The course provides students with the basic knowledge to read economic statistics and interpret the characteristics of economic systems and their trends. At the end of the course the student will be able to use the techniques of collection, organization and analysis of statistical data learned in the course, as well as the basic concepts of probability calculation and statistical inference for the analysis of data deriving from sample surveys.
Curriculum
teacher profile teaching materials
Introductory concepts: Statistical variables and measurement scales. Simple distributions. Tabular and graphical representations. Empirical distribution function.
Measures of central tendency: Mode. Median. Quantiles. Arithmetic mean.
Measures of variability: Mean deviations. Variance. Coefficient of variation. Interquartile range.
Baiscs of concentration.
Skewness of a distribution.
Basic concepts on time series and index numbers.
Bivariate, marginal, and conditional distributions. Moments of bivariate distributions, association and independence.
Probability Theory:
Axiomatic definition of probability. Conditional probability. Independence. Bayes’ theorem. One-dimensional discrete random variables. Probability mass function, density function, cumulative distribution function. Moments of random variables. Main discrete probability distributions: binomial, Poisson, uniform.
Main continuous probability distributions: uniform, normal, exponential. Overview of Student’s and Fisher’s distributions.
Multivariate random variables: marginal and conditional probability functions, independence and correlation.
Properties of probability distributions: linear combinations of random variables, convergence, law of large numbers, and central limit theorem.
Statistical Inference:
Overview of population and sample: finite and infinite populations; random sampling from finite and infinite populations; probability distribution of the random sample.
Sample statistics and their distributions: sampling distribution of the mean; sampling distribution of the variance.
Parameter estimation: properties of estimators; confidence intervals for key population parameters.
Hypothesis testing: basic concepts of test theory; Type I and Type II errors; hypothesis testing for key population parameters.
Introduction to regression: Simple linear regression; estimation and hypothesis testing for the parameters of the regression line.
-Borra, S., & Di Ciaccio, A. (2021). Statistica. Metodologie per le scienze economiche e sociali. McGraw-Hill Education. (only in Italian)
-Newbold, P., Carlson, W. L., & Thorne, B. (2023). Statistics for Business and Economics, Pearson Education.
Programme
Descriptive Statistics:Introductory concepts: Statistical variables and measurement scales. Simple distributions. Tabular and graphical representations. Empirical distribution function.
Measures of central tendency: Mode. Median. Quantiles. Arithmetic mean.
Measures of variability: Mean deviations. Variance. Coefficient of variation. Interquartile range.
Baiscs of concentration.
Skewness of a distribution.
Basic concepts on time series and index numbers.
Bivariate, marginal, and conditional distributions. Moments of bivariate distributions, association and independence.
Probability Theory:
Axiomatic definition of probability. Conditional probability. Independence. Bayes’ theorem. One-dimensional discrete random variables. Probability mass function, density function, cumulative distribution function. Moments of random variables. Main discrete probability distributions: binomial, Poisson, uniform.
Main continuous probability distributions: uniform, normal, exponential. Overview of Student’s and Fisher’s distributions.
Multivariate random variables: marginal and conditional probability functions, independence and correlation.
Properties of probability distributions: linear combinations of random variables, convergence, law of large numbers, and central limit theorem.
Statistical Inference:
Overview of population and sample: finite and infinite populations; random sampling from finite and infinite populations; probability distribution of the random sample.
Sample statistics and their distributions: sampling distribution of the mean; sampling distribution of the variance.
Parameter estimation: properties of estimators; confidence intervals for key population parameters.
Hypothesis testing: basic concepts of test theory; Type I and Type II errors; hypothesis testing for key population parameters.
Introduction to regression: Simple linear regression; estimation and hypothesis testing for the parameters of the regression line.
Core Documentation
Recommended texts:-Borra, S., & Di Ciaccio, A. (2021). Statistica. Metodologie per le scienze economiche e sociali. McGraw-Hill Education. (only in Italian)
-Newbold, P., Carlson, W. L., & Thorne, B. (2023). Statistics for Business and Economics, Pearson Education.
Type of evaluation
The exam is written and consists of: exercises and/or multiple-choice questions and/or theoretical questions. The professor, if deemed necessary, may require an additional oral examination. Candidates who receive a failing grade in a written exam are not allowed to take the written exam in the following exam date of the same session. It is not permitted to bring any formula sheets and/or books into the examination room. teacher profile teaching materials
Introductory concepts: Statistical variables and measurement scales. Simple distributions. Tabular and graphical representations. Empirical distribution function.
Measures of central tendency: Mode. Median. Quantiles. Arithmetic mean.
Measures of variability: Mean deviations. Variance. Coefficient of variation. Interquartile range.
Baiscs of concentration.
Skewness of a distribution.
Basic concepts on time series and index numbers.
Bivariate, marginal, and conditional distributions. Moments of bivariate distributions, association and independence.
Probability Theory:
Axiomatic definition of probability. Conditional probability. Independence. Bayes’ theorem. One-dimensional discrete random variables. Probability mass function, density function, cumulative distribution function. Moments of random variables. Main discrete probability distributions: binomial, Poisson, uniform.
Main continuous probability distributions: uniform, normal, exponential. Overview of Student’s and Fisher’s distributions.
Multivariate random variables: marginal and conditional probability functions, independence and correlation.
Properties of probability distributions: linear combinations of random variables, convergence, law of large numbers, and central limit theorem.
Statistical Inference:
Overview of population and sample: finite and infinite populations; random sampling from finite and infinite populations; probability distribution of the random sample.
Sample statistics and their distributions: sampling distribution of the mean; sampling distribution of the variance.
Parameter estimation: properties of estimators; confidence intervals for key population parameters.
Hypothesis testing: basic concepts of test theory; Type I and Type II errors; hypothesis testing for key population parameters.
Introduction to regression: Simple linear regression; estimation and hypothesis testing for the parameters of the regression line.
-Borra, S., & Di Ciaccio, A. (2021). Statistica. Metodologie per le scienze economiche e sociali. McGraw-Hill Education. (only in Italian)
-Newbold, P., Carlson, W. L., & Thorne, B. (2023). Statistics for Business and Economics, Pearson Education.
Programme
Descriptive Statistics:Introductory concepts: Statistical variables and measurement scales. Simple distributions. Tabular and graphical representations. Empirical distribution function.
Measures of central tendency: Mode. Median. Quantiles. Arithmetic mean.
Measures of variability: Mean deviations. Variance. Coefficient of variation. Interquartile range.
Baiscs of concentration.
Skewness of a distribution.
Basic concepts on time series and index numbers.
Bivariate, marginal, and conditional distributions. Moments of bivariate distributions, association and independence.
Probability Theory:
Axiomatic definition of probability. Conditional probability. Independence. Bayes’ theorem. One-dimensional discrete random variables. Probability mass function, density function, cumulative distribution function. Moments of random variables. Main discrete probability distributions: binomial, Poisson, uniform.
Main continuous probability distributions: uniform, normal, exponential. Overview of Student’s and Fisher’s distributions.
Multivariate random variables: marginal and conditional probability functions, independence and correlation.
Properties of probability distributions: linear combinations of random variables, convergence, law of large numbers, and central limit theorem.
Statistical Inference:
Overview of population and sample: finite and infinite populations; random sampling from finite and infinite populations; probability distribution of the random sample.
Sample statistics and their distributions: sampling distribution of the mean; sampling distribution of the variance.
Parameter estimation: properties of estimators; confidence intervals for key population parameters.
Hypothesis testing: basic concepts of test theory; Type I and Type II errors; hypothesis testing for key population parameters.
Introduction to regression: Simple linear regression; estimation and hypothesis testing for the parameters of the regression line.
Core Documentation
Recommended texts:-Borra, S., & Di Ciaccio, A. (2021). Statistica. Metodologie per le scienze economiche e sociali. McGraw-Hill Education. (only in Italian)
-Newbold, P., Carlson, W. L., & Thorne, B. (2023). Statistics for Business and Economics, Pearson Education.
Type of evaluation
The exam is written and consists of: exercises and/or multiple-choice questions and/or theoretical questions. The professor, if deemed necessary, may require an additional oral examination. Candidates who receive a failing grade in a written exam are not allowed to take the written exam in the following exam date of the same session. It is not permitted to bring any formula sheets and/or books into the examination room.