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When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g.
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response.
In the first example provided above, the sex of the patient would be a nuisance variable. For example, consider if the drug was a diet pill and the researchers wanted to test the effect of the diet pills on weight loss. The explanatory variable is the diet pill and the response variable is the amount of weight loss.
Ordination methods can broadly be categorized in eigenvector-, algorithm-, or model-based methods. Many classical ordination techniques, including principal components analysis, correspondence analysis (CA) and its derivatives (detrended correspondence analysis, canonical correspondence analysis, and redundancy analysis, belong to the first group).
It is explanatory knowledge that provides scientific understanding of the world. (Salmon, 2006, pg. 3) [1] According to the National Research Council (United States): "Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work." [2]
Provided the availability of valid instruments, RE and FE methods extend to the case where some of the explanatory variables are allowed to be endogenous. As in the exogenous setting, RE model with Instrumental Variables (REIV) requires more stringent assumptions than FE model with Instrumental Variables (FEIV) but it tends to be more efficient ...
Use of the phrase "working hypothesis" goes back to at least the 1850s. [7]Charles Sanders Peirce came to hold that an explanatory hypothesis is not only justifiable as a tentative conclusion by its plausibility (by which he meant its naturalness and economy of explanation), [8] but also justifiable as a starting point by the broader promise that the hypothesis holds for research.
Explanatory research usually focuses on "why" or "what caused" a phenomenon. Formal hypotheses posit possible explanations (answers to the why question) that are tested by collecting data and assessing the evidence (usually quantitative using statistical tests).