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The program will include the following topics, listed below in chronological order. The lessons will cover both methodological and applied aspects, also through the use of statistical software. Introductory notions: statistical population; statistical unit, variables and categories; data sources; data matrix; descriptive statistics and statistical inference. Statistical distributions: absolute, relative, and cumulative frequencies; frequency distributions and class grouping; bivariate distributions; time series and territorial series. Graphical representations: quantitative variables; qualitative variables; bar charts; charts for time series and territorial series. Measures of central tendency: arithmetic, harmonic, geometric, quadratic, and power means; analytical means for frequency distributions; weighted means; quantiles and other measures of central tendency. Variability: mean deviations; average differences; coefficient of variation; range and interquartile range; measures of concentration for transferable variables (Lorenz curve and Gini index); decomposition of deviance. Shape: skewness and other shape indices based on quantiles. Index numbers: definition; relative and average relative variations; composite index numbers. Analysis of bivariate distributions: graphical representations; marginal and conditional distributions, and statistical independence; measures of dependence; dependence in the mean. Regression: the simple linear regression model; fitting the regression line to data; linear independence and measures of linear association. Probability: random experiments; sample space and events; definitions of probability; assigning probabilities to events; main theorems of probability; conditional probability and independence; Bayes' theorem. Random variables: discrete and continuous variables; cumulative distribution function; expected value and variance of random variables; quantiles. Some probability models: discrete uniform; Bernoulli; binomial; Poisson; exponential; normal and standard normal. Joint random variables: marginal and conditional distributions; covariance and correlation; independence. Law of large numbers and central limit theorem. Population and sample: random sample; population; sample space; statistics and their sampling distributions. Point estimation: properties of estimators (bias and mean squared error); selection criteria. Interval estimation: confidence intervals for the mean of a normal population with known or unknown variance, for the mean of any population in large samples, and for a proportion in large samples.

Knowledge of the main tools of mathematical analysis.

Required textbook: Cicchitelli, D’Urso, Minozzo. Statistics: Principles and methods. Pearson, 2021 Additional material for theory and exercises will be provided in the form of handouts on Google Classroom.

Attendance is strongly recommended.

The final grade will be based on a written exam to assess knowledge of statistical methodologies (60% of the grade) and practical skills (40% of the grade).

The lectures will take place in the classroom according to the schedule.