Applied Statistics

Learning Objectives

Upon completion of the course, the students will:

• Understand the basic principles of the scientific method: Measurements, data collection, sampling, sampling bias, experimental design
• Identify different types of data and dataset structure
• Be capable to use fundamental statistic methods to process and analyse data
• Be able to set up and use the R programming language and its libraries for data manipulation, statistical analysis and visualisation
• understand distributions, the importance of the normal distribution, the measures of central tendency and variability
• Be able to identify different types of problems and apply the appropriate methodologies to answer research questions
• will be capable to process and transform datasets using R
• will be capable to fit simple and multiple linear models, analyses of variance, categorical data analysis and interpret results from real world datasets

Lectures

Two hours lectures will be divided in one hour of theory and one of hands on tutorial with R software

1. Introduction statistical thinking and R programming
2. Summary statistics
3. Visualisation
4. Random variables and probability distributions
5. Hypothesis testing
6. Univariate statistics
7. Simple and Multiple regressions
8. Categorical data analysis
9. Introduction to Machine Learning
10. Monte Carlo test and Bayesian approaches
11. Power Analysis
12. Introduction to reporting and R Markdown

Recommended Bibliography

• Statistics and Probability in Forensic Anthropology, Obertova Z, Stewart A , Cattaneo C, Elsevier 2020 ISBN:978-0-12-815764-0
• Statistics, An introduction using R, M. J. Crawley, 2^nd Edition, 2015, Wiley, ISBN: 978-1-118-94109-6
• Applied Statistics: theory and problem solutions with R, D. Rasch, R. Verdooren, J. Plutz, 2019, Wiley, ISBN: 978-1-119-55154-6
• Discovering statistics using R, A. Field, J. Miles, Z. Field, 2012, Sage Publishing, ISBN: 9781446289136
• Statistics and the Evaluation of Evidence for Forensic Scientists, 3rd Edition, C. Aitken, F. Taroni, S. Bozza, ISBN: 978-1-119-24522-3
• R for Health Data Science, E. Harrison, R. Pius, Routledge, 2021, ISBN 9780367428198