Chapter 6 Key Terms

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Key terms for Chapter 6 for Data Analytics
Matt Evans
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Benford’s law The principle that in any large, randomly produced set of natural numbers, there is an expected distribution of the first, or leading, digit with 1 being the most common, 2 the next most, and down successively to the number 9.
computer-assisted audit techniques (CAATs) Automated scripts that can be used to validate data, test controls, and enable substantive testing of transaction details or account balances and generate supporting evidence for the audit.
descriptive analytics Procedures that summarize existing data to determine what has happened in the past. Some examples include summary statistics (e.g., Count, Min, Max, Average, Median), distributions, and proportions.
diagnostic analytics Procedures that explore the current data to determine why something has happened the way it has, typically comparing the data to a benchmark. As an example, these allow users to drill down in the data and see how they compare to a budget, a competitor, or trend.
fuzzy matching Process that finds matches that may be less than 100 percent matching by finding correspondences between portions of the text or other entries.
predictive analytics Procedures used to generate a model that can be used to determine what is likely to happen in the future. Examples include regression analysis, forecasting, classification, and other predictive modeling.
prescriptive analytics Procedures that work to identify the best possible options given constraints or changing conditions. These typically include developing more advanced machine learning and artificial intelligence models to recommend a course of action, or optimizing, based on constraints and/or changing conditions.
process mining Analysis technique of business processes used to diagnose problems and suggest improvements where greater efficiency may be applied.
t-test A statistical test used to determine if there is a significant difference between the means of two groups, or two datasets.
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