ME420 Statistics

Contents and structure

Students will receive training in data exploration and analysis of different types of data, interpreting patterns in these data with statistical tests, from simple classic tests such as chi-squared, t-test, wilcoxon rank sum test, correlation and ANOVA, to more advanced techniques such as multiple regression. Students will also receive training in estimating uncertainty/confidence intervals, experimental design, planning the collection and presentation of data. Use of the programme R will also be taught.

Learning Outcome

Knowledge: After completion of the course students will be able to:

• understand the importance of statistics in research
• understand the most important principles behind statistics
• thick critically in relation to reported statistics
• Consider nature more objectively taking into account patterns and trends in statistics

Skills: After completion of the course students will be able to

• apply some of the most important statistical methods to a range of datasets
• plant field research for a bachelor thesis, taking into account good statistical practice
• use the most commonly used statistical software

General qualifications: After completion of the course, students will be able to:

• think critically in relation to statistics reported in the media, reports and scientific papers
• Consider nature more objectively in terms of patterns and trends
• perform statistical analyses required in e.g., bachelor thesis

Recommended previous knowledge

None

Teaching methods

Lectures and seminars (ca.1/4) and practical exercises (ca. 3/4). Students need their own laptop computer for the practical exercises

Compulsory learning activities

1) Short written assignment delivered at the end of week 1. Students detail the experimental design of their own project that they will complete during the course

2) A text document submitted during week 4 consisting of annotated R code. Students collect data for their projects during weeks 2-4 and complete basic statistics using R.

Assessment

Oral exam

Students give a presentation of their own project, detailing the methods they used to analyse and interpret the data, as well as considering alternative methods.

Examination support material

Own laptop, text books, etc

More about examination support material