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Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems. Such problems included the fabrication of semiconductors and the understanding of communications networks, which concerned Bell Labs.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.
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.
Causal research, is the investigation of (research into) cause-relationships. [ 1 ] [ 2 ] [ 3 ] To determine causality, variation in the variable presumed to influence the difference in another variable(s) must be detected, and then the variations from the other variable(s) must be calculated (s).
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.
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]
The Blinder-Oaxaca decomposition (/ ˈ b l aɪ n d ər w ɑː ˈ h ɑː k ɑː /) or Kitagawa decomposition, is a statistical method that explains the difference in the means of a dependent variable between two groups by decomposing the gap into within-group and between-group differences in the effect of the explanatory variable. The method was ...
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).