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While the concepts of Bayesian statistics are thought to date back to 1763, marketers' exposure to the concepts are relatively recent, dating from 1959. [2] Subsequently, many books [5] [6] [7] and articles [8] [9] have been written about the application of Bayesian statistics to marketing decision-making and market research.
A typical "Business Statistics" course is intended for business majors, and covers [71] descriptive statistics (collection, description, analysis, and summary of data), probability (typically the binomial and normal distributions), test of hypotheses and confidence intervals, linear regression, and correlation; (follow-on) courses may include ...
Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods .
Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of business intelligence, which is a set of technologies and processes that uses data to understand and analyze business performance to drive decision-making .
Operations research (or operational research) is an interdisciplinary branch of applied mathematics and formal science that uses methods such as mathematical modeling, statistics, and algorithms to arrive at optimal or near optimal solutions to complex problems; Management science focuses on problems in the business world.
In business analysis, the Decision Model and Notation (DMN) is a standard published by the Object Management Group. [1] It is a standard approach for describing and modeling repeatable decisions within organizations to ensure that decision models are interchangeable across organizations.
Business decision mapping (BDM) is a technique for making decisions, particularly for the kind of decisions that often need to be made in business.It involves using diagrams to help articulate and work through the decision problem, from initial recognition of the need through to communication of the decision and the thinking behind it.
Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains. A related field, decision engineering, also investigates the improvement of decision-making processes but is not always as closely tied to data science.