Universidad Carlos III de Madrid
Statistics
25615496
Iowa State Course Substitution
Engineering Statistics
STAT 305
Course Info
International Credits:
6.0
Converted Credits:
3.5
Country:
Spain
Language:
English
Course Description:
Statistics
Department assigned to the subject: Department of Statistics
Type: Basic Core ECTS Credits : 6.0
Year : 1 Semester : 2
Coordinating teacher: VILLAGARCIA CASLA, TERESA
Academic Year: ( 2017 / 2018 ) Review date: 06042017
STUDENTS ARE EXPECTED TO HAVE COMPLETED
Calculus I
Algebra
COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS.
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.
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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
The final grade will be computed giving a 50% weight to the grade in the final exam and a 50% weight to the
continuous evaluation grade. A minimum of 5 points on the final exam will be required.
The continuous evaluation consists of a midterm theoretical exam and a practice exam. The midterm exam will render
10% of the final grade, while the practice exam will render 40%. You have to present the practice workbook and take
the practice exam. Without any of these two things, the grade in the practice part of the course will be 0.
Summarizing:
10% Midterm theoretical exam
40% Practice workbook + Practice exam (May)
50% Final exam
The assessment system applied to the students that have not followed the continuous will be the most favourable to
the student under the University rules.
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% endoftermexamination: 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", McGrawHill.
Review
 Evaluation Date:
 February 20, 2018
 Evaluated:
 Amy Froelich
 Comments:

Calculus background required. Covers extra topics not in STAT 305quality control.