# Statistics A

Course Code : | 5001OIWSTA |

Study domain: | Instructional and educational |

Academic year: | 2017-2018 |

Semester: | 1st semester |

Sequentiality: | |

Contact hours: | 14 |

Credits: | 6 |

Study load (hours): | 168 |

Contract restrictions: | No contract restriction |

Language of instruction: | Dutch |

Exam period: | exam in the 1st semester |

Lecturer(s) | Ellen Vandervieren Tine van Daal |

### 1. Prerequisites *

- competences corresponding the final attainment level of secondary school

an active knowledge of

- Dutch
- English

- general knowledge of the use of a PC and the Internet

### 2. Learning outcomes *

- knowledge of the statistical concepts variable and test level;
- ability to make and interpret frequency distribution using R;
- knowledge of parameters of location and dispersion;
- ability to choose the right parameters of location and dispersion, compute and interpret them using R;
- knowledge of parameters of skewness and kurtosis;
- ability to choose the right parameters of skewness and kurtosis, compute and interpret them using R;
- ability to make and interpret descriptive graphs;
- knowledge of the normal distribution and the ability to examine whether a variable is not normally distributed using R;
- knowledge of the standard normal distribution and the ability to use z-scores in computations;
- basic knowledge of statistical concepts from sample theory (sample distribution; central limit theorem; standard error; ...);
- ability to compute and interpret confidence intervals for the mean, variance, skewness, kurtosis and a proportion using R.

### 3. Course contents *

Statistics is an important tool for social scientific research. The aim of this course is to get more insight in some descriptive and inferential statistical techniques that are frequently used. Hereby, the focus is on applying statistics in real research contexts. During this course, you will get familiar with the different attributes of data and the statistical methods that can be applied to synthesize and interpret the most important characteristics. Different univariate techniques are considered, such as computing parameters of location and dispersion. Besides, the essence of sample theory will be explained and illustrated. The techniques that are discussed will all be executed using the statistical software package R.

### 4 International dimension*

### 5. Teaching method and planned learning activities

Personal work

Directed self-study

**5.3 Facilities for working students ***

Classroom activities

- Exercise sessions: free to choose the group division

Directed self-study (possibly with response lecture)

- Blended learning with limited amount of classroom activities in the evening

### 6. Assessment method and criteria

### 7. Study material *

#### 7.1 Required reading

De Maeyer, S., Ardies, J. en Coertjens L. (2011). * Univariate Statistiek voor de humane wetenschappen: een openleerpakket met R. *Available in bookstore Acco.

**7.2 Optional reading**

The following study material can be studied voluntarily :*Dalgaard * ** ** P. (2008), *Introductory statistics with R, 2nd edition*, Springer

### 8. Contact information *

Ellen Vandervieren

(Ve35.107; Tel. 03/265.45.10)

http://www.uantwerpen.be/ellen-vandervieren