Statistics

Universidad de Deusto - Bilbao

Course Description

  • Course Name

    Statistics

  • Host University

    Universidad de Deusto - Bilbao

  • Location

    Bilbao, Spain

  • Area of Study

    Statistics

  • Language Level

    Taught In English

  • Prerequisites

    College level math

    Hours & Credits

  • Contact Hours

    60
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    6
  • Overview

    DESCRIPTION

    The main goal of the course is to provide students with a set of competences for the understanding and application of statistical concepts and techniques in engineering disciplines. Students will learn to represent data information using tables, graphs and parameters in order to facilitate comprehension and decisions; they will be able to identify situations with random behavior and calculate probability of these phenomena. Besides, they will know, identify and classify random variables from different sources of information. Students will learn to identify and solve problems in which the variable under study follows a known probability distribution. They will elaborate, build up and validate statistical models suitable for real problems. They will make use of estimation and inference for studying the behavior of a population model from a sample of the population under study. Student will study two or more variables sets identifying independence and interdependence situations, and be able to assess the importance of statistics and its proper use in specific engineering problems.

    OBJECTIVES

    - Represent data information using tables, graphs and parameters in order to facilitate
    comprehension and decisions.
    - Identify situations with random behaviour and calculate probability of these
    phenomena.
    - Know, identify and classify random variables from different sources of information.
    - Identify and solve problems in which the variable under study follows a known
    probability distribution. Elaborate,build up and validate statistical models suitable for
    real problems.
    - Use of estimation and inference for studying the behaviour of a population model from
    a sample of the population under study.
    - Study two or more variables sets identifying independence and interdependence
    situations.
    - Assess the importance of statistics and its proper use in specific engineering problems.


    CONTENTS
    Chapter 1. Descriptive statistics.
    Type of data. Graphical and numerical methods for describing quantitative and qualitative data.
    Dataset management for descriptive statistics applications.
    Chapter 2. Probability calculation.
    The concept of probability. Experiments and events. Set theory. Interpretations of probability.
    Probability axioms. Study of simple probabilities. Independent events. Conditional probability.
    Total Probability theorem. Bayes rule.
    Chapter 3. Random variable.
    Concept of one-dimensional random variable. Discrete random variables. Continuous random
    variables. Uniform distribution. Distribution function. Transformed distribution. Measurements
    of position. Measurements of dispersion. Centralization moments. Markov's and Chebyshev's
    inequalities. Concept of multidimensional random variable.
    Chapter 4. Models for Discrete variables.
    Bernouilli distribution. Binomial distribution. Geometric distribution. Negative binomial
    distribution. Hypergeometric distribution. Poisson distribution.
    Chapter 5. Models for Continuous variables. Uniform distribution. Normal distribution. Central
    limit theorem.
    Chapter 6. Introduction to statistical inference. Sampling theory.
    Sample and population. Types of sampling. Statistic concept. Sample distributions. Sample
    mean distribution. Corrected sample variance distribution. Statistics and distributions used for
    comparison of normal variables.
    Chapter 7. Parameters Estimation. Hypothesis testing.
    Classical theory of parameters estimation. Point estimation. Confidence interval estimation.
    Hypothesis testing. Classification of hypotheses. Parametrical hypothesis testing. Testing
    process. Statistics for hypothesis testing: mean, mean difference, mean difference paired
    samples, variance, variance quotient, proportion, proportion difference.
    Chapter 8. Linear regression and correlation
    Linear correlation. The simple linear regression model (SLR). Choice of a regression model.
    Prediction.

    METHODOLOGY
    Classroom activities:
    - Lectures explaining the theoretical aspects
    - Resolution of exercises and example problems
    Out-of-class activities:
    - Individual study of lectures material
    - Undertaking of proposed exercises and revision

    ASSESSMENT
    Subject assessment will be done by knowledge tests and also through exercises that must be
    handled in during the course.
    - Three continuous assessment tests will be done in class time. Each of them will have a value
    of 20% of the overall mark. If the student passes them, the corresponding chapters will be
    considered as passed, and it is not necessary to repeat the test of that block in the final exam.
    - After finishing each unit, an activity or a battery of exercises will be proposed. When the
    deadline is finished, the results and indications for auto evaluation will be uploaded to ALUD
    (Deusto online platform). All the activities will be valued with a 20% of the final score.
    - The final test will have four parts. The fourth one will be compulsory for all students, and will
    have a value of 20% of the overall mark. The other three parts will have a value of 60% of the
    overall mark, and will be done by those students who have not passed them during continuous
    assessment tests done during the course.

    READINGS
    Presentations, class notes and exercises statements at ALUD Statistic Course:
    http://alud.deusto.es

    BIBLIOGRAPHY

    Probability and statistics for engineering and the sciences. Jay L. Devore
    Introduction to probability and statistics for engineers and scientist. Ross, Sheldon M
    Probability & Statistics for Engineers and Scientists. Pearson. R.E. Walpole

Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.