Basic Statistics

Aims

Statistics and quantitative analytic methods are essential in many scientific disciplines, aiding data-driven decision-making. This course introduces foundational statistical concepts and techniques, emphasizing practical applications and conceptual understanding over theoretical derivations. Examples from real data in the literature, along with ad-hoc simulations will help illustrate statistical concepts and the interpretation of analysis results.

Completing the course will equip students to:

• Explore and describe data using tables and graphs.
• Understand and perform basic statistical inference tasks.
• Implement simple regression models.
• Interpret results from various statistical analyses.

Additionally, the course aims to deepen conceptual understanding through simulation and resampling methods, such as the bootstrap, to enhance the students’ statistical inference skills.

This course is ideal for graduate students and applied researchers in quantitative disciplines who wish to understand and implement their first elementary statistical analyses.

Content

• Defining data science tasks such as description, prediction, and explanation.
• Descriptive statistics and how to compute them: data types, measures of central tendency, dispersion, and association.
• Elementary inferential statistics: populations, sampling distributions, confidence intervals, p-values, hypothesis testing.
• Basics of statistical modelling: regression models for describing relationships between variables.
• Simulation-based approaches: generating random variables, resampling, bootstrapping for confidence intervals.

Methods

The course combines instructor-led sessions introducing concepts and demonstrating techniques, hands-on exercises for practical application, and home assignments to reinforce skills.

Target Group

All Doctoral Candidates

Requirements

Some familiarity with statistical software is advantageous. The course will use R within RStudio for illustrating techniques, but students may choose any statistical software they are familiar with. Working on practical examples and assignment will require a computer, and students should bring a laptop with all necessary software already installed. While the teaching material provides code examples in R, the course does not offer formal R training

About the Trainer

The instructor for this workshop is Giusi Moffa, assistant professor of Statistics at the Department of Mathematics and Computer Science of the University of Basel, with consolidated experience in data analyses supporting research across diverse disciplines, including clinical and biomedical studies and epidemiology.

Workload

Total of 30 hours (16h attendance, 14h preparatory readings and assignments, optioal 1h online session)

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.