B4788 - ANALISI DATI E STATISTICA PER LA GEOLOGIA

Learning outcomes

The course provides an introduction to basic techniques for the statistical analysis of geological data and an introduction to the use of software tools for statistical analysis most widespread in the geological field. At the end of the course the student knows the main techniques of descriptive statistics for univariate distributions, knows the basic elements of statistical inference, understands the basic methodologies for multivariate statistical analysis and for the interpretation of the results of the corresponding models. Moreover, the student is able to apply the learned theoretical techniques to geological data by using software dedicated to the statistical analysis of experimental data and the student knows how to comment and critically evaluate the results of this analysis.

Course contents

Course contents (divided by module):

Module 1

Module 1

• Introduction to statistics and data organization.

• Frequency distributions and graphical representation of data: bar charts, histograms, box plots, scatter plots, frequency polygons, and pie charts.

• Descriptive statistics: measures of central tendency (mean, median, mode, quartiles, and percentiles), measures of variability (variance, standard deviation, range, and interquartile range), and measures of shape (skewness and kurtosis).

• Fundamentals of probability: events, probability, conditional probability, and independence.

• Discrete and continuous random variables; main probability distributions (Binomial, Poisson, and Normal distributions).

• Statistical inference: sampling distributions, the Central Limit Theorem, point estimation, and confidence intervals.

• Hypothesis testing: tests on the mean of a single population, comparison of two populations, and the chi-square goodness-of-fit test.

• Bivariate data analysis: correlation, simple linear regression, coefficient of determination, residual analysis, and prediction intervals.

• One-way Analysis of Variance (ANOVA).

• Principal Component Analysis (PCA) for dimensionality reduction and the exploratory analysis of multivariate datasets.

• Introduction to statistical data analysis using the R software environment, with applications to geological datasets.

Module 2

• Introduction to R programming and the RStudio integrated development environment
• Core programming principles (types of variables, for and while loops, if and else)
• Fundamentals of R (vectors, functions, packages in R)
• Matrices and matrix operations
• Data frames in R
• Plotting data in R and advanced visualization with GGPlot2
• Exploratory data analysis in R

Readings/Bibliography

• Vermeesch, P. (2021). Statistics for Geoscientists: Lecture Notes [Materiale didattico]. University College London. https://www.scribd.com/document/637121387/Untitled
• S.M. ROSS, Probabilità e statistica per l'Ingegneria e le Scienze, Ed. APOGEO
• R. Kabacoff. R in Action: Data Analysis and Graphics with R. 3rd edition (May 3, 2022)

Teaching methods

Classroom lectures for the theory, and computer Laboratory for codes implementation and analysis of proposed examples.

In consideration of the type of activity and teaching methods adopted, the frequency of this training activity requires the prior participation of all students in Modules 1 and 2 of safety training in the places of study, in e-learning mode.

Assessment methods

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Assessment methods

Student assessment consists of two components: an individual project and a written examination.

The project must be submitted at least seven days before the date of the written examination. Submission of the project by this deadline is a mandatory requirement for admission to the written examination.

Project (maximum 10 points)

The project consists of the statistical analysis of a dataset selected by the student from a set of datasets provided by the instructor during the course. The project must be carried out using the R software and must include:

• the R code developed for the analysis;

• the application of the statistical methods covered during the course;

• the presentation and interpretation of the results obtained;

• a critical discussion of the findings emerging from the analysis.

The project will be assessed on the basis of the methodological correctness of the analysis, the quality of the statistical analysis, the correctness of the R code, the clarity of the presentation, and the student's ability to critically interpret the results.

Written examination (maximum 22 points)

The written examination consists of:

• open-ended questions on the theoretical topics covered during the course;

• open-ended questions concerning the statistical analysis of the dataset developed in the project;

• multiple-choice questions on theoretical topics and programming in R.

The written examination is designed to assess the student's understanding of the theoretical concepts, the ability to correctly apply the statistical methods covered during the course, and the knowledge of the data analysis tools presented during the course.

Final grade 

The final mark out of 30 is determined on the basis of the total score obtained. Honours (30 cum laude) are awarded to students who achieve a total score greater than 30 points.

Teaching tools

Slides and material for laboratory exercises provided by the teachers. The teaching material will be available on the University of Bologna e-learning platform (https://virtuale.unibo.it).

Office hours

See the website of Germana Landi

See the website of Giancarlo Tamburello