Kurser i statistik

The objective of the course is to give an introduction to basic probability theory and statistical inference.  The course is campus based and is given every year in Alnarp, Uppsala and Umeå.  Theobjective and content differs between the three campuses, too see the course specific content visit the individual application pages.

Shared objectives 

• Describe common statistical methods including assumptions.
• Crry out a basic statistical analysis using a computer program.
• Interpret and evaluate results correctly and draw reasonable conclusions.
• Clearly and concisely communicate results and conclusions.

The objective of the course is to give an overview of the basic principles behind design and analysis of replicated factorial experiments aiming at comparing experimental treatments. Examples are given within agricultural sciences and related fields of research. On completion of the course, the student will be able to

• describe basic principles in experimental design, such as replication, blocking and balance
• specify linear fixed- and mixed-effects models, including assumptions
• select an appropriate model for a given experimental design
• use the statistical software R for analysis of fixed- and mixed-effects models
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions.

Content

• Block design, crossed design, hierarchical design and split-plot design
• Analysis of variance
• Linear mixed-effects models for normally distributed observations
• Assumptions and transformation of data
• Multiple comparisons.

Prerequisites

Statistics I or equivalent, i.e. knowledge of basic principles of hypothesis testing and confidence intervals.

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For exact dates and application.

The objective of the course is to give an overview of linear, nonlinear and nonparametric regression. On completion of the course, the student will be able to:

• specify regression models including conditions and assumptions
• select an appropriate regression model for a given problem
• carry out a regression analysis in the statistical software R
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusion.

Content

• Simple linear regression.
• Multiple linear regression.
• Nonlinear models.
• Nonparametric regression and generalized additive models (GAM).
• Analysis of residuals.

Prerequisites

Statistics I or equivalent, i.e. knowledge of basic principles of hypothesis testing and confidence intervals.

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For exact dates and application.

The objective of the course is to give an overview of generalized linear models with applications in the agricultural sciences and related fields of research. On completion of the course, the student will be able to:

• specify generalized linear models including assumptions
• select an appropriate generalized linear model for a given problem
• use the statistical software R for generalized linear modelling
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions.

Content

• Binomial and multinomial logistic regression
• Count regression (Poisson, negative binomial)
• Generalized linear mixed models
• Overdispersion and zero-inflation

Prerequisites

Statistics III: Regression Analysis or equivalent 

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The objective of the course is to provide an overview of machine learning methods. Upon completion, students will be able to:

• identify and select appropriate predictive and classification models for various problems
• implement machine learning models in R
• apply cross-validation and resampling techniques for model assessment
• interpret model results and evaluate their performance critically
• communicate statistical findings effectively in written and oral form.

Content

• Principles of machine learning: over- and underfitting, bias-variance tradeoff, cross-validation.
• Tree-based methods: Decision trees and ensemble methods.
• Artificial neural networks (ANN).
• Unsupervised learning: PCA and clustering.

Prerequisites

Statistics III: Regression analysis or equivalent

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For exact dates and application.

The objective of the course is that the student becomes better at handling and analyzing quantitative data using different statistical methods. After the course the student is expected to be more confident about which methods are appropriate for hers/his research and how to apply these.

Content

• Design of experiments.
• Analysis of variance.
• Regression.
• Generalized linear models.
• Non-parametric methods.
• Multivariate statistical methods.

The content of the final project in the course depends on what specific methods and type of data the participants have in their research project.

Visit the application site

For exact dates and application.