Guías Docentes Electrónicas
1. General information
Course:
STATISTICS
Code:
38306
Type:
BASIC
ECTS credits:
6
Degree:
345 - UNDERGRADUATE DEGREE PROGRAMME IN CIVIL ENGINEERING
Academic year:
2022-23
Center:
603 - E.T.S. CIVIL ENGINEERS OF CR
Group(s):
20 
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: ROSA EVA PRUNEDA GONZALEZ - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Politecnico 2-D33
MATEMÁTICAS
3292
rosa.pruneda@uclm.es

2. Pre-Requisites
Previous knowledge: basic mathematical operations(powers, logarithms, fractions), polynomials, matrices, derivation, integration and graphic representation of functions. Basic computing skills.
3. Justification in the curriculum, relation to other subjects and to the profession
This course provides the necessary skills for analyzing and interpretating data. In many areas of civil engineering the data analysis allows to make decisions in the professional performance. In particular, the contents of this course will be useful in subjects as Technology of Materials, Hydraulic Engineering and Hydrology or Maritime and Coastal Engineering; Safety, reliability, risk and life cycle performance of structures, market analysis, etc. Economics; Transportation, Urban Planning, etc.

4. Degree competences achieved in this course
Course competences
Code Description
CE01 Students can apply their knowledge in the practical solution of civil engineering problems, with capacity for the analysis and definition of the problem, the proposal of alternatives and their critical evaluation, choosing the optimal solution with technical arguments and with capacity of defense against third parties.
CE02 Students have the ability to broaden their knowledge and solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study. Self-study ability, to undertake further studies with a high degree of autonomy
CE04 Students have the ability to solve mathematical problems that may arise in engineering. Ability to apply knowledge of: linear algebra; geometry; differential geometry; differential and integral calculus; differential and partial derivative equations; numerical methods; numerical algorithms; statistics and optimization.
CE06 Students have a basic knowledge of the use and programming of computers, operating systems, databases and software with engineering application.
CG01 Students achieve general knowledge of Information and Communication Technologies (ICT).
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Students know and interpret the fundamental measures of descriptive statistics, approximate data through regression adjustments, know the fundamentals of probability, estimate the parameters of statistical models, build confidence intervals, contrast hypotheses and make decisions.
Students are familiar with computer use: operative systems, databases, programming languages, and software applied to civil engineering.
Students are able to express correctly both orally and in writing and, in particular, they can use the language of mathematics as a way of expressing accurately the quantities and operations in civil engineering. Students get used to teamwork and behave respectfully.
Students use mathematical and computer tools to pose and solve civil engineering problems.
Students learn the most important approximations for numerical method resolution, use some statistical, data processing, mathematical calculation and visualization software packages at user level, develop algorithms and program using a high-level programming language, visualize functions, geometric shapes and data, design experiments, analyze data, and interpret results.
Additional outcomes
Description
Students realize that uncertaint is everywhere and engineers have to deal whit it. They develope skills analyzing the information contained in a data set by means of frequency tables, graphs and statistics. They know the most common models of discrete and continuous random variables and their relationship with engineering problems. They get the most common methods, including probability plots, for the estimation of extreme values ¿¿in engineering designs. They know return period concept for measuring engineering risk and make decisions based on probability, applying the usual estimation methods; contrast of hypothesis, regression, etc.
6. Units / Contents
  • Unit 1: DESCRIPTIVE STATISTICS. Frequency tables. Graphics. Statistics.
  • Unit 2: PROBABILITY. Definition. Properties. Conditional probability. Total probability and Bayes Theorems.
  • Unit 3: RANDOM VARIABLES. One-dimensional variables: Definition. Discrete variables. Probability function. Continuous variables. Density function. Mixed variables. Probability-density function. Distribution function. Two-dimensional variables: Definition. Density, probability and distribution function for two-dimensional variables.
  • Unit 4: DISCRETE VARIABLES. One-dimensional variables: Bernouilli, binomial, negative binomial, pascal or geometric, hypergeometric, poisson. Two-dimensional variables: Multinomial.
  • Unit 5: CONTINUOUS VARIABLES. One-dimensional variables: Uniform, exponential, gamma, beta, normal, log-normal.
  • Unit 6: EXTREME DISTRIBUTIONS. Order Statistics. Distribution of an order statistic. Maximum distribution. Minimum distribution. Extreme distributions. Return period. Critical design values.
  • Unit 7: PROBABILITY PLOTS. Empirical function. Fundamentals of probability plots. Exceedance probability. Return period.
  • Unit 8: ESTIMATION. Punctual and by intervals. Estimation of proportions. Estimation of means. Estimation of variances.
  • Unit 9: HYPOTHESIS CONTRASTS. Fundamentals of the hypothesis contrast. Power of a contrast. P-value. Contrasts of proportions, means and variances. Goodness of fit tests.
  • Unit 10: REGRESSION. Linear regression model. Hypothesis of the model. Matrix form of a regression problem. Analysis of variance. Hypothesis contrasts in the regression models.
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 CE01 CE02 CE04 CE06 CG01 1 25 N N
Class Attendance (practical) [ON-SITE] Problem solving and exercises CE01 CE02 CE04 CE06 CG01 1.08 27 N N
Final test [ON-SITE] Assessment tests CE01 CE02 CE04 CE06 CG01 0.16 4 Y Y Recoverable
Study and Exam Preparation [OFF-SITE] Self-study CE01 CE02 CE04 CE06 CG01 3.24 81 N N
Progress test [ON-SITE] Combination of methods CE01 CE02 CE04 CE06 CG01 0.16 4 Y N
Total: 5.64 141
Total credits of in-class work: 2.4 Total class time hours: 60
Total credits of out of class work: 3.24 Total hours of out of class work: 81

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
Progress Tests 40.00% 0.00% Progress tests and on-line activities.
Final test 60.00% 100.00% Final test
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:
    EXAM (60%) + PROJECTS AND TESTS (40%)
    You need 5 or more out of 10 to pass.

