APPLIED STATISTICS
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
- Contacts/Info
None.
The exam is composed by two main parts: 1) theory; 2) exercises + R software questions. The program covers the topics from above, meaning the material available on the weblearning page of the course. The students might also refer to suggested textbooks.
The exam is written. It might be oral at the discretion of the lecturer.
The course aims to provide the students with the knowledge of the fundamentals of statistics and statistical learning for data analysis, starting from the vocabulary of statistics to model implementation and interpretation. The course has a twofold nature, and is structured in: a) theoretical and b) practical lectures (statistics with the R software).
The introduction of theoretical concepts is always combined with the application of statistical methods to analyze real-world data and solve practical problems in a variety of domains including management, marketing, economics and finance. Therefore, at the end of the course the students will be able to:
- understand the main domains of applications of statistics, with particular reference to the areas of management, economics, marketing and finance
- understand the main concepts of theoretical and applied statistics
- understand the most widely known applied statistics models and machine learning algorithm
- analyze data and implement statistical models with R
- model real-world data, including corporate and market data
- formulate and build predictive models, forecast key variables and assess forecast uncertainty
- soundly interpret model outputs and derive implications for the specific domain of knowledge
CONTENUTI
Lectures contents:
Introduction to Statistics, its basic vocabulary and importance.
Elements of descriptive statistics (univariate).
Elements of descriptive statistics (bivariate).
Simple linear regression model.
Multiple linear regression model.
Logistic regression model.
Linear and logistic regression model selection.
Introduction to supervised and unsupervised learning / Networks - decision trees and random forests, K-means and hierarchical clustering, introduction to network science
Final recap of the illustrated statistical methods and their joint discussion.
Practical Lectures contents:
Introduction to the R software.
Fundamentals of programming with R.
Descriptive statistics with R.
Simple linear regression model with R.
Multiple linear regression model with R.
Logistic regression model with R.
Linear and logistic regression model selection with R.
Basics of decision trees and random forests, K-means and hierarchical clustering / Network models in R.
Final practice with R.
Lectures contents:
Introduction to Statistics, its basic vocabulary and importance.
Elements of descriptive statistics (univariate).
Elements of descriptive statistics (bivariate).
Simple linear regression model.
Multiple linear regression model.
Logistic regression model.
Linear and logistic regression model selection.
Introduction to supervised and unsupervised learning / Networks - decision trees and random forests, K-means and hierarchical clustering, introduction to network science
Final recap of the illustrated statistical methods and their joint discussion.
Practical Lectures contents:
Introduction to the R software.
Fundamentals of programming with R.
Descriptive statistics with R.
Simple linear regression model with R.
Multiple linear regression model with R.
Logistic regression model with R.
Linear and logistic regression model selection with R.
Basics of decision trees and random forests, K-means and hierarchical clustering / Network models in R.
Final practice with R.
The course is structured in theoretical and practical lectures with the R software. Both theoretical and practical lectures are based on the instructor’s material.
Students may also wish to refer to the following books for:
a) a basic statistics review - Newbold, P. (2013). Statistics for business and economics. Pearson;
b) a reference to the topics covered (and very interesting additional material) - James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer
None.