37321 - Statistics for Data Analysis

Learning outcomes

Attending students will learn how to apply the main statistical methods that can be used in data analysis, particularly when dealing with the main features of management and business control. At the end of the course students will be able to: - represent a set of data and describe them with specific indices; - represent and evaluate the relationship between two different variables, with a particular focus on linear regression and correlation; - perform some basic evaluating procedures of probability; - apply some parameter estimation methods; - check the validity of some specific statistical hypotheses; - know and apply the main sampling methods and the sample strategies associated to them.

Course contents

First Section - Esploratory Statistics

Classification of statistical variables. Mean values and measures of dispersion. Gini's concentration ratio. Indices of heterogeneity. Statistical ratios and Index numbers. Human development index (HDI). Bivariate statistical data. Two-way contingency table. Indices of association. Linear regression and correlation.

Second section - Elements of Probability

Events and logical  operations Combinatorics. Definitions and axioms of probability. Some basic results derived from the axioms. Conditional probability and independence. Discrete random variables: support, probability function, cumulative distribution function, median and centiles, expected value , variance and standard deviation. Discrete distribution models: Bernoulli, Binomial, Geometric, Hypergeometric, Poisson variables. Continuous random variables. Density  and graduation function. Continuous distribution models: uniform, exponential, Gaussian and derived distributions.

Third Section - Statistical Inference 

Population, sample and sample space, statistic and estimator. Bias and Mean square error. Point and interval estimation of a frequency, a mean and a standard deviation. Hypothesis testing. Significance level and power of a test. Tests for checking frequencies. Tests for checking  the Gaussian parameters. One-factor ANOVA. Tests for checking independence of two variables.

Fourth Section - Sampling Methods

Sampling plan and sampling strategy. Probability of extraction and inclusion. Simple Random Sampling, Probabilized Sampling, Stratified Sampling and Cluster Sampling.

 

 

Readings/Bibliography

Maurizio Brizzi (2014), Elementi di probabilità e di inferenza statistica, Webster / Libreriauniversitaria.it, Limena (PD).

Teaching methods

Front lessons with the possibility of laboratory sessions.

Assessment methods

Written test composed by three exercises, during approximately 100 minutes. Oral test (not optional) during 15-20 minutes.

Written test results will be published in Alma Esami. At the end of the oral test an overall mark will be assigned to the candidate.

Evalutation scale:

30 e lode (A+) = excellent

28 - 30 (A) = very good

26 - 27 (B) = good

24 - 25 (C) = discrete

21 - 23 (D) = sufficient

18 - 20 (E) = barely sufficient.

Students with specific learning disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University's dedicated support office in good time:

https://site.unibo.it/studenti-con-disabilita-e-dsa/en

The office will propose any appropriate accommodations for eligible students. These accommodations must be submitted to the course instructor for approval at least 15 days in advance. The instructor will assess their appropriateness, taking into account the intended learning outcomes of the course.

The use of AI tools is prohibited in all assessments. Any use of AI tools constitutes a breach of academic integrity.

Teaching tools

Teaching sheets available to the students on Virtuale.unibo platform.

Office hours

See the website of Maurizio Brizzi