The teaching is organized as follows:
This course aims at providing the students with the mathematical tools (set-theoretic and algebraic structures, differential and integral calculus in one or several real variables, ordinary differential equations) whose knowledge is indispensable for the achievement of the degree. A particular attention is paid to the concrete application of the learned notions.
This course aims to provide the students with the fundamental of descriptive statistics, inferential statistics and probability theory.
Algebra. Sets, relations and functions. Real numbers. Linear algebra (affine geometry).
Functions of one real variable. Generalities. The topology of the real line, of the extended real line and of affine spaces. Limits, continuity and local behaviour. Derivation. Drawing the graph of a function. Integration.
Functions of several real variables and differential equations. Functions of two or more real variables: generalities, partial derivatives, differential. Ordinary differential equations: the linear case, the separable variables case.
Part I) descriptive statistics.
Univariate statistics: main chart (pie chart, bar chart, histogram e box-plot), measures of location (mean, mode and median), measure of spread (range, interquartile range, variance, standard deviation), measure of asymmetry (third moment, skewness index, Pearson's skewness coefficient) measure of kurtosis (fourth moment, kurtosis, excess kurtosis).
Bivariate statistics: main representations (contingency tables e shattered plots), main measures (mean, variance and covariance), correlation analysis (linear regression and Pearson's correlation coefficient).
Part II) Probability theory
Probability: probability definition (classic and modern), event taxonomy (independent events, mutually exclusive events, complementary event, union event and intersection event). Conditional probability. Probability of notable events.
Random variables: discrete random variable (discrete probability distribution, expected value and variance), continuous random variable (probability density function, expected value and variance), main continuous distributions (uniform, gaussian, standard normal and chi-square).main discrete distributions (binomial and Bernoulli), central limit theorem, Chebyshev’s inequality, convergence in law of random variables and limit random variable.
Part III) Inferential Statistics.
Estimation theory: estimation problem, main properties of an estimator (unbiased, consistency and efficiency). point estimation (expected value and variance), interval estimation (expected value and variance).
Hypothesis test: Problem statement (first type and second type error, theoretical distribution), testing process, chi-square based independence test.
Part IV) Laboratory
Spreadsheet fundamental: scope and main software. Cells main properties: value, format, absolute and relative reference. Calc for basic statistics: single and double entry table, bar plot, shatter plot and linear regression plot.
The exam requires two tests. The former test is composed by close questions while the latter is composed by open questions and exercises. To pass the exam, a student has to get a positive mark in both the tests.
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