Unit MATHEMATICS II AND STATISTICS
- Course
- Engineering management
- Study-unit Code
- A002895
- Curriculum
- In all curricula
- CFU
- 12
- Course Regulation
- Coorte 2024
- Offered
- 2024/25
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa integrata
MATHEMATICS II
| Code | A002901 |
|---|---|
| CFU | 6 |
| Teacher | Luca Zampogni |
| Teachers |
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| Hours |
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| Learning activities | Base |
| Area | Matematica, informatica e statistica |
| Academic discipline | MAT/05 |
| Type of study-unit | Obbligatorio (Required) |
STATISTICS
| Code | A002903 |
|---|---|
| CFU | 6 |
| Teacher | Barbara Guardabascio |
| Teachers |
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| Hours |
|
| Learning activities | Base |
| Area | Matematica, informatica e statistica |
| Academic discipline | SECS-S/02 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | ENGLISH |
| Contents | In a world data driven it is more and more important let students acquire an operative ability in terms of data retrieval and analysis of data (strongly appreciated by the labour market) which allows them to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media The course of Statistics provides several insights to various facets of statistics and concepts required to solve various data science problems. It is an in-depth presentation of the main topics in statistical science with which any data scientist should be familiar including descriptive statistics, probability theory, statistical estimation and inference methods, linear and logistic regression models. Emphasis will be placed on intuitive and rigorous understanding of the fundamentals of statistics, accompanied by implementation of statistical methods to solve real world business problems. The practical part is focused on the computation and interpretation of empirical results, and it will be realized using the R software. |
| Reference texts | Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to the students by the course instructors during the course. Suggested books are: Alan Agresti and Maria Kateri (2021), “Foundations of Statistics for Data Scientists”. CRC Press Taylor & Francis Group. Alan Agresti and Maria Kateri (2021), “R-Web-Appendix of Foundations of Statistics for Data Scientists”. Freely available at http://stat4ds.rwth-aachen.de/pdf/DS_R_webAppendix.pdf |
| Educational objectives | By the end of the course students should: Know the fundamental theoretical notions of statistical analysis and illustrate the basic techniques for organizing, summarizing and graphically representing a dataset. They will also be able to formulate and conduct simple inferential procedures and regression analyses on small datasets. Produce and interpret basic statistical analyses. The objectives of the course, thus, include the ability of recognizing the different types of data and selecting among several statistical tools the most appropriate for the problem at hand. Interpret, evaluate and compare linear regression models, summarize and communicate the results with suitable graphical representations. |
| Prerequisites | The statistical methods introduced in the course are strongly dependent on basic mathematical tools, therefore it is expected that students had successfully attended and completed the planned mathematics courses. |
| Teaching methods | The course is carried out through lectures and practical exercises using data. Techniques will be introduced by examples and described in mathematical formulas. Focus will be on the practical implementation of each technique, R coding and interpretation of results. |
| Other information | Attending classes is strongly advised. |
| Learning verification modality | Assessment will be through a written examination, containing both theoretical questions and numerical data analysis exercises. The examination is designed to test students’ analytical skills, interpretation and understanding of relevant issues presented during the course. |
| Extended program | Descriptive statistics Introduction to statistical data science. Type of data and variables. Summarizing univariate data: location and variability indices, graphical representations. Descriptive statistics for multivariate data: bivariate quantitative data, categorical data and contingency tables, graphical representations. Probability Definitions of probability, sample space and events, probability axioms. Marginal and conditional probability, independent events, theorem of total probability, Bayes theorem and its applications. Random variables, discrete and continuous probability distributions. Main probability distributions: Bernoulli, Binomial, Normal, Chi-Square, t-distribution, F-distribution. Sampling distributions and the central limit theorem. Inference Point estimates and confidence intervals. Significance testing. Linear regression Introduction to statistical models. The simple linear regression model: ordinary least squares estimation of the regression coefficients and error variance. Inference on the regression coefficients of normal linear model. Model assessment. Predicting future observations. The multiple linear regression model. Logistic regression The logistic regression model for binary data. Maximum likelihood estimation and parameters interpretation: effects on probability and odds. Forecasting. |
| Obiettivi Agenda 2030 per lo sviluppo sostenibile |