Statistical analysis is the collection and interpretation of data in order to uncover patterns and trends. It is a component of data analytics. Statistical analysis can be used in situations like gathering research interpretations, statistical modeling or designing surveys and studies. It can also be useful for business intelligence organizations that have to work with large data volumes.
In the context of business intelligence (BI), statistical analysis involves collecting and scrutinizing every data sample in a set of items from which samples can be drawn. A sample, in statistics, is a representative selection drawn from a total population.
The goal of statistical analysis is to identify trends. A retail business, for example, might use statistical analysis to find patterns in unstructured and semi-structured customer data that can be used to create a more positive customer experience and increase sales.
Steps of statistical analysis
Statistical analysis can be broken down into five discrete steps, as follows:
- Describe the nature of the data to be analyzed.
- Explore the relation of the data to the underlying population.
- Create a model to summarize an understanding of how the data relates to the underlying population.
- Prove (or disprove) the validity of the model.
- Employ predictive analytics to run scenarios that will help guide future actions.
Statistical analysis software
Software for statistical analysis will typically allow users to do more complex analyses by including additional tools for organization and interpretation of data sets, as well as for the presentation of that data. IBM SPSS Statistics, RMP and Stata are some examples of statistical analysis software. For example, IBM SPSS Statistics covers much of the analytical process. From data preparation and data management to analysis and reporting. The software includes a customizable interface, and even though it may be hard form someone to use, it is relatively easy for those experienced in how it works.
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