ESTDESC - Descriptive Statistics

Transcription

1 Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: ETSECCPB - Barcelona School of Civil Engineering ECA - Department of Civil and Environmental Engineering BACHELOR'S DEGREE IN PUBLIC WORKS ENGINEERING (Syllabus 2010). (Teaching unit Compulsory) 7,5 Teaching languages: Catalan, Spanish, English Teaching staff Coordinator: Others: JOSE LUIS DIAZ BARRERO JOSE LUIS DIAZ BARRERO, MARÍA ISABEL ORTEGO MARTÍNEZ Opening hours Timetable: To be agreed with the students. Degree competences to which the subject contributes Specific: Ability to solve the types of mathematical problems that may arise in engineering. Ability to apply knowledge of: linear algebra; geometry; differential geometry; differential and integral calculus; differential equations and partial derivatives; numerical methods; numerical algorithms; statistics and optimisation. Transversal: 592. EFFICIENT ORAL AND WRITTEN COMMUNICATION - Level 2. Using strategies for preparing and giving oral presentations. Writing texts and documents whose content is coherent, well structured and free of spelling and grammatical errors TEAMWORK - Level 2. Contributing to the consolidation of a team by planning targets and working efficiently to favor communication, task assignment and cohesion EFFECTIVE USE OF INFORMATI0N RESOURCES - Level 3. Planning and using the information necessary for an academic assignment (a final thesis, for example) based on a critical appraisal of the information resources used SELF-DIRECTED LEARNING - Level 3. Applying the knowledge gained in completing a task according to its relevance and importance. Deciding how to carry out a task, the amount of time to be devoted to it and the most suitable information sources THIRD LANGUAGE. Learning a third language, preferably English, to a degree of oral and written fluency that fits in with the future needs of the graduates of each course. Teaching methodology L'assignatura consta de 5 hores a la setmana de classes presencials a l'aula. Es dediquen a classes teòriques el 45% de les hores setmanals en grup gran, en què el professorat exposa els conceptes i materials bàsics de la matèria, presenta exemples i realitza exercicis. Es dediquen 25% de les hores setmanls, a la resolució de problemes. Es realitzen exercicis pràctics per tal de consolidar Learning objectives of the subject Students will acquire the skills to represent and process data, including basic knowledge of databases and computer 1 / 7

2 software with applications in engineering. They will also learn statistical concepts. Upon completion of the course, students will have acquired the ability to: 1. Carry out a data analysis of a construction engineering problem using a computer tool that employs the studied techniques. 2. Carry out a multiple linear regression analysis using computer software. 3. Carry out data simulations and transformations of random variables, as well as studies of distributions. Data analysis; Regression models, parameter estimation; Probability and uncertainty; Basics of point estimation and interval estimation; Hypothesis testing Study load Total learning time: 187h 30m Theory classes: 34h 18.13% Practical classes: 15h 8.00% Laboratory classes: 26h 13.87% Guided activities: 7h 30m 4.00% Self study: 105h 56.00% 2 / 7

3 Content Exploratory data analysis Learning time: 36h Theory classes: 7h 30m Laboratory classes: 3h 45m Self study : 21h Simulation of data. Qualitative variables, discrete, continuous. Graphical representation. Location measures. Calculation of statistics. Transformacón variables. Simulation data. Qualitative variables, discrete, continuous. Graphical representation. Measures of location and dispersion. Transformation of data. Simulation data. Qualitative variables, discrete, continuous. Graphical representation. Measures of location and dispersion. Transformation of data. Multivariate analysis and data processing. Tables of double entry. Least-square adjustment. Correlation. Trends. Multivariate analysis and data processing. Tables of double entry. Least-square adjustment. Correlation. Trends. Multivariate analysis and data processing. Tables of double entry. Least-square adjustment. Correlation. Trends. Study and graphical representation. Analysis of the trend. Components. Smoothing and seasonal index computations. Study and graphical representation. Analysis of the trend. Components. Smoothing and seasonal index calculations. Study and graphical representation. Analysis of the trend. Components. Smoothing and seasonal index calculations. 3 / 7

4 Elementary Probability. Probability models. Learning time: 48h Theory classes: 7h 30m Laboratory classes: 8h 45m Self study : 28h Counting techniques. Definition of probability. Conditional probability. Bayes Formula. Counting techniques. Discrete Probability. Conditional probability. Bayes Formula. Counting techniques. Discrete Probability. Conditional probability. Bayes Formula. Definition. Distributions of Probability. Discrete and continuous variables. Probability density function. Moments. Discrete distributions: Bernoulli, binomial, geometric, Poisson,... Representation of probability functions. Definition. Distributions of Probability. Discrete and continuous variables. Probability density function. Moments. Discrete distributions: Bernoulli, binomial, geometric, Poisson,... Representation of probability functions. Definition. Probability distributions. Discretes and continuous variables. Probability density function. Moments. Discrete Distributions: Bernoulli, binomial, geometric, Poisson,... Representation of probability functions. Continuous models: uniform, normal, exponential,..... Central limit theorem. Processing methods. Representation of densities. Point processes in time. Return periods. Continuous models: uniform, normal, exponential,..... Central limit theorem. Processing methods. Representation of densities. Point processes in time. Return periods. Continuous models: uniform distribution, normal, exponential,..... Central limit theorem. Transforming variables. Representation of densities. Puntual processes in time. Return periods. 4 / 7

