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Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."
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.
Outline of statistics; List of probability topics; Glossary of probability and statistics; Glossary of experimental design; Notation in probability and statistics; List of probability distributions; List of graphical methods; List of fields of application of statistics; List of stochastic processes topics; Lists of statistics topics; List of ...
The explanatory variable is the diet pill and the response variable is the amount of weight loss. Although the sex of the patient is not the main focus of the experiment—the effect of the drug is—it is possible that the sex of the individual will affect the amount of weight lost.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
In clinical research, the effect a drug may have on a patient may be modeled with ordinal regression. Independent variables may include the use or non-use of the drug, as well as control variables such as demographics and details from medical history. The dependent variable could be ranked on the following list: complete cure, improved symptoms ...
The simplest direct probabilistic model is the logit model, which models the log-odds as a linear function of the explanatory variable or variables. The logit model is "simplest" in the sense of generalized linear models (GLIM): the log-odds are the natural parameter for the exponential family of the Bernoulli distribution, and thus it is the simplest to use for computations.
As statistics and data sets have become more complex, [a] [b] questions have arisen regarding the validity of models and the inferences drawn from them. There is a wide range of conflicting opinions on modelling. Models can be based on scientific theory or ad hoc data analysis, each employing different methods. Advocates exist for each approach ...