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Predictive modeling is a statistical technique used to predict future behavior. It utilizes predictive models to analyze a relationship between a specific unit in a given sample and one or more features of the unit. The objective of these models is to assess the possibility that a unit in another sample will display the same pattern.
Explanatory power is the ability of a hypothesis or theory to explain the subject matter effectively to which it pertains. Its opposite is explanatory impotence . In the past, various criteria or measures for explanatory power have been proposed.
Scatterplot of tips vs. bill. Points below the line correspond to tips that are lower than expected (for that bill amount), and points above the line are higher than expected. We might expect to see a tight, positive linear association, but instead see variation that increases with tip amount. In particular, there are more points far away from ...
The New York Times of November 10, 1919, reported on Einstein's confirmed prediction of gravitation on space, called the gravitational lens effect.. The concept of predictive power, the power of a scientific theory to generate testable predictions, differs from explanatory power and descriptive power (where phenomena that are already known are retrospectively explained or described by a given ...
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 .
Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research.
The goal of logistic regression is to use the dataset to create a predictive model of the outcome variable. As in linear regression, the outcome variables Y i are assumed to depend on the explanatory variables x 1,i... x m,i. Explanatory variables. The explanatory variables may be of any type: real-valued, binary, categorical, etc.
Over time, as successive modifications build on top of each other, theories consistently improve and greater predictive accuracy is achieved. Since each new version of a theory (or a completely new theory) must have more predictive and explanatory power than the last, scientific knowledge consistently becomes more accurate over time.