Statistics II

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Statistics II

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    Business Administration, Economics, Mathematics, Statistics

  • Language Level

    Taught In English

  • Prerequisites

    Statistics I

  • 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


    Students will acquire knowledge and skills necessary to:

    1. Perform statistical inference in one population
    2. Understand the key concepts in hypothesis testing
    3. Become familiar with the issues of comparing two populations
    4. Interpret and apply the concepts of the simple linear regression model
    5. Carry out the abovementioned analyses in statistical software


    Students will be able to:

    1. Develop their ability to think analytically
    2. Become familiar with a statistical software
    3. Establish a framework to solve problems
    4. Develop their interactive skills
    5. Enhance their critical thinking
    6. Improve their learning skills and communication


    Chapter 1. Inference in one population
    Introduction: parameters and statistical inference
    Point estimators
    The estimation of the mean and variance
    The sampling distribution of the sample mean
    Estimation using confidence intervals
    Confidence interval for the mean of a normal population with known variance
    Confidence interval for the mean in large samples
    Confidence interval for the mean of a normal population with unknown variance: t distribution
    Confidence interval for the variance of a normal population

    Chapter 2. Basic concepts in hypothesis testing
    Definition of a test of hypothesis
    The null and alternative hypotheses
    Type I and type II errors, power of the test
    The concept of p-value and decision-making
    Main steps needed to perform a test of hypothesis

    Chapter 3. Comparing two populations
    Independent samples from two populations
    Inference for the population means in small samples
    Inference for the population means in large samples
    Comparing the variances of two normal populations: the F distribution

    Chapter 4. Regression analysis: the simple linear regression model
    The goal of regression analysis
    The specification of a simple linear regression model
    Least-squares estimators: construction and properties
    Inference in the linear regression model
    Inference for the slope
    Inference for the variance
    Mean response and confidence intervals
    New response and prediction intervals

    Chapter 5. Regression analysis: assumptions, model diagnostics, multiple linear regression model
    The residual analysis
    The ANOVA decomposition
    Nonlinear relationships and linearizing transformations
    The linear regression model in matrix form
    Introduction to multiple linear regression

    Theory (3 ECTS): During theoretical sessions, the contents of the course will be introduced, explained and ilustrated with examples. Teaching materials will be provided on the Internet.
    Practice (3 ECTS): During practical sessions, black-board exercises will be solved. Software-related
    activities will take place in the computer labs. In the 15th week of the term, a group-review session for the final exam will be held.


    60% of the semester mark will be obtained from the final exam. To pass the course, a minimum score of 4 (out of 10)
    on the final exam is required.
    The remaining 40% will be based on a continued assessment during the term. Students will be required
    to demonstrate their understanding of theoretical concepts as well as their ability to apply the theory to solve problems.

    The percentages are as follows:
    17,5% from the grade in the first midterm exam.
    17,5% from the grade in the second midterm exam.
    5% from the evaluation of different activities carried out in class.
    The activities in class whose evaluation corresponds to the final 5% of the grade may consist of one or several exercises. This number and the evaluation dates will be set by each group instructor according to the rate of progress of the teaching in the group.

    % end-of-term-examination: 60

    % of continuous assessment (assigments, laboratory, practicals?): 40

    - Paul Newbold Statistics for Business and Economics, Pearson Prentice Hall, 2010
    - Cheng Lee Statistics for business and financial economics, World Scientific, 2000
    - Sheldon Ross Introductory Statistics, Elsevier Academic Press, 2005

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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