Checking date: 30/04/2019

Course: 2019/2020

Study: Bachelor in Industrial Electronics and Automation Engineering (223)

Coordinating teacher: DELGADO GOMEZ, DAVID

Department assigned to the subject: Department of Statistics

Type: Basic Core
ECTS Credits: 6.0 ECTS


Branch of knowledge: Social Sciences and Law

Students are expected to have completed
Calculus I Algebra
Competences and skills that will be acquired and learning results. Further information on this link
In today's world there is an enomous amount of available information. There are diverse sources and many of them are accessible through the Internet. To analyze this information and draw valid conclusions we need to use some specific techniques. Statistics is the most widely used and the most successful technique. In this course we will learn how to obtain information from the data with techniques that you will use both in your studies and in your professional career, because these techniques are commonly used by most companies and organizations. Today a statistical analysis is inconceivable without computer resources. Therefore the teaching of Statistics will rely heavily on computer practices and a part of the final exam will be held in a computer classroom. After completing this course, you should be able to extract information from the data and to express those conclusions in a written report. Also, you can establish relationships between variables using the regression model and to interpret the model properly.
Description of contents: programme
Topics: 1. Descriptive Statistics 1.1 Qualitative and Quantitative data. 1.2 Univariate Descriptive Statistics. 1.2.1 Summary of data using frequency tables. 1.2.2 Graphical representation of data. ¿ Graphical representation for qualitative data: Bar chart, pie chart, Pareto diagram. ¿ Graphical representation for quantitative data: Histograms, frequency polygons, boxplots. 1.2.3 Analytical measures for data summary. ¿ Measures of central tendency: Average, median and mode. ¿ Measures of variability: Variance, Coefficient of Variation, Median, Quartiles and Percentiles. ¿ Other Measures: Skewness and kurtosis. 1.3 Descriptive statistics for two variables. Scatter plots. Covariance and correlation. 2. Probability 2.1 Introduction to the concept of probability: ¿ Equiprobability and Laplace rule. ¿ Frequentist approach and law of large numbers. 2.2 Events and operations with events. Event definition. Venn diagrams. Union, Intersection and complementary events. 2.3 Definition and properties of the probability. 2.4 Independence and conditional probability. 2.5 law of total probability. 2.6 Bayes Theorem. 3. Random variables and probability models 3.1 Definition of random variable (discrete / continuous) and properties. Probability function, density function. 3.2 Expectation and variance of discrete and continuous random variables. 3.3 Distribution function. 3.4 Probability Models for discrete random variables. Bernoulli, Binomial. 3.5 Probability Models for continuous random variables. The normal distribution. The central limit theorem. 4. Statistical Inference 4.1 Introduction to statistical inference. Population and sample. Distribution of the sample mean. 4.2 Confidence intervals for the sample mean. 5. Hypothesis Testing 5.1 Population and sample (review). 5.2 Null hypothesis and alternative hypothesis. 5.3 Hypothesis testing for the mean, proportion and variance of one population. 5.4 Hypothesis testing for two populations: Difference of means and proportions. 6. Quality control 6.1 Introduction to quality control 6.2 Control charts for variables. Control charts for the mean and range. Process capability. 6.3 Control charts for attributes. P and np control charts. 7. Regression 7.1 Introduction to linear regression. 7.2 Simple regression. ¿ Hypothesis. ¿ Estimation of parameters. Significance and interpretation ¿ Diagnosis. 7.3 Multiple regression. ¿ Hypothesis. ¿ Estimation of parameters, significance and interpretation ¿ Diagnosis ¿ Multicollinearity 7.4 Regression with qualitative variables (dichotomous / polytomous).
Learning activities and methodology
- Lecture: 2,5 ECTS - Problem solving sessions (in small groups): 1,5 ECTS - Computes sessions (consistent of individual work out of the classroom with programmed tutorial sessions) 1,5 ECTS - Evaluation sessions (continuos evaluation, some of them at computes laboratories): 0,5 ECTS
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
  • PEREZ, C.. "Estadística práctica con Statgraphics". 2000.
  • PEÑA, D. Y ROMO, J.. "Introducción a la Estadística para las Ciencias Sociales". McGraw-Hill.

The course syllabus and the academic weekly planning may change due academic events or other reasons.