Learning objectives
The course is aimed at a significant deepening of some statistical models of particular relevance to management engineering: the multiple regression model, time series analysis, and forecasting models. Learning the MATLAB language is an integral part of the course.
Prerequisites
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Course unit content
The course includes theoretical lessons and exercises. The course topics will cover:
Deepening understanding of regression: from the regression line to the simple regression model; from the simple regression model to the multiple regression model.
Time series: from independent data to dependent data; preliminary transformations and models.
Forecasting: using estimated models to obtain projections and forecasts.
From theory to practice: using software (MATLAB) for statistical processing.
Full programme
- Regression Model
Introduction and Review of Basic Concepts
Relationship between variables
Regression line and parameter calculation: least squares method
Introduction of random elements: simple regression model
Parameter estimation
Inference on parameters
- Multiple Regression Model
Introduction of additional regressors in the model
Matrix notation of the multiple regression model
Parameter estimation: generalization of the least squares method
Corrected coefficient of determination and the issue of parsimony
- Time Series Analysis
Dependent data
Preliminary transformations: contemporaneous and intertemporal
Temporal variations and aggregations
Temporal dependence: autocovariance and autocorrelation
Identification of cycles: periodogram
Models for decomposing latent components
Local polynomial regression and moving averages
Stochastic models for time series: ARIMA processes
- Forecasting with Time Series Models
The forecasting problem
Prediction errors and loss functions
Evaluation of predictive performance
Forecasting using exponential smoothing models
Forecasting using ARIMA models
Bibliography
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Teaching methods
Lectures and laboratory with Matlab.
Assessment methods and criteria
Oral Exam.
In the exam, the student must demonstrate knowledge of the various theoretical models indicated in the program and developed during the course. Proficiency in the MATLAB language is an essential requirement. The interview can be conducted, at the student's choice, through the discussion of a Project Work developed by writing a MATLAB program.
Other information
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2030 agenda goals for sustainable development
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