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).
The course aims to deepen notions of descriptive and inferencial 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 introduced.
Description / Program
See attached pdf.
Learning Method / Style of Lessons
There will be theoretical and applied frontal lectures.
There will be graded homework assignments and a final exam. The exam (that will count for 70% of the final grade), will comprise two parts: a theory part and a practical part that will be held in the computer lab (to this aim students are requested to get familiar with the computers available in the USI computer lab).
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.