- Docente: Cinzia Franceschini
- Credits: 6
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Sciences and Management of Nature (cod. 9257)
Learning outcomes
At the end of the course, the student learn major statistical methods to deal with ecological, economical and social data, both using univariate and multivariate approaches. The student will have the capacity to deal with the practical applications of several statisical methods to real world case and data.
Course contents
Learning outcomes
At the end of the course, the student will have a good knowledge of basic and advanced multivariate statistical techniques useful to conduct statistical analysis on real dataset. The course blends theory and practice for a better understanding of the subject.
Univariate Statistics:
Descriptive statistics and introduction to statistical distributions
Location measures : mean, median, mode
Graphical representation: frequency distribution, bar graphs, histograms
Measures of scatter: range, inter-quartile range, variance
Bivariate Statistics:
Two-way tables: joint, marginal and conditional distributions
Association: independence, chi-square test
Concordance: covariance, correlation
Multivariate Statistics:
Data matrix
Mean vector
Covariance matrix
Distance Matrix
A brief introduction to the following multivariate statistical techniques: Principal Components, Cluster Analysis, Correspondence Analysis, Multidimensional Scaling, Discriminant Analysis
R software: after a brief introduction to the R software, we'll use it in applications of each topic of the course.
Readings/Bibliography
Izenman, A. J. (2008). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, Springer.
Thomas H. Wonnacott, Ronald J. Wonnacott , Introductory Statistics, 5th Edition, Wiley.
Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2002.
John Verzani. Using R for Intoductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005.
Slides of the course.
Teaching methods
Frontal Lectures and laboratory lectures
Assessment methods
Written exam with TRUE/FALSE questions
Teaching tools
Blackboard, slides, computer lab.
Office hours
See the website of Cinzia Franceschini