Main aims of business statistics. Statistical data sources for firms. data quality. Probabilistic and non probabilistic sampling methods. Statistical quality control: on-line and off-line methods: control chart e ANOVA method. Time series analysis: classical methods. Regression analysis. Efficiency and productivity. Frontier production function. Total factor productivity.
Course Content - Last names M-Z
Terminology and purposes of company statistics. Statistical sources for the company. Data quality. Probabilistic and non-probabilistic sampling. The representation over time of economic phenomena. Index numbers. Deflazionamento. Statistical quality control: on-line and off-line methods. Statistics for the study of the relationships between economic variables (linear regression) Statistics for the study of productivity and efficiency. The analysis of the historical series and forecasts
Biggeri L., Bini M., Coli A., Grassini L., Maltagliati M. (2016) Statistica per le decisioni aziendali. Ed. Pearson, Milano.
Learning Objectives - Last names A-L
Knowledge. Methods for data collection and analysis for the main topics relaed to business. Data sources for firms. Technical language and methods in the follow in the application fields: statistical quality control, efficiency and productivity analysis, time series analysis (classical methods), forecasting.
Learning Objectives - Last names M-Z
KNOWLEDGE: Provide knowledge for data collection and analysis in typical business problems. Provide knowledge on the main available data concerning the company Provide notions of technical terminology appropriate to the applied application context such as: quality control, analysis of efficiency and productivity, representation of phenomena over time and forecasting, analysis of the relationship between phenomena of corporate interest.
SKILLS: skills to analyze data using computers and interpret the results produced by a statistical software or package
Skills acquired at the end of the course:
Ability to perform analysis in the context of online and offline quality control. Knowing how to conduct efficiency and productivity analysis using models and index numbers. Knowing how to perform a proper time series analysis with classic models. Knowing how to consult the main statistical sources and interpret the metadata.
Prerequisites - Last names A-L
Required exams: STATISTICA
Prerequisites - Last names M-Z
PROPEDEUTICAL TEACHING: STATISTICS
Teaching Methods - Last names A-L
48 hours
Type of Assessment - Last names A-L
Written exam (+ additional oral if required or requested by the student)
Type of Assessment - Last names M-Z
Written test
Course program - Last names M-Z
Technical terminology: primary, secondary data, data by analogy, metadata, microdata, elementary data, experimental and observational data, administrative and statistical data, etc. Types of studies: exploratory, descriptive, explanatory. Statistical sources: SISTAN, ISTAT and "unofficial" sources. Quality of statistical data.
Probabilistic sampling: simple random sample, stratified, clustered, multi-stage. Non-probabilistic sampling.
Statistical quality control. Process capacity and process capacity indices. Online methods: control chart for variables for monitoring averages and variability. Concept of rational subgroup. Sensitivity of a counter chart to identify shift in parameters. Off-line methods: experimental data and technical language of experiments. One-way and two-way analysis of variance.
Representation of economic quantities over time. Elementary and synthetic index numbers. Numbers of prices and quantities. Deflation of economic aggregates.
Productivity measures. Partial and global productivity. Numbers indexes of quantities and total factor productivity.
Efficiency measures. Efficiency on the input side and on the output side. Frontier production function concept. Parametric and non-parametric approaches to efficiency estimation. Relationship between efficiency and productivity.
Analysis of the relationship between economic variables by simple and multiple linear regression.
Analysis of the time series using the classic decomposition method. Simple and thoughtful mobile averages. Additive and multiplicative model. Seasonality and seasonal coefficients. Trend estimation by an analytical function.
Exercises with Excel.