AIMS AND SCOPE
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.
LEARNING OBJECTIVES
Lectures and practical activities and data analysis in R. Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Basics of linear algebra and statistics.
Mandatory.
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.
R programming. Basic commands. Data types. Vectors. Matrices. Lists. Dataframes. Loading and saving data. Charts. Conditional statements and loops. Writing R functions.
Slides nad notes shared by the teacher through Studium.
Argomenti | Riferimenti testi | |
1 | Introduction to R, Basic Commands in R, Indexing Data, Matrices and Lists, Loading Data; | Sections 2.3 and 2.4 of [1] |
2 | Charts and Data Visualization | Lecture notes |
3 | Mean, Median, Variance, standard deviation, quantiles, percentiles, interquartile distance, boxplot, outlier detection | Lecture notes |
4 | Bivariate analysis, statistical inference, contingency table, joint probability, marginal probability, chi-squared test, t-test, linear regression. | Section 3.6 of [1], lecture notes |
Practical activity and data analyis with R. Learning assessment may also be carried out on line, should the conditions require it.