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. Regression analysis. Efficiency and productivity. Frontier production function. Total factor productivity. Time series analysis: classical methods.
Biggeri L., Bini M., Coli A., Grassini L., Maltagliati M. (2017; II edizione) 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.
Prerequisites - Last names A-L
Required exams: STATISTICA
Teaching Methods - Last names A-L
Class lectures. 48 hours.
Type of Assessment - Last names A-L
Written exam (1 hour) + additional oral if requested by the student (max 3 points).
Details on Moodle platform.
Course program - Last names A-L
Objectives of business statistics. 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, cluster, multi-stage. Non-probabilistic sampling.
Representation of economic variables over time. Elementary and synthetic index numbers. Numbers of prices and quantities. Deflation of economic aggregates.
Statistical quality control. Process capacity and process capacity indices. Online methods: control chart for variables for monitoring averages and variability. Trial control charts. Concept of rational subgroup. Sensitivity of a control chart to identify shift in parameters. Off-line methods: experimental data and technical language of experiments. Analysis of variance.
Productivity measures. Partial and global productivity. Numbers indexes of quantities and total factor productivity.
Efficiency measures. Input and output efficiency. Frontier production function. 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 time series using the classic decomposition method. Simple and centered mobile average. Additive and multiplicative decomposition model. Seasonality and seasonal coefficients. Trend estimation by an analytical function. Goodness of fitting and forecasting accuracy.
Performance indicators (financial ratios) from balance sheet and income statement data.