The course aims at providing students with specific competences in sampling tecniques and statistical data analysis. Particular relevance is given to probability and inference, as the course means to provide students with the necessary tools for supporting decisional processes through the management of data bases and the use of statistical models.
Curriculum
Canali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofsCanali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofsCanali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofsCanali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofsCanali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofsCanali
teacher profile teaching materials
variables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Statistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Programme
descriptive statisticsvariables and their measurement
univariate distributions
describing data with tables and graphs
measures of position
variability
bivariate descriptive statistics
independence, association, correlation
probability distributions for discrete and continuous variables
sampling distributions
Inference:
estimation
hypothesis test
Core Documentation
A. Agresti, B. FinlayStatistical methods for the social sciences
Pearson International Edition - 4th edition 2009
Reference Bibliography
A. Agresti, B. Finlay Statistical methods for the social sciences Pearson International Edition - 4th edition 2009Type of delivery of the course
It is a traditional course with lectures in the classroom. There are also 2 hours a week dedicated to excersisesType of evaluation
There is a written examination consisting in 3 or 4 numerical excercises to evaluate the degree of knowledge of the subject. teacher profile teaching materials
It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Cicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Programme
The course introduces the fundamental concepts of statistics, distinguishing between descriptive and inferential statistics, and addresses data sources and collection methods, with particular focus on the nature of statistical variables.It covers frequency distributions (simple and grouped), including absolute, relative, and cumulative frequencies, alongside the most suitable graphical representations for different types of variables (qualitative, discrete, continuous).
A substantial section is dedicated to summary measures, including analytical means (arithmetic and weighted means), positional averages (median, mode, quantiles), and the cumulative distribution function. The concept of variability is explored through mean deviations, standard deviation, variance (with properties and demonstrations), the coefficient of variation, and other heterogeneity indices.
The course also explores relationships between variables through joint distributions, contingency tables, and measures of association (Phi coefficient, chi-square statistic, Cramér’s V). The topic of correlation is introduced along with the correlation coefficient.
Focus then shifts to simple linear regression, the least squares method, decomposition of deviance, and goodness-of-fit measures.
In the field of probability, the course discusses random experiments, the classical and frequentist definitions of probability, independent events, and conditional probability (including Bayes' theorem). It covers probability distributions (Binomial, Normal), combinations of random variables, and sampling distributions (especially the sample mean distribution).
Finally, the course concludes with estimation theory and hypothesis testing: introducing estimators, confidence intervals (for proportions and means), and statistical tests, including a discussion on Type I and Type II errors and the use of Normal and Student’s t-distribution tables.
Core Documentation
Statistica – Principi e MetodiCicchitelli G., D’Urso P., Minozzo M.
Ed. Pearson
Introduzione alla Statistica
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P.
Ed. Mc Graw Hill
Type of evaluation
Written exam with exercises and proofs