The goal of this course is to introduce students to the basics of applied statistics, univariate and multivariate, from a descriptive and inferential point of view. For inference we will take both a frequentist and a Bayesian prospective. Simulation algorithms such as optimization algorithms, Bootstrap, Jackknife, Monte Carlo integration, Importance sampling and Markov chain Monte Carlo will also be introduced. Data and examples will be used to illustrate the theory and the code to perform applied statistical analysis will be either provided by the instructor or developed by the students.
Course Prerequisites
Probability theory
Teaching Methods
Lectures and lab sessions.
Assessment Methods
Final project (to be presented in front of the class) and active in class participation.
Contents
• Exploratory data analysis, (univariate, bivariate, and multivariate) via descriptive statistics and graphical rapresentatios • Review of main random variables, discrete and continuous, using Monte Carlo simulation and random number generators • Population and sample • Point estimation • Confidence intervals • Hypothesis testing • Prediction • Model choice • Linear and logistic regression
Course Language
English
More information
Office hours will take place before and after the start of classes and/or by appointment by emailing: antonietta.mira@uninsubria.it
