### General Information

Course Code | E_EOR1_STAT |
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Credits | 6 EC |

Period | P4+5 |

Course Level | 100 |

Language of Tuition | English |

Faculty | School of Business and Economics |

Course Coordinator | mr. M.H.C. Nientker |

Examiner | mr. M.H.C. Nientker |

Teaching Staff |

### Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods | Seminar, Lecture |
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Target audiences

This course is also available as:

### Course Objective

The course statistics is an introduction into the basic concepts ofmathematical statistics. At the end of the course the student can

formulate statistical models, derive different types of estimators,

formulate and execute various hypothesis tests and construct confidence

intervals.

### Course Content

In statistics one tries to make statements about the generating processbehind observed data. The lectures include: statistical models, point

estimations, hypothesis tests, and the construction of confidence

intervals; probability theory and classes of probability distributions

are added to these topics when they are needed. The material is

illustrated on the basis of many (practical) examples. In this course,

the emphasis is on parametric statistics, in which the probability

distribution is known up to a finite number of parameters.

### Teaching Methods

Lectures: 2 times 2 hours of lectures per weekTutorials: 1 time 2 hours of tutorial per week

The purpose of the lectures is to gain new knowledge and insights from

mathematics. As the course progresses so will the level of abstractness.

It is very helpful to prepare yourself by reading relevant sections in

the book and/or lecture notes before you come to the lecture.

In the tutorials we focus on solving statistical problems in the form of

exercises. Most exercises are of an applied nature although some will

ask you to provide a theoretical result. Answers are important, but the

route to the answer is obviously more important than the answer itself.

Note that the exam questions will be very similar to the tutorial

exercises. The tutorials are therefore a useful preparation for the

examination.

### Method of Assessment

Individual assignmentMidterm - individual assessment

Final written exam - individual assessment

### Literature

"Statistical Inference" by G. Casella and R.L. Berger (2008),International Edition of the 2nd revised edition, Cengage Learning.

Lecture notes.

### Target Audience

First year Bsc Econometrie & Operations Research and first year BscEconometrics and Data Science.

### Recommended background knowledge

This course presumes that students are familiar with:- Abstract reasoning

- Calculus

- Probability theory