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[h1]Statistics in business analytics[/h1]
The main objective of Business Statistics is to make┬áinferences┬á(e.g., analysis, prediction, decision making) about certain characteristics of a┬ápopulation┬ábased on information contained in a random sample from the entire population. The condition for┬árandomness is essential to make sure the sample is representative of the population.
Business Statistics is the science of┬áÔÇÿgood’ decision making in the face of uncertainty┬áand is used in many disciplines, such as financial analysis, econometrics, auditing, production and operations, and marketing research. It provides knowledge and skills to interpret and use statistical techniques in a variety of business applications. A typical Business Statistics analysis covers statistical study, descriptive statistics (collection, description, analysis, and summary of data), probability, hypotheses testing and data modeling using the best fit technique.
Statistics is a science of making decisions with respect to the characteristics of a group of persons or objects on the basis of numerical information obtained from a randomly selected sample of the group. Statisticians refer to this numerical observation as┬árealization┬áof a random sample. However, notice that┬áone cannot see a random sample.┬áA random sample is only a sample of a finite┬áoutcomes of a random process.
HybridStat can apply the aforementioned statistical tools in order to help you make the most appropriate decision and derive the optimal plan for your business. We can guide you through the whole process and provide continuous consulting based on your dynamic needs. Specifically, HybridStat can contribute in the following domains:
[h3]Business questionnaire design[/h3]
- Discussion of the nature of the questions to be addressed (e.g. yes/no, scaled or free text expected answers)
- Discussion about the design of the questions so that proper statistical methods can be used
- Optimal design of the questionnaire so that the downstream analysis can yield useful outcomes
[h3]Business questionnaire analysis[/h3]
- Data description and reporting (basic statistics)
- Data mining (clustering, classification, sophisticated feature selection and dimensionality reduction)
- Principal Component Analysis
- Factorial analysis
- Statistical data modeling and prediction
[h3]Statistics services in finance[/h3]
- Financial time-series analysis
- Modern risk analysis
- Gaussian likelihood modeling
- GARCH models
- Financial and stock market time-series analysis
- The application of robust statistical models used with success in other domains, to financial data
- Statistical learning methods with accurate predictions