Statistics
A.Y. 2018/2019
Learning objectives
Principale obiettivo del corso è quello di fornire strumenti idonei per la descrizione sintetica quantitativa uno o più caratteri di interesse che si rilevano nei più svariati campi (economico, sociologico, politico, amministrativo, storico, giuridico, ecc.) Tale descrizione può essere realizzata aggregando i dati osservati in tabelle, dandone una adeguata rappresentazione grafica, costruendo opportuni indici di posizione e di variabilità, individuando le più opportune misure che ne evidenziano le relazioni. Alla descrizione statistica è necessario affiancare l'inferenza statistica, quando i dati sono tratti da rilevazioni campionarie parziali; in tal caso la conoscenza dei suddetti caratteri non è in termini "certi" ma solo "probabili" ed ha lo scopo di fornire le indicazioni sulla intera collettività di riferimento. Vengono pertanto forniti gli argomenti di base del Calcolo delle probabilità e dell'Inferenza statistica, con particolare riferimento alla teoria della stima.
Expected learning outcomes
Undefined
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second trimester
ATTENDING STUDENTS
Course syllabus
Part I: Descriptive statistics
Frequency tables and data visualization, location (mean, mode and median) and variability (variance, standard deviation, coefficient of variation, heterogeneity) measures, two-way contingency tables, association measures, correlation and simple linear regression.
Parte II: Probability and inference
Random experiments, Kolmogorov probability axioms, random variables, Bayes theorem, Bernoulli, Binomial and Normal distributions, basic sampling theory, sampling distributions, central limit theorem, estimation, confidence intervals for means and proportions.
Frequency tables and data visualization, location (mean, mode and median) and variability (variance, standard deviation, coefficient of variation, heterogeneity) measures, two-way contingency tables, association measures, correlation and simple linear regression.
Parte II: Probability and inference
Random experiments, Kolmogorov probability axioms, random variables, Bayes theorem, Bernoulli, Binomial and Normal distributions, basic sampling theory, sampling distributions, central limit theorem, estimation, confidence intervals for means and proportions.
Website
NON-ATTENDING STUDENTS
Course syllabus
Part I: Descriptive statistics
Frequency tables and data visualization, location (mean, mode and median) and variability (variance, standard deviation, coefficient of variation, heterogeneity) measures, two-way contingency tables, association measures, correlation and simple linear regression.
Parte II: Probability and inference
Random experiments, Kolmogorov probability axioms, random variables, Bayes theorem, Bernoulli, Binomial and Normal distributions, basic sampling theory, sampling distributions, central limit theorem, estimation, confidence intervals for means and proportions.
Frequency tables and data visualization, location (mean, mode and median) and variability (variance, standard deviation, coefficient of variation, heterogeneity) measures, two-way contingency tables, association measures, correlation and simple linear regression.
Parte II: Probability and inference
Random experiments, Kolmogorov probability axioms, random variables, Bayes theorem, Bernoulli, Binomial and Normal distributions, basic sampling theory, sampling distributions, central limit theorem, estimation, confidence intervals for means and proportions.
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professors:
Manzi Giancarlo, Verrecchia Flavio