Academic Year 2019/2020 - 1° Year
Teaching Staff: Alessandro Ortis
Credit Value: 3
Laboratories: 36 hours
Term / Semester:

Learning Objectives


The aim of the course is introduce the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.



  1. Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
  2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference.
  3. Making judgements (Autonomia di giudizio). On completion, students will able to extract knowledge from data through statistical analyses in R.
  4. Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R.
  5. Learning skills (Capacità di apprendimento). On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.

Course Structure

Lectures and practical activities and data analysis in R.

Required Prerequisites

Basics of linear algebra and statistics.

Attendance of Lessons


Detailed Course Content

Use of the statistical software in R regarding:

Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation.

Probability. Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian.

Statistical inference. Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions.

Statistical models. The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model.

Textbook Information

Documents available on the web page of The R Project for Statistical Computing: and other resources available on the web.

Course Planning

 SubjectsText References
1Introduction to R, Basic Commands in R, Indexing Data, Matrices and Lists, Loading DataLecture Notes 
2Graphs, Data Types and Structures, Conditional Statements and Loops, Graphs and Data VisualizationLecture Notes 
3Mean, Median, Variance, standard deviation, quantiles, percentiles, interquartile distance, boxplot, outlier detectionLecture Notes 
4Functions in R, data filteringLecture Notes 
5Bivariate analysis, statistical inference, contingency table, joint probability, marginal probability, chi-squared test, t-test, linear regression.Lecture Notes 

Learning Assessment

Learning Assessment Procedures

Practical activity and data analyis with R