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- In statistics and in empirical sciences, a data generating process is a process in the real world that "generates" the data one is interested in. Usually, scholars do not know the real data generating model.
en.wikipedia.org/wiki/Data_generating_process
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Jan 4, 2020 · The DGP is the processes that cause data to occur as they do. They are a Platonic ideal that we do not and cannot know. Only in simulations can we define a mathematical DGP, in the real world they are hidden from us. The aim of mathematical modelling is abstraction of the data.
Dec 7, 2023 · By using causal diagrams to illustrate different data generation processes, even when working with the same data, we can arrive at vastly different conclusions. Let's examine an example of a drug designed to reduce the risk of heart attack.
A 'Data Generation Process' refers to the procedure of creating data based on a specific model or algorithm, which allows for the simulation and investigation of various scenarios to understand the underlying patterns and probabilities associated with the data.
Overall, we will consider two general type of data generation processes: (1) probability based, and (2) non-probability based. In the former, data is generated by a probabilistic process, i.e., each element of the population is selected with a (known) probability to form part of the sample data.
Data generation refers to the process of creating a large amount of data using computer software and algorithms. It involves generating data points for specific input variables within defined ranges, allowing for the analysis and study of various operating conditions.
Data generation plays a pivotal role in facilitating informed decision-making processes. By systematically and purposefully producing data, organizations can uncover patterns, trends, and correlations that provide meaningful insights into various aspects of their operations.
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