D. Piccolo (2010) Statistica per le decisioni con R. Il Mulino, Bologna
Learning Objectives
Knowledge: Introductory-level concepts of probability and statistics. Acquired expertise: Students will acquire the ability to organize and analyze a real data set, by an adequate probabilistic model. Moreover, they will be able to critically understand features and limits of models and methods, illustrated during the course.
Prerequisites
Courses required: Analysis I: Integral and Differential Calculus
Teaching Methods
Lectures
Further information
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Type of Assessment
Oral and written examination
Course program
Descriptive statistics: frequency distributions, measures of location and variability. Statistical graphs. Introduction to probability. Conditional probability and independence. Bayes' formula. Discrete and continuous random variables. Introduction to statistical inference: point estimation, hypothesis tests and confidence intervals. Association and independence. Simple and multiple linear regression model. Analysis of variance. Introduction to logistic regression. Introduction to software R.