00914 - Statistics

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Reduced inequalities

Academic Year 2021/2022

Learning outcomes

This course provide a practical introduction to techniques for data management. The main learning goal is to familiarize students with tools for choosing between synthetic measures, graphics and measures of relationships between two variables.Moreover, the student will be able to apply reduction techniques and to deal with classification procedures.

Course contents

- Introduction. Statistical surveys. Descriptive statistics vs inferential statistics. Types of statistical variables. Levels of measurements. Data matrices. The summation operator.
- Frequency distributions and cumulative frequency distributions. The empirical distribution function.
- Measures of central tendency and their properties. Measures of variability and their properties. Measures of distribution shape. Boxplots.
- Bivariate frequency distributions. Marginal and conditional distributions. Conditional mean and conditional variance. Variance decomposition. Statistical independence.
- Scatterplots. Covariance and correlation. Linear regression.
- Matrices for multivariate statistical analysis.
- Clustering techniques: agglomerative hierarchical and partitioning methods.
- Principal components analysis.

Readings/Bibliography

D. Freedman, R. Pisani, R. Purves, Statistics, W. W. Norton, 1997.  
J.I. Galbraith, I. Moustaki, D. J. Bartholomew, F. Steele, The analysis and interpretation of multivariate data for social scientists, 2002,  Chapman & Hall  
Some additional readings will be found at https://iol.unibo.it/.

Teaching methods

Each topic covered in the lectures will be followed by exercises in practical classes.

Assessment methods

Written test and oral examination

Office hours

See the website of Carlo Trivisano