B029774 - STATISTICS FOR BUSINESS

Italian

Italian

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.

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.

Biggeri L., Bini M., Coli A., Grassini L., Maltagliati M. (2017; II edizione) Statistica per le decisioni aziendali. Ed. Pearson, Milano.
Further material will be made available to students on the moodle platform

Knowledge. Methods for data collection and analysis for the main topics related 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.

KNOWLEDGE: Providing knowledge for data collection and analysis in typical business problems. Provide knowledge on the main data available that interest the company Provide notions of technical terminology appropriate to the application context dealt with such as: quality control, efficiency and productivity analysis, representation of phenomena over time and forecasting, analysis the relationship between phenomena of corporate interest.
SKILLS: skills to be able to analyze data by computer and interpret the results produced by a software or statistical package
Skills acquired at the end of the course:
Ability to perform analyzes in the context of online and offline quality control. Knowing how to conduct efficiency and productivity analyzes using models and index numbers. Knowing how to perform a correct analysis of time series with classic models. Knowing how to consult the main statistical sources and interpret metadata

Required exams: STATISTICA

Required exams: STATISTICA

Class lectures. 48 hours.

48 hours

Written exam. For details, see Moodle page

The exam consists of a written test with questions of two types:
- exercises or theoretical demonstrations;
- multiple-choice questions (4 options) on the topics of the program.

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.

Aims of business statistics. 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.
Comparison of economic quantities over time. Elementary and synthetic index numbers. Index numbers of prices and quantities. Deflation of economic aggregates.
Probabilistic sampling: simple, stratified, clustered, multi-stage random sample. Non-probabilistic sampling.
Statistical quality control. Process capability and process capability indices. Online methods: control chart for variables for monitoring averages and variability. Trial control chart. Sensitivity of a control chart to detect shifts in parameters. Off line methods: experimental data and technical language of the experiments. Analysis of variance.
Productivity measures. Partial and global productivity. Index numbers of quantities and total factor productivity.
Efficiency measures. Efficiency on the input side and on the output side. Border production function concept. Parametric approach to efficiency estimation. Relationship between efficiency and productivity.
Analysis of the relationship between economic variables by means of simple and multiple linear regression (outline).
Time series analysis using the classical decomposition method. Simple and weighted moving averages. Additive and multiplicative decomposition model. Seasonality and seasonal coefficients. Estimation of the trend by means of an analytic function. Goodness-of-fit indices and evaluation of forecast accuracy.

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