(a) A "statistical" technique which enables predictions to be made from a defined set of data. (b) A technique employed in "hypnosis", in which the hypnotized subject is guided by the hypnotist to return to an earlier time in life, or perhaps even to experiences from a past-life incarnation. Sometimes, the regression occurs spontaneously, without suggestion or guidance by the hypnotist. See also "past-life regression".
(1) In a hypnotherapy context, "regression" refers to reversion to an earlier mental or behavioral level. Narrowly, it refers to age regression in which a subject relives or recalls an earlier experience, generally with all five senses functioning. Broadly, it refers to the accessing of prevailing cortical brainwave patterns that are slower than those of ordinary waking consciousness — the slower the brainwaves, the younger the corresponding age. See text, Chapter 10. See also, " Altered State of Consciousness," " consciousness," "hypnosis," "primary process cognition, " " State of Consciousness" and "whole brain cognition." (2) In a statistical context, "regression" refers to finding the equation that represents the linear relationship between two correlated variables. The equation, the general form of which is y = bx + a, can be used to predict the value of one variable that corresponds to a known value of the other variable. See text, Chapter 13. See also, "coefficient of correlation" and "correlated."
Fitting a model to data that relates a response or outcome variable to one or more explanatory variables.
in statistics, an analysis of the mathematical relationship(s) between a response variable and one or more predictor variables.
a statistical technique for using the values of one variable to predict the values of another, based on information about their relationship, often given in a scattergram. Multiple regression involves the prediction of an interval-level variable from the values of two or more other variables. Logistic regression does this too, but predicts the values of nominal or ordinal variables.
aka Regression Analysis - A defined process for quantifying and modeling the output of a process relative o its input variables. It estimates the relationship between inputs and outputs of a process and produces mathematical model of that relationship. Its use can lead to a better understanding of the critical factors controlling ht equality of the process output.
A statistical method which tries to predict a dependent variable (result) by combining a number of independent variables (measures). For example, regression analysis could predict your life expectancy by combining your grandparents' age at death, whether you smoke, whether you have high blood pressure, etc.
This is the method of working out the relationship between two variables.
a statistical procedure used to estimate the linear dependence of one or more independent variables on a dependent variable.
The measurement in changes of one variable that is the result of changes in other variables. Regression analysis attempts to identify the degree of correlation between dependent and independent variables and predict the latter value.
A statistical procedure that is used to describe linear relationships between dependent variables and independent variables.
going back in time during hypnotic trance to remember past experiences and replaying them in the imagination for therapeutic purposes. Sometimes regression is spontaneous.
A form of statistical modelling that attempts to evaluate the relationship between one variable (termed the dependent variable) and one or more other variables (termed the independent variables). It is a form of global analysis as it only produces a single equation for the relationship thus not allowing any variation across the study area. Geographically Weighted Regression is a local analysis form of regression.
the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)
a simple (for a computer) method of correlating data set A and data set B
a statistical technique for estimating the equation that best fits sets of observations
a data analysis technique that optimally fits a model based on the squared differences between data points and model fitted points. Typically, regression chooses model coefficients to minimize the sum of these squared differences
an examination of the relationship between a response (dependent) variable and the explanatory (independent) variable(s); a measure of the tendency for the expected value of one of these jointly correlated random variables to approach more closely the mean value of its own data set than that of any of the other variables.
Regression is a statistical technique used to help individuals predict x when they know y and the relationship between x and y. A linear equation can be compared to predict criterion scores with one or more predictor variables.
Statistical term to describe methods for estimating the relationship between a dependent (response) variable Y and one or more independent (explanatory) variables X.
Regression is a statistical operation, which graphically can be described as drawing a straight trend line (or "best-fit" line) as close to all the points in a graph as possible. The "best-fit" line, or regression, then is said to represent the points in the graph. Also called Tuning.
Statistical method for predicting unknown values of one correlation variable with another. Parametric and non-parametric equivalents are available.
a statistical analysis that assesses the association between two variables.
The measurement of the mean expectation of one stochastically random dependent variable against another; the tendency for the expected value of one of two jointly correlated random variables to approach more closely the mean value of its set than the other.
A statistical technique which finds correlations, or fits trend lines to data.
A statistical technique for estimating the best fitting line through a series of data points in a scatter plot.
a statistical technique used to estimate mathematical models of economic and other processes. It is used to find a mathematical expression which best fits the relationship between a group of random variables as indicated by a sample of data.
A method of generating a linear equation that best describes the relationship between variables. Simple regression is the study of two variables and multiple regression is the study of three or more variables.
A statistical technique used to establish the relationship of a dependent variable (e.g., excess return) and one or more independent variables (e.g., exposure to market, size, and value risks). Slope coefficients measure the sensitivity of the dependent variable to changes in the independent variables. By measuring exactly how large and significant each independent variable has historically been in its relation to the dependent variable, the future value of the dependent variable can be estimated. Essentially, regression analysis attempts to measure the degree of correlation between the dependent and independent variables, thereby establishing the latter's predictive values.
A technique used in hypnosis, involving suggesting to hypnotized persons that they are returning to an earlier time. Sometimes the regression occurs spontaneously, without suggestion.
A measure of the relationship between two variables. The value of one trait can be predicted by knowing the value of the other variable. For example, easily obtained carcass traits (hot carcass weight, fat thickness, and loin muscle area) are used to predict percent lean. Likewise, breeding value estimates based on limited data are regressed back toward the population average to account for the imperfection of this relationship.
A statistical measure of the effect of one interval or ratio level variable on another, used to indicate the statistical significance of the relationship and to generate an equation to predict or estimate the value of the dependent variable for a new case, based only on the known value of the dependent variable.
A model that aims to assess how much one variable affects another. This is related to correlation, but implies causality.
A data mining function for predicting continuous target values for new records using a model built from records with known target values. ODM supports the Support Vector Machine algorithm for regression. See approximation.
A mathematical relationship between two variables (eg, the height and weight of women in Australia). For simplicity, the relationship is often taken to be a linear one (ie, a straight line when plotted), but it can also be a curve. When the regression relationship for the variables is known, we can predict the approximate value of one variable from the value of the other.
Any of several statistical techniques concerned with estimating the mean value of one or more variables by knowing the value of at least one other variable.
(theory) The activation of the subconscious mind into the Akashic records through meditation or guided hypnosis, to recall parts of a past life. A very emotional experience of viewing a past incarnation(s) and understanding how those events are affecting the current physical embodiment now.
is a statistical model relating a dependent variable to one or more independent variables. [6
Mathematical term for calculating a line of best fit.
A statistical tool which utilizes the relation between two or more variables so that one variable can be predicted or estimated from the other(s).
Usually linear regression is used to explain and/or predict. The general form is Y = a + bX + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope and u is the regression residual. The a and b are chosen in a way to minimize the squared sum of the residuals.
The process of determining a prediction equation for predicting Y from X.
Relationship of the mean value of one variable and the