Analysis of the time series using the classic approach: decomposition and deseasonalization, model evaluation indexes, exponential damping, correlogram. Analysis of index numbers: simple and complex index numbers. Analysis of productivity and efficiency: partial and total factor productivity, parametric and non-parametric efficiency. Regression: introduction to the analysis of simple linear regression using an application approach
Handouts, slides and practical exercises are available on Moodle. The reference text (chapters 3, 5, 6 and 7) is: Statistica per le decisioni aziendali - Luigi Biggeri - Matilde Bini - Alessandra Coli - Laura Grassini - Mauro Maltagliati -Pearson
Statistica-Levine David M.; Krehbiel Timothy C.; Berenson Mark L.-Pearson
handouts on spreadsheet provided during class
The course aims to provide students with not only basic knowledge but also application tools for time series analysis (calssical approach), productivity and efficiency analysis, index numbers and linear regression.
The objectives of knowledge and understanding and the ability to apply knowledge and understanding are as follows.
Knowledge and understanding: To know the theoretical foundations of the statistical models for the classical analysis of the time series, the calculation of the number of synthetic indexes and the analysis of efficiency and productivity.
Ability to apply the acquired knowledge: it requires the ability to apply the theoretical tools acquired, through the use of the spreadsheet and to interpret the results
the knowledge of the main descriptive statistics tools (indexes of central tendency, of variability and of association between variables) and of inferential statistics -topics covered in the course B018993-STATISTICA- is fundamental.
Lectures, mainly carried out in the computer classroom, with a computer available to each student
during the course will be given handouts to students and classes will be scheduled in a computer room
Type of Assessment
The test consists in the application of statistical tools to the computer during the course using as a base one or more data sets. For example, a historical series will be provided on which to estimate seasonality and trend coefficients, and / or a dataset of production units on which to calculate efficiency output, input efficiency and productivity indices.
Basic knowledge for the use of the spreadsheet: relative and absolute references, the "solver" tool, organization of data by sorting tools, filter and Pivot Table, basic Excel statistical functions.
Time series analysis using the classical approach: decomposition and seasonal adjustment, the assessment indices of models, exponential smoothing, correlogram
Analysis of Index numbers: simple and complex index numbers
Productivity and efficiency analysis: partial and total factor productivity, efficiency and paramentrica not paramentrica
Regression: Introduction to the analysis of linear regression (simple and multiple) through an application approach