# Elementary Statistics

Course Code : | 1001WETEST |

Study domain: | Mathematics |

Academic year: | 2017-2018 |

Semester: | 2nd semester |

Sequentiality: | Minimum 8/20 for Calculus. |

Contact hours: | 30 |

Credits: | 3 |

Study load (hours): | 84 |

Contract restrictions: | No contract restriction |

Language of instruction: | Dutch |

Exam period: | exam in the 2nd semester |

Lecturer(s) | Florence Guillaume |

### 1. Prerequisites *

At the start of this course the student should have acquired the following competences:

an active knowledge of

an active knowledge of

- Dutch

Basic knowledge of mathematics

### 2. Learning outcomes *

- you understand the basic concepts of probability theory and know how to apply this theory to solve new probability problems;
- you can analyze a sample with descriptive statistics and interpret the results correctly;
- you learn to deal with data from diverse research domains in a scientific way;
- you understand the general framework of parameter estimation, confidence intervals, hypothesis testing and linear regression and know how to apply this theory in some specific cases (e.g. t-tests for normal variables), in written exercises as well as using statistical software;
- you know the mathematical basis of statistics, learn to think formally and are able to work with the software R.
- given a data set and some specific research questions, you are able to translate the problem on your own and in a creative way into a statistical model and to formulate a correct answer by means of appropriate statistical techniques.
- You can present your results in a written report.

### 3. Course contents *

- The basics of probability theory are explained. The mathematical definition of a probability distribution is given and several probability rules are derived. The student is able to apply the theory in concrete probability problems.
- Some specific discrete and continuous probability distributions are discussed (binomial, normal etc.).
- The basic theory of descriptive statistics, parameter estimation, confidence intervals and hypothesis tests is explained. Specific cases (e.g. t-test for normal distributions) are treated in more detail. The statistical software program R is used to apply this theory.
- Finally a short introduction is given to linear regression. The main focus is on the correct use and interpretation of the output of the software program R.

### 4 International dimension*

### 5. Teaching method and planned learning activities

Class contact teachingLectures Practice sessions Laboratory sessions

Personal workExercises

Personal work

### 6. Assessment method and criteria

Written assignment

Examination

Written assignment

### 7. Study material *

#### 7.1 Required reading

Course notes and transparencies are available through blackboard.

**7.2 Optional reading**

The following study material can be studied voluntarily :Books:

- Montgomery, D.C. and Runger G.C. Applied Statistics and Probability for Engineers.
- Agresti, A. and Franklin, C. Statistics - The Art and Science of Learning from Data.

### 8. Contact information *

Docent: Florence Guillaume (Florence.Guillaume@uantwerpen.be)

Assistent: Valérie De Witte (Valerie.DeWitte@uantwerpen.be)