5 Statistical Inference Learning time: 36h Theory classes: 7h 30m Laboratory classes: 3h 45m Self study : 21h Samples. Estimation by the method of moments. Maximum likelihood estimation. Properties of estimators. Examples of estimation. Samples. Estimation by the method of moments. Maximum likelihood estimation. Properties of estimators. Samples. Estimation by method of moments. Concept of likelihood. Most likelihood estimate. Properties of estimators. Concept of testing hypotheses. Applications in normal sampling. Decision rules. Errors type I and II. Power. Test on average and variance of normal populations. T distributions, Chi2, F. Estimation of the p-value. Concept of contrasting hypotheses Applications in normal sampling. Decision rules. Errors type I and II. Power. Contrasts of mean and variance of normal populations. T distributions, Chi2, F. Estimate p-value. Concept of contrasting hypotheses Applications in normal sampling. Decision rules. Errors type I and II. Power. Contrasts of mean and variance of normal populations. T distributions, Chi2, F. Estimation of the p-value. 5 / 7

6 Linear regression model Learning time: 60h Theory classes: 11h 30m Laboratory classes: 9h 45m Self study : 35h Regression model and its extensions. Adjustment by least squares. Usual assumptions of the model. Regression model and its extensions. Adjustment by least squares. Common model assumptions Regression model and its extensions. Adjustment by least squares. Common model assumptions First example. F tests on the regression. T Test on coefficients. Normality of residuals. First example. F tests on the regression. T Test on coefficients. Normality of residuals. First example. F tests on the regression. T Test on coefficients. Normality of residuals. Multiple regression. ANOVA. Factors. Multiple regression. ANOVA. Factors Multiple regression. ANOVA. Factors. Review and synthesis of the contents of theory, problems and laboratory of issues 3 and 4. Evaluation of them by conducting written tests. Review and synthesis of the contents of theory, problems and laboratory issues 1, 2, 3 and 4. Final evaluation of them by conducting written tests for students that fail or to improve the final qualification. Qualification system The mark of the course is obtained from the ratings of continuous assessmentthat consist in several activities carried out during the semester. The assessment tests consist of a part with questions on concepts associated with learning objectives in terms of subject knowledge and understanding, and applying a set of exercises ( problems and practical exercises with computer). The percentages allocated to each part are: theoretical concepts (25%), practical exercises with the aid of a computer (25%) and problems (50%). Criteria for re-evaluation qualification and eligibility: Students that failed ordinary evaluation and have been regularly attending tests throughout the course will have the option to perform a re-evaluation test during the period specified in the academic calendar. The highest mark for the subject in the case of attending the evaluation exam will be five. In the case of justified absences to the regular evaluation tests that prevent the assessment of some parts of the contents of the subject, with prior approval of the Head of Studies, students may get evaluated by the re-evaluation test of the contents that have not been previously examined as well as the contents whose tests students have failed. The limitation on the maximum mark shall not apply to the parts assessed for the first time. Regulations for carrying out activities Failure to perform or continuous assessment activity in the scheduled period will result in a mark of zero in that activity. 6 / 7

7 Bibliography Basic: Devore, J.L. Probabilidad y estadística para ingeniería y ciencias. 8a ed. México DF: Cengage Learning, ISBN Freund, J. E.; Miller, I.; Miller, M. Estadística matemática con aplicaciones. 6a ed. México DF: Prentice Hall, ISBN Ross, S. M. Introduction to probability and statistics for engineers and scientists. 4th ed. Amsterdan: Elsevier, ISBN Ang, A. H-S; Tang, W.H. Probability concepts in engineering: emphasis on applications to civil and enviromental engineering. 2nd ed. New York: Wiley, ISBN Cohen, Y.; Cohen, J.Y. Statistics and data with R : an applied approach through examples. Chichester: Wiley, ISBN Complementary: Canavos, G. C. Probabilidad y estadística: aplicaciones y métodos. McGraw-Hill, ISBN Mood, A. M; Graybill, F.A; Boes, D.C. Introduction to the theory of statistics. 3rd. McGraw-Hill, ISBN Arriaza Gómez, A.J. y otros. Estadística básica con R y R-Commander (llibre on-line) [on line]. Servicio de Publicaciones Universidad de Cádiz, 2008 [Consultation: 27/04/2016]. Available on: <uhttp://knuth.uca.es/moodle/course/view.php?id=37>. ISBN / 7