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
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.Canali
teacher profile teaching materials
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.Canali
teacher profile teaching materials
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.Canali
teacher profile teaching materials
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.Canali
teacher profile teaching materials
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.Canali
teacher profile teaching materials
Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Programme
An introduction to the fundamental ideas of statistics. Topics include:Descriptive statistics: graphical summaries of data, measuring the center of quantitative data (mode, median, mean), measuring the variability of quantitative data (standard deviation, variance), simmetry of a distribution (Fisher coefficient), the association between two variables (chi-square), the association between two quantitative variables (covariance, correlation coefficient).
Probability: probability of events (probability of the complement of an event, of the union and intersection of two events, conditional probability, independent events, Bayes theorem), discrete and continuous random variables (the Bernoully r.v., the Binomial r.v., the Poisson r.v., the Normal r.v., the Student t r.v., che Chi-square r.v.).
Inferential statistics: sample versus population, sampling distributions, point and interval estimates of population parameters, confidence intervals for a population mean and a proportion, significance tests about hypotheses on a population mean and a proportion, p-values.
Regression analysis: the regression line, ordinary least squares, describing the strength of the association, nonlinear regression models, confidence intervals and tests on the parameters of the regression line, prediction.
Core Documentation
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, fourth edition:Chap. 1 (excluding 1.11), Chap. 2 (excluding 2.4, 2.5), Chap. 3 (excluding 3.4, 3.5, 3.6), Chap. 4 (excluding 4.3, 4.4, 4.5, 4.6, 4.15), Chap. 5 (excluding 5.3, 5.6, 5.7, 5.8), Chap. 6 (excluding 6.3), Chap. 9 (excluding 9.3), Chap. 10, Chap. 11 (excluding 11.2.1), Chap. 12, Chap. 13, Chap. 14 (excluding 14.2, 14.6, 14.7),Chap. 17 (excluding 17.6), Chap.18 (excluding 18.3, 18.4), Chap. 19 (excluding 19.5), Chap. 20 (excluding 20.6, 20.7), Chap. 22 (excluding 22.2.2), Chap. 23.
A collection of exercises by the teacher is available to students on Moodle.
Reference Bibliography
G.Cicchitelli, P.D'Urso, M.Minozzo. Statistica: principi e metodi. Pearson, quarta edizione.Type of delivery of the course
6 lecture hours plus 2 pratical hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam. 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.