Statistics I

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Statistics I

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    Mathematics, Statistics

  • Language Level

    Taught In English

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview


    - To analyze univariate and bivariate data
    - To solve probability problems
    - To use random variables
    - To demonstrate an understanding of basic concepts and techniques in estimation
    - To be able to solve problems in estimation
    - To be able to solve problems using a statistical software.


    1. Introduction.
    1.1. Concepts and use of Statistics.
    1.2. Statistical terms: populations, subpopulations, individuals and samples.
    1.3. Types of variables.
    2. Analysis of univariate data.
    2.1. Representations and graphics of qualitative variables.
    2.2. Representations and graphics of quantitative variables.
    2.3. Numerical summaries.
    3. Analysis of bivariate data.
    3.1. Representations and graphics of qualitative and discrete data.
    3.2. Representations and numerical summaries of quantitative data: covariance and correlation.
    4. Probability and probabilistic models.
    4.1. Random experiments, sample space, elemental and composite events.
    4.2. Properties of Probability. Conditional Probability and its properties.
    4.3. Random variables and their characteristics.
    4.4. Discrete probability models: Bernoulli variables and related distributions.
    4.5. Continuous probability models: The normal distribution and related distributions.
    4.6. Introduction to the bivariate normal distribution.
    5. Introduction to Statistical Inference.
    5.1. Parameter point estimation.
    5.2. Goodness-of-fit to a probability distribution. Graphical methods.
    5.3. The sample mean distribution.
    5.4. Confidence interval for the mean.


    14 Theoretical support materials available on the Web, and 14 sessions based on problem-solving sessions and
    practical computing. No group tutorials except during the last week.


    60% of the final grade will be achieved by a final examination for assessing the knowledge acquired. A minimum of
    4 points (out of 10) is required in the final exam. The remaining 40% is obtained by two midterm exams (15%+20%)
    and the compulsory tasks assigned in the computational labs (5%). Theoretical questions as well as queries on
    computational laboratories can be asked in the exams.
    % end-of-term-examination: 60
    % of continuous assessment (assigments, laboratory, practicals?): 40

Course Disclaimer

Please note that there are no beginning level Spanish courses offered in this program.

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.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

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.


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