    During the course, 2 partial exams will be carried out. Minimum grade of 4 out of 10 is required in each one of them. The first partial includes the topics 1 to 5 inclusive, the second 6 to 10 inclusive. The average of the two partial exam's score is the EXAM score. In addition, various tests and activities will be carried out, the average of them will be the PROJECTS AND TESTS score.

    Partial exams and / or PROJECTS AND TESTS scores (4 out of 10 minimum) are keept to the Final and Retake.

    The ordinary call will consist of an exam (60%) with two partials (minimum score 4 out 10 each one and final score is the average) and one practical computer test (40%).
    Grades are not saved from previous courses.
  • Non-continuous evaluation:
    The student will have to do a global exam including all the course and competence contents. To pass the course, the student must obtain at least a 5 out of 10 score, which will constitute 100% of his/her grade.

    Unless stated otherwise, continuous evaluation criteria will be applied to all students.

    Anyone choosing non-continuous assessment must notify it to the lecturer within the class period of the subject. The option is only available if the student's participation in evaluation activities (from the continuous assessment) has not reached 50% of the total evaluation for the subject.

    For the retake exam, the assessment type used for the final exam will remain valid.
    Grades are not saved from previous courses.

Specifications for the resit/retake exam:
Same criteria that the final exam. All the exam and projects and tests are recoverable.
Specifications for the second resit / retake exam:
The student will have to do a global exam that will include all the course and competences content. To pass the course, the student must obtain at least a 5 out of 10 score, which will constitute 100% of his/her grade.
Grades are not saved from previous courses.
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Final test [PRESENCIAL][Assessment tests] 4
Study and Exam Preparation [AUTÓNOMA][Self-study] 75

Unit 1 (de 10): DESCRIPTIVE STATISTICS. Frequency tables. Graphics. Statistics.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 3
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 2 (de 10): PROBABILITY. Definition. Properties. Conditional probability. Total probability and Bayes Theorems.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 3

Unit 3 (de 10): RANDOM VARIABLES. One-dimensional variables: Definition. Discrete variables. Probability function. Continuous variables. Density function. Mixed variables. Probability-density function. Distribution function. Two-dimensional variables: Definition. Density, probability and distribution function for two-dimensional variables.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 3
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 4 (de 10): DISCRETE VARIABLES. One-dimensional variables: Bernouilli, binomial, negative binomial, pascal or geometric, hypergeometric, poisson. Two-dimensional variables: Multinomial.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 3

Unit 5 (de 10): CONTINUOUS VARIABLES. One-dimensional variables: Uniform, exponential, gamma, beta, normal, log-normal.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 6 (de 10): EXTREME DISTRIBUTIONS. Order Statistics. Distribution of an order statistic. Maximum distribution. Minimum distribution. Extreme distributions. Return period. Critical design values.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 7 (de 10): PROBABILITY PLOTS. Empirical function. Fundamentals of probability plots. Exceedance probability. Return period.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 3

Unit 8 (de 10): ESTIMATION. Punctual and by intervals. Estimation of proportions. Estimation of means. Estimation of variances.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 9 (de 10): HYPOTHESIS CONTRASTS. Fundamentals of the hypothesis contrast. Power of a contrast. P-value. Contrasts of proportions, means and variances. Goodness of fit tests.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 2
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Unit 10 (de 10): REGRESSION. Linear regression model. Hypothesis of the model. Matrix form of a regression problem. Analysis of variance. Hypothesis contrasts in the regression models.
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 3
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] 2

Global activity
Activities hours
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
 
 
Castillo, Enrique Introducción a la Estadística Aplicada con Mathematica [s.n.] 84-604-0299-1 1991 Ficha de la biblioteca
Castillo, Enrique; Pruneda, Rosa Eva Introducción a la Estadística Aplicada Moralea 84-923157-4-1 2001 Ficha de la biblioteca
Devore, Jay L. Probabilidad y estadística para ingeniería y ciencias / CENGAGE Learning, 978-607-522-828-0 2016 Ficha de la biblioteca
Peña, Daniel Fundamentos de Estadística Alianza Editorial 978-84-206-8380-5 2008 Ficha de la biblioteca
Spiegel, Murray R. Estadística McGraw-Hill 978-970-10-6887-8 2009 Ficha de la biblioteca
Walpole, Ronald E. Probability and Statistics for Engineers and Scientists Pearson Educación 978-970-26-0936-0 2007 Ficha de la biblioteca



Web mantenido y actualizado por el Servicio de informática