The course is intended to give a broad overview of experimental designs and statistical methods in order for students to plan their own experiments and to analyze existing data. Students are recommended to follow the course shortly before they start their bachelor project, their M.Sc. thesis project, or as a part of a Ph.D. study programme.

## Prerrequisitos

The students are assumed to possess a basic knowledge of statistics at a level corresponding to at least “Matematik/Statistik” (1st year of bachelor study). The time schedule does not allow for repetition of basic statistics, so students lacking an up-to-date knowledge are requested to refresh fundamental statistical concepts and principles prior to the course. Although the course puts emphasis on applying statistics, it is unavoidable that some theory will be encountered, so students with poor mathematical skills should consider whether the course fulfils their needs.

Academic qualifications equivalent to a BSc degree is recommended.

## Proceso de aplicación

Self Service at KUnet

## Resultados del aprendizaje

**Knowledge:**

The student will get an overview of a range of statistical concepts and tools:

- Regression
- ANOVA
- Interaction between factors
- Design considerations
- Model check and fit, data representation
- Systematic and random effects
- Logistic regression
- Contingency tables
- Use of statistical software (SAS)

**Skills:**

A student that has successfully finished the course will possess the following qualifications:

- be able to select the appropriate statistical model for the design in question.
- be able to apply General Linear Models (analysis of variance (ANOVA), analysis of covariance (ancova), polynomial and multiple regression, nested and mixed anovas)
- be able to describe multivariate anova (manova), repeated measurements anova, logistic regression and log-linear models.
- be able to develop and apply statistical models that incorporate qualitative (both fixed and random effects) and quantitative variables, main effects, interactions, and second or higher order terms.
- be able to use statistical software (currently SAS) to load a data set, to sort and summarize data, to perform relevant statistical analyses, and to report the results either graphically or in tables.
- be able to estimate the parameters of a statistical model and their standard errors, and to test whether they are significantly different from 0.
- be able to identify significant and non-significant factors so as to simplify the statistical model using various criteria for best fit.
- be able to apply a priori and a posteriori tests to identify treatment differences.
- be able to use the statistical model as a predictive tool to forecast the expected outcome of an observation from a set of independent variables.
- be able to check whether data meet the assumptions of the model and, if needed, to select an appropriate data transformation.

**Competences:**

The student will learn the most commonly used experimental designs and appreciate their advantages with respect to the subsequent statistical analysis of data. The student will be able to select or, if necessary, to develop a statistical model for the experimental design, state the relevant statistical hypotheses, conduct the statistical analysis (generally using statistical software), present the results in a clear and understandable way, and finally interpret the results in a biological context to reach a sound conclusion based on the empirical evidence. In addition, the student should possess the necessary theoretical insight in statistics to be able to understand and comment critically on the use of statistics by others.

## Archivos/Documentos

## Categorías CINE (ISCED)