The course assumes prior knowledge of the following topics:
Probability of an event; Discrete and continuous random variables.
Probability distribution function, density function and cumulative distribution function.
Conditional probability and distribution. Law of total probability, Independence of events, Bayes Theorem. Expectation and variance of a random variable; Some specific random variables (Bernoulli, Binomial, Uniform, Gaussian).
Basic knowledge of the freeware statistical software R Project.
The course aims to deepen notions of descriptive and inferential statistics both from a theoretical and an applied point of view. The students will be able to analyze a given data set. The freeware statistical software R Project will be used.
Description / Program
See attached pdf.
Learning Method / Style of Lessons
There will be theoretical and applied frontal lectures.
Compliant with COVID-19 guidelines.
Class participation is a mandatory component of the course.
There will be a final exam that will comprise 100% of the final grade.
The exam will consist of a set of multiple choice questions (with only forward scrolling allowed) with additional open questions in form exercises similar to homeworks and in-class exercises.
If the exam will be entirely online for University restrictions, open questions will be converted to multiple-choice questions of higher value (towards the final grade) than the remaining ones.
Students are requested to bring to class their own laptop, if available.
There is no specific text book. Class notes and slides will be distributed during the course. For the theory part of the course, a good reference book is Introduction to the Theory of Statistics by A. M. Mood, F. A. Graybill, D. C. Boes; Publisher: McGraw-Hill 1974. The book is out of print and can be downloaded here http://www.ebooksdirectory.com/details.php?ebook=3627
For the applied part of the course students are referred to the online material available available here https://www.r-project.org
Lecture notes will also be provided.