÓNOMA][Self-study] | 12 |
| Global activity | |
|---|---|
| Activities | hours |
10. Bibliography and Sources
