93264 - Introductory Statistics

Academic Year 2021/2022

  • Moduli: Simone Giannerini (Modulo 1) Simone Giannerini (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: First cycle degree programme (L) in Genomics (cod. 9211)

Learning outcomes

The course covers the fundamental aspects of probability theory and the principles of statistical inference. Upon successful completion of this Course, students are able to perform a rigorous data analysis: i) manipulate and summarize data; ii) visualize and understand relationships inside data; iii) apply the appropriate tools of probability theory and inferential statistics to extract useful information, test hypotheses and make predictions.

Course contents

-- Probability Theory

  1. Fundamentals of probability
  2. Random variables and probability distributions
  3. Functions of a random variable
  4. Bivariate random variables
  5. Convergence of random variables and limit theorems

-- Statistics

  1. Fundamentals of statistics
  2. Point estimation
  3. Interval estimation
  4. Hypothesis testing

-- Modern and reproducible data analysis with R and knitr

  1. Introduction to R
  2. Introduction to knitr

 

Readings/Bibliography

-- Main textbook

-- Probability and mathematical statistics

-- R and knitr

Teaching methods

  • Lectures.
  • Classes.
  • Computer science lab sessions.

All students must attend Module 1, 2 on Health and Safety online

Assessment methods

A two-hour written examination composed of

  • Exercises.
  • Theoretical questions.
In case of online exams, the theoretical part will be assessed by means of an oral examination.

Teaching tools

The following material will be provided:

  • Slides of the lectures.
  • Solved exercises.
  • Mock exam.

Office hours

See the website of Simone Giannerini

SDGs

Quality education

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.