Techniques for Data Analysis
A.Y. 2019/2020
Learning objectives
The mean aim of this course is to provide suitable tools to quantitatively describe one or more phenomena belonging to different environments (i.e. economic, social, political, administrative, historical, juridical, etc.).
This topic can be addressed by the organization of the raw data in frequency tables, the graphical representation and through the use of suitable indexes. Furthermore, when the observed data are the result of (partial) sample surveys, it is necessary to take advantage of the inference. In these cases, through the partial knowledge of the phenomenon, we can extend the results to the whole population, in term of probability.
This, during the class will be presented the elementary tools of calcolus of probability and the estimation theory.
The basic matematical elements are exential to understand and to apply these statistical tools.
This topic can be addressed by the organization of the raw data in frequency tables, the graphical representation and through the use of suitable indexes. Furthermore, when the observed data are the result of (partial) sample surveys, it is necessary to take advantage of the inference. In these cases, through the partial knowledge of the phenomenon, we can extend the results to the whole population, in term of probability.
This, during the class will be presented the elementary tools of calcolus of probability and the estimation theory.
The basic matematical elements are exential to understand and to apply these statistical tools.
Expected learning outcomes
At the end of this course, the students will know and will be able to understand the main mathematical and statistics techniques presented during the classes. They will know to carry out a descriptive analysis of a dataset, by highlighting the main features of the variables of interest. Furthermore, the students will be able to draw significant and reasonable conclusions in concordance with the context whom the aim of the analysis belongs (i.e. economic, social, political, administrative, historical and juridical).
Through several examples based on real data, we will show how to face analysis, how to interpret the results and how to draw conclusions coherent with the context in which the variables belong. We will judge the ability to understand and exposition of the students through an analysis similar to the ones presented during the classes. The mathematics and statistics techniques will give the fundamentals for further development and deeper analysis that the students can be faced with during their educational path.
Through several examples based on real data, we will show how to face analysis, how to interpret the results and how to draw conclusions coherent with the context in which the variables belong. We will judge the ability to understand and exposition of the students through an analysis similar to the ones presented during the classes. The mathematics and statistics techniques will give the fundamentals for further development and deeper analysis that the students can be faced with during their educational path.
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 Statistic
Introduction.
Organization of raw data in frequency tables and graphical representation.
Classification of statistical data.
Statistical series.
Measures of position: Mean, Median and Mode.
Absolute and relative indexes of variabilities.
The Gini heterogeneity index.
L'indice di eterogeneità di Gini.
Bivariate analysis.
Study of contingency tables.
Independence statistic and connection.
The linear correlation and the Pearson correlation coefficient.
The linear regression.
Part II:
Probability and Inference:
Random experiment, events, and definition of probability. Some elementary concepts about the calculus of probability.
Sampling schemes.
Continuous and dicrete random variables.
The Bernoulli, Binomial and Normal random variables.
The expected values and variance of random variables.
The Bayes theorem and Bayesian statistic.
Valore atteso e varianza di variabili casuali. Il teorema di Bayes e la statistica bayesiana.
The central limit theorem.
The sample mean random variable.
The sample proportion random variable.
The point estimation.
The interval estimation of mean and proportion.
Descriptive Statistic
Introduction.
Organization of raw data in frequency tables and graphical representation.
Classification of statistical data.
Statistical series.
Measures of position: Mean, Median and Mode.
Absolute and relative indexes of variabilities.
The Gini heterogeneity index.
L'indice di eterogeneità di Gini.
Bivariate analysis.
Study of contingency tables.
Independence statistic and connection.
The linear correlation and the Pearson correlation coefficient.
The linear regression.
Part II:
Probability and Inference:
Random experiment, events, and definition of probability. Some elementary concepts about the calculus of probability.
Sampling schemes.
Continuous and dicrete random variables.
The Bernoulli, Binomial and Normal random variables.
The expected values and variance of random variables.
The Bayes theorem and Bayesian statistic.
Valore atteso e varianza di variabili casuali. Il teorema di Bayes e la statistica bayesiana.
The central limit theorem.
The sample mean random variable.
The sample proportion random variable.
The point estimation.
The interval estimation of mean and proportion.
Website
NON-ATTENDING STUDENTS
Course syllabus
Part I:
Descriptive Statistic
Introduction.
Organization of raw data in frequency tables and graphical representation.
Classification of statistical data.
Statistical series.
Measures of position: Mean, Median and Mode.
Absolute and relative indexes of variabilities.
The Gini heterogeneity index.
L'indice di eterogeneità di Gini.
Bivariate analysis.
Study of contingency tables.
Independence statistic and connection.
The linear correlation and the Pearson correlation coefficient.
The linear regression.
Part II:
Probability and Inference:
Random experiment, events, and definition of probability. Some elementary concepts about the calculus of probability.
Sampling schemes.
Continuous and dicrete random variables.
The Bernoulli, Binomial and Normal random variables.
The expected values and variance of random variables.
The Bayes theorem and Bayesian statistic.
Valore atteso e varianza di variabili casuali. Il teorema di Bayes e la statistica bayesiana.
The central limit theorem.
The sample mean random variable.
The sample proportion random variable.
The point estimation.
The interval estimation of mean and proportion.
Descriptive Statistic
Introduction.
Organization of raw data in frequency tables and graphical representation.
Classification of statistical data.
Statistical series.
Measures of position: Mean, Median and Mode.
Absolute and relative indexes of variabilities.
The Gini heterogeneity index.
L'indice di eterogeneità di Gini.
Bivariate analysis.
Study of contingency tables.
Independence statistic and connection.
The linear correlation and the Pearson correlation coefficient.
The linear regression.
Part II:
Probability and Inference:
Random experiment, events, and definition of probability. Some elementary concepts about the calculus of probability.
Sampling schemes.
Continuous and dicrete random variables.
The Bernoulli, Binomial and Normal random variables.
The expected values and variance of random variables.
The Bayes theorem and Bayesian statistic.
Valore atteso e varianza di variabili casuali. Il teorema di Bayes e la statistica bayesiana.
The central limit theorem.
The sample mean random variable.
The sample proportion random variable.
The point estimation.
The interval estimation of mean and proportion.
SECS-S/01 - STATISTICS - University credits: 6
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES - University credits: 3
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES - University credits: 3
Lessons: 60 hours
Professors:
Liuzzi Danilo, Nicolussi Federica
Shifts:
-
Professors:
Liuzzi Danilo, Nicolussi FedericaProfessor(s)
Reception:
Wednesday 10:30-13:30 am
Room 32, Via Conservatorio 7, DEMM, 3rd floor