Identifying Data 2019/20
Subject (*) ESTATÍSTICA Code 730G04008
Study programme
Grao en enxeñaría en Tecnoloxías Industriais
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
First Basic training 6
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Análise Económica e Administración de Empresas
Economía
Empresa
Matemáticas
Coordinador
Garcia del Valle, Alejandro
E-mail
alejandro.garcia.delvalle@udc.es
Lecturers
Garcia del Valle, Alejandro
E-mail
alejandro.garcia.delvalle@udc.es
Web
General description Este curso ensina os conceptos de Estatística Aplicada á Enxeñaría Industrial

Study programme competencies
Code Study programme competences
A1 FB1 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 CB2 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 CB3 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 CB4 Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como leigo
B5 CB5 Que os estudantes desenvolvan aquelas habilidades de aprendizaxe necesarias para emprenderen estudos posteriores cun alto grao de autonomía
B6 B3 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 B5 Ser capaz de realizar unha análise crítica, avaliación e síntese de ideas novas e complexas
C1 C3 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 C6 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
Be able to solve the mathematical problems of Statistics that can be applied in engineering. 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: Statistics
Introduction to Statistics Introduction Random phenomena. Statistical inference. Stages of a statistical investigation. Problems.
2. Exploratory data analysis. Descriptive statistics. Tabulation of a sample with repetitive data: frequency table. Histogram Cumulative diagram Tabulation of a sample with non-repetitive data: frequency table. Measures of central tendency. Measures of dispersion. Other measures of dispersion. Measures of form. Diagram of boxes and whiskers. Analysis of the stability of the relative frequencies. Problems.
3. Probability. Sample space. Operations with success. Counting techniques Fundamental properties of the frequencies. Axioms of the probabilities. Probability function. Properties deduced from the axioms. Definition of probability according to Laplace. Probability conditioned. Product theorem Total probability theorem. Bayes theorem. Dependence and independence of events. Problems.
4. Ramdom variables. Random variable. Discrete random variable: characteristics. Continuous random variable: characteristics. Tchebycheff's theorem. Characteristic function Transformation of random variables. Problems.
5. Discrete random variables and probability distributions. Introducción. Pruebas de Bernouilli. Distribución binomial. Distribución geométrica. Distribución hipergeométrica. Distribución de Poisson. Aproximación de distribuciones. Problemas.
6. Continous random variables and probability distributions. Introducción. Distribución uniforme. Distribuciones Erlang y gamma. Distribución exponencial. Distribución de Weibull. Distribución normal. Gráficos de probabilidad. Problemas.
7. Joint probability distributions. Distribuciones de probabilidad conjuntas. Función de distribución conjunta. Distribuciones marginales. Variable aleatoria bidimensional discreta. Variable aleatoria bidimensional continua. Variables aleatorias independientes. Variable aleatoria n dimensional. Esperanza matemática. Teoremas de adición. Transformación de variables aleatorias. Teorema central de límite. Problemas.
8. Statistical inference. Statistical sampling. Distributions associated with a sampling process. Distribution of the sample mean. The statistical variance sample. Chi-square distribution of Pearson. Simple random sampling of a normal distribution. Student's t distribution. Student's reason F distribution of Snedecor. Problems.
9. Point estimation of parameters. Estimation by points. Centered estimators. Consistent estimators Sufficiency. Criterion of Neyman-Fisher. Methods of obtaining estimators. Problems.
10. Statistical intervals for a single sample. Confidence intervals. Confidence interval for the mean of a normal population with known variance. Confidence interval for the mean of a normal population with unknown variance. Confidence interval for the variance of a normal population. Confidence interval for the proportion of a population. Problems.

11. Test of hypotheses for a single sample. Contrast of statistical hypothesis. Unilateral and bilateral contrasts. P values in contrast to hypotheses. Connection between hypothesis contrasts and confidence intervals. General procedure for hypothesis contrasts. Test of the mean of a normal population with known variance. Test of the mean of a normal population with unknown variance. Contrast of the variance and standard deviation of a normal distribution. Contrast of the proportion of a population. Contrast of goodness of fit. Contrast with contingency tables. Problems.
12. Regression. Association between random variables. Regression analysis. Quadratic minimum linear regression. Problems.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A1 B2 B3 B4 B5 B6 B7 C4 C1 25 45 70
Problem solving A1 B2 B3 B4 B5 B6 B7 C1 C4 20 20 40
ICT practicals A1 B2 B3 B4 B5 B6 B7 C4 C1 12 18 30
Mixed objective/subjective test A1 B2 B3 B4 B5 B6 B7 C4 C1 3 6 9
 
Personalized attention 1 0 1
 
(*)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.

Personalized attention
Methodologies
Mixed objective/subjective test
ICT practicals
Description
The personalized attention will be made in the tutorials.

Assessment
Methodologies Competencies Description Qualification
Mixed objective/subjective test A1 B2 B3 B4 B5 B6 B7 C4 C1 Midterm exam with test questions and troubleshooting. 70
ICT practicals A1 B2 B3 B4 B5 B6 B7 C4 C1 Evaluation of case studies solved in small groups. 30
 
Assessment comments
<p>First opportunity evaluation: a weighted note will be calculated according to the weights indicated in the Methodologies.</p><p>Second chance evaluation: the same criteria will be followed as for the first opportunity.</p><p>The "students with recognition of part-time dedication and academic exemption of attendance exemption" will communicate at the beginning of the course their situation to the teachers of the subject, as established by the "Standard that regulates the regime of dedication to the study of undergraduate students in the UDC "(Art.3.be 4.5) and the" Standards for evaluation, review and claim of the qualifications of the undergraduate and master's degree studies (Art. 3 e 8b). The students in this situation will be evaluated on the date approved by the School Board, through an additional test that will consist of the resolution of exercises on the contents of step 3 of the Guide. This test is equivalent to "Practices through ICT" and has a weight of 30%.</p>

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

Complementary S. Christian Albright, Wayne Winston, Christopher J. Zappe (1999). Data Analysis &amp;amp;amp;amp;amp;amp;amp;amp; Decision Making with Microsoft Excel. Duxbury
Ronald E. Warpole (1999). Probabilidad y Estadística para Ingenieros. Pearson


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Industrial Management/730G03024
Simulation of Industrial Processes and Optimization/730G04065

Other comments

A sustainable use of resources must be made to prevent the negative impact on the natural environment. For this reason, the delivery of the documentary works carried out in this subject:

• They will be requested in virtual format and / or computer support

• It will be done through Moodle, in digital format without needing to print them

• If it is necessary to make them on paper: a) plastics will not be used, b) double-sided impressions will be made, c) recycled paper will be used, d) the printing of drafts will be avoided.



(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.