Programme

Introduction to statistics (1) (2): data collection and descriptive statistics, statistical inference and probabilistic models, population and sample, short history of statistics, sample and census survey, sample survey, sampling techniques, simple random sampling, problems; Descriptive statistics (1) (3): organization and description of data, tables and graphs of absolute and relative frequencies, grouping of data, histograms, olives, steam and leaf diagrams, the quantities that summarize the data, median average and sample fashion, sample variance and standard deviation, sample percentiles and box plots, Chebyshev inequality, normal samples, bivariate data set and sample correlation coefficient, problems; Parametric estimation (1): maximum likelihood estimators, evaluation of the efficiency of point estimators, confidence intervals for the average of a normal distribution with known variance, confidence intervals for the average of a normal distribution with unknown variance confidence intervals for the variance of a normal distribution, confidence intervals for the difference between the means of two normal distributions, approximate confidence intervals for the average of a Bernoulli distribution, problems; Hypothesis testing (1): significance levels, the verification of hypothesis on the average of a normal population, the case in which the variance is known, the case of unknown variance and the t test, checks whether two normal populations have the same average, the case of unknown but equal variances, the testing of hypotheses on a Bernoulli population, confidence intervals for the mean and bilateral tests, independence tests and contingency tables, proble me; Regression (4): estimation of regression parameters with the least squares method, a statistical solution for BLUE estimators, assumptions on the distribution of the model, estimate and hypothesis test for the regression parameters, coefficient of determination, estimate and forecast for a specific value of the explanatory variable, least squares lost (1), problems; Multiple regression (1), (5): estimate of the regression parameters with the least squares method, assumptions on the distribution of the model, estimate and hypothesis test for the regression parameters coefficient of multiple determination, prediction of future answers, problems ; Applications with R (6): examples for the sciences in R code.

Core Documentation

(1) Probabilità e statistica, S. M. Ross, Apogeo - Maggioli Editore, 2015 (2) Lezioni di Statistica descrittiva, L. Pieraccini, A. Naccarato, Giappichelli Editore, 2003 (3) Statistica aziendale, B. Bracalente, M. Cossignani, A. Mulas, McGraw-Hill Editore, 2009 (4) Statistical Inference, G. Casella, R. Berger, Duxbury Advanced Series, 2002 (5) Econometrica, J. Johnston , Franco Angeli, 2001 (6) Introductory Statistics with R, P. Dalgaard, Springer, 2008

Type of delivery of the course

Frontal lectures

Type of evaluation

Oral exam

Programme

Introduction to statistics (1) (2): data collection and descriptive statistics, statistical inference and probabilistic models, population and sample, short history of statistics, sample and census survey, sample survey, sampling techniques, simple random sampling, problems; Descriptive statistics (1) (3): organization and description of data, tables and graphs of absolute and relative frequencies, grouping of data, histograms, olives, steam and leaf diagrams, the quantities that summarize the data, median average and sample fashion, sample variance and standard deviation, sample percentiles and box plots, Chebyshev inequality, normal samples, bivariate data set and sample correlation coefficient, problems; Parametric estimation (1): maximum likelihood estimators, evaluation of the efficiency of point estimators, confidence intervals for the average of a normal distribution with known variance, confidence intervals for the average of a normal distribution with unknown variance confidence intervals for the variance of a normal distribution, confidence intervals for the difference between the means of two normal distributions, approximate confidence intervals for the average of a Bernoulli distribution, problems; Hypothesis testing (1): significance levels, the verification of hypothesis on the average of a normal population, the case in which the variance is known, the case of unknown variance and the t test, checks whether two normal populations have the same average, the case of unknown but equal variances, the testing of hypotheses on a Bernoulli population, confidence intervals for the mean and bilateral tests, independence tests and contingency tables, proble me; Regression (4): estimation of regression parameters with the least squares method, a statistical solution for BLUE estimators, assumptions on the distribution of the model, estimate and hypothesis test for the regression parameters, coefficient of determination, estimate and forecast for a specific value of the explanatory variable, least squares lost (1), problems; Multiple regression (1), (5): estimate of the regression parameters with the least squares method, assumptions on the distribution of the model, estimate and hypothesis test for the regression parameters coefficient of multiple determination, prediction of future answers, problems ; Applications with R (6): examples for the sciences in R code.

Core Documentation

(1) Probabilità e statistica, S. M. Ross, Apogeo - Maggioli Editore, 2015 (2) Lezioni di Statistica descrittiva, L. Pieraccini, A. Naccarato, Giappichelli Editore, 2003 (3) Statistica aziendale, B. Bracalente, M. Cossignani, A. Mulas, McGraw-Hill Editore, 2009 (4) Statistical Inference, G. Casella, R. Berger, Duxbury Advanced Series, 2002 (5) Econometrica, J. Johnston , Franco Angeli, 2001 (6) Introductory Statistics with R, P. Dalgaard, Springer, 2008

Type of delivery of the course

Frontal lectures

Type of evaluation

Oral exam