Statistical Data Analysis

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Statistical Data Analysis

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Mathematics, Statistics

  • Language Level

    Taught In English

  • Course Level Recommendations

    Upper

    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

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    COURSE OBJECTIVE
    This course acquaints the students with the theory and application of several widely used statistical analysis techniques. After completing this course the student knows the theory behind the different techniques and is able to verify which techniques are applicable to a given data set. Using the learned statistical tools, the student is able to summarize and analyze real data sets using the statistical software package R.

    COURSE CONTENT
    This is an advanced level statistical data analysis course that builds on an introductory course on statistics, e.g. Algemene Statistiek. The course introduces the students to several widely used statistical models and methods, and the students are taught how to apply these tools to real data with the use of the statistical software package R.

    The following subjects are covered:

    • summarizing data
    • investigating the distribution of data
    • robust methods
    • non-parametric methods
    • bootstrap
    • two-sample problems
    • contingency tables
    • multiple linear regression.

    The course is a combination of theory (in the lectures) and practice (in the computer classes). Since the solutions of the computer assignments are discussed during the lectures, the theory is explicitly linked to the practice of statistical data analysis.

    TEACHING METHODS
    Lectures, computer classes.

    TYPE OF ASSESSMENT
    Weekly homework assignments in R and written exam.

    RECOMMENDED BACKGROUND KNOWLEDGE
    Students should have basic knowledge on statistics

Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.