# MAT-20306 Advanced Statistics

## Course

Credits 6.00

Teaching method | Contact hours |

Practical intensively supervised | 24 |

Tutorial | 36 |

Self-study |

### Language of instruction:

English

### Assumed knowledge on:

MAT-11806 or MAT-14303 or MAT-15403

The student should be familiar with 1) The principles of probability calculus and the subjects: estimation, construction of confidence intervals and hypothesis testing from statistical inference 2) Application of these principles to inference about central values (mean or success probability) for the 1-sample and 2-sample situations, in case of Normal observations and binary (0,1) observations 3) Methods of analysis for simple (one explanatory variable) linear regression and one-way ANOVA.

(To refresh this knowledge, (parts of) chapters 1 to 6, 8 and 11 of the book can be studied.)

### Contents:

Brief overview of (a) the principles of inference and (b) inference about means in the 1- and 2-sample situation, including non-parametric procedures.
Choosing the sample size required to obtain a given precision in the 1- and 2-sample situations.

Multiple linear regression: 1) model formulation and meaning of model parameters and 2) inference on (a) a single parameter (b) a linear combination of model parameters (c) several model parameters simultaneously.

Factorial designs: completely randomized design for 1 and 2 factors, block designs.

Two-way analysis of variance: additive and interaction models, overparametrization, F-tests for interaction and/or main effects, t-tests for one parameter or a linear combinations of parameters.

Inference (notably Chi-Square tests) for (count) data summarized in a contingency table.

### Learning outcomes:

After the course the student should (within the limits of the subjects treated) be able to:

- translate a research question into a statistical hypothesis: make a plan (type of design or sampling procedure) for the data collection.

- choose an appropriate model with an understanding of the ingredients of the model in relation to the data;

- analyze the data (with SPSS);

- interpret the results and form conclusions relevant for the actual problem;

- assess and if necessary criticize the sampling procedure, choice of model or analysis of a reported experiment.

### Activities:

1. lectures: follow classes, study the book, make exercises;

2. computer practicals (compulsory): (learn how to) use SPSS and PQRS, work on case studies.

### Examination:

Written exam. The computer practical should result in a pass.

### Literature:

- Statistical Methods and Data Analysis by R. Lyman Ott and Michael Longnecker (ISBN 0495109142: sixth edition);

- lecture notes available in English (Wur Shop).

Programme | Phase | Specialization | Period | ||
---|---|---|---|---|---|

Compulsory for: | BAS | Animal Sciences | BSc | 1AF, 1MO, 2AF, 2MO, 6MO | |

BPW | Plant Sciences | BSc | 1MO | ||

MFT | Food Technology | MSc | G: Sensory Science | 2MO | |

BEB | Economics and Governance | BSc | 2MO | ||

Restricted Optional for: | MOA | Organic Agriculture | MSc | 1AF | |

MAS | Animal Sciences | MSc | 1AF, 1MO, 2MO, 2AF, 6MO | ||

MES | Environmental Sciences | MSc | 2AF | ||

MPS | Plant Sciences | MSc | C: Natural Resource Management | 1MO | |

MPS | Plant Sciences | MSc | D: Plant Breeding and Genetic Resources | 1MO | |

MNH | Nutrition and Health | MSc | D: Sensory Science | 2AF | |

MFQ | Food Quality Management | MSc | 6MO | ||

MAM | Aquaculture and Marine Resource Management | MSc | B: Marine Resources and Ecology | 2MO, 2AF | |

MAM | Aquaculture and Marine Resource Management | MSc | A: Aquaculture | 2MO, 2AF |