| Study programme competencies |
| Code | Study programme competences |
| A1 | Capacidade para a resolución dos problemas matemáticos que poidan formularse na enxeñaría. Aptitude para aplicar os coñecementos sobre: álxebra lineal; xeometría; xeometría diferencial; cálculo diferencial e integral; ecuacións diferenciais e en derivadas parciais; métodos numéricos; algorítmica numérica; estatística e optimización. |
| B2 | Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo |
| B3 | Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitiren xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética |
| B4 | Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como leigo |
| B5 | Que os estudantes desenvolvan aquelas habilidades de aprendizaxe necesarias para emprenderen estudos posteriores cun alto grao de autonomía |
| B6 | Ser capaz de concibir, deseñar ou poñer en práctica e adoptar un proceso substancial de investigación con rigor científico para resolver calquera problema formulado, así como de comunicar as súas conclusións –e os coñecementos e razóns últimas que as sustentan– a un público tanto especializados como leigo dun xeito claro e sen ambigüidades |
| B7 | Ser capaz de realizar unha análise crítica, avaliación e síntese de ideas novas e complexas |
| C1 | Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
| C4 | Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Ability to solve statistical problems encountered in engineering. | A1 |
C1 |
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| Using statistical software for solving engineering problems involving randomness and large volume of data. | A1 |
C1 |
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| Capacity for abstraction, understanding, analysis and simplification of instances and processes. | A1 |
B2 B3 B4 B5 B6 B7 |
C1 C4 |
| Contents |
| Topic | Sub-topic |
| The following topics develop the contents established in the tab of the Verification Memory that are: | Exploratory analysis of data. Univariate and multivariate probability distributions. Regression. Statistical inference. Estimation by points and intervals. Contrast of hypotheses. Regression and analysis of variance. |
| Introduction to Statistics | |
| 2. Exploratory data analysis. | |
| 3. Probability. | |
| 4. Ramdom variables. | |
| 5. Discrete random variables and probability distributions. | |
| 6. Continous random variables and probability distributions. | |
| 7. Joint probability distributions. | |
| 8. Statistical inference. | |
| 9. Point estimation of parameters. | |
| 10. Statistical intervals for a single sample. | |
| 11. Test of hypotheses for a single sample. | |
| 12. Regression an analysis of variance (ANOVA). |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A1 B6 B7 C1 C4 | 30 | 36 | 66 |
| Problem solving | B3 B4 B5 C1 C4 | 20 | 18 | 38 |
| ICT practicals | A1 B6 | 10 | 10 | 20 |
| Mixed objective/subjective test | A1 B2 B3 B4 B5 | 3 | 9 | 12 |
| Objective test | A1 B2 B3 B4 | 3 | 9 | 12 |
| Personalized attention | 2 | 0 | 2 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Guest lecture / keynote speech | Lectures about the course topics. |
| Problem solving | Solving exercises and statistical problems encountered in engineering. |
| ICT practicals | Resolution of practical cases of statistical problems by Excel. |
| Mixed objective/subjective test | Midterm exam of the first issues of the subject. |
| Objective test | Final exam |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| ICT practicals | A1 B6 | Evaluation of case studies solved in small groups. | 25 |
| Mixed objective/subjective test | A1 B2 B3 B4 B5 | Midterm exam with test questions and troubleshooting. | 25 |
| Objective test | A1 B2 B3 B4 | Final exam with test questions and troubleshooting. | 50 |
| Assessment comments | |||
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<div>IMPORTANT: Attendance to at least the 80% of classes is required in order to pass the course, unless justified and autorized by the professor. Students who do not meet this requirement will have the qualification of SUSPENSE.</div><div><br /></div><div>The "students with recognition of a part-time academic and exemption of assistance" will communicate at the beginning of the course your situation to the teachers of the subject, as established by the "Standard that regulates the dedication to the study of undergraduates in the UDC "(Art.3.be 4.5) and the" Standards for evaluation, review and claim of the qualifications of undergraduate and master's degree (Art. 3 and 8b).</div><div><br /></div><div>Students in this situation will be assessed on the date approved by the School Board, by an objective test consisting of solving exercises on the contents of step 3 of the Guide.</div><div><br /></div>
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| Sources of information |
| Basic |
Douglas C. Montgomery, George C. Runger (2011). Applied Statistics and Probability for Engineers. John Wiley
García del Valle, Alejandro; Crespo, Diego (2010). Apuntes de Estadística para Ingenieros. Moodle UDC |
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| Complementary |
S. Christian Albright, Wayne Winston, Christopher J. Zappe (1999). Data Analysis &amp;amp;amp;amp;amp; Decision Making with Microsoft Excel. Duxbury
Ronald E. Warpole (1999). Probabilidad y Estadística para Ingenieros. Pearson |
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| Recommendations | ||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus | ||
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| Other comments | |
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There is a very extensive and updated bibliography on Statistics in the library of the Polytechnic School (much of it in English). The notes of the subject will be available in Moodle as well as the proposed cases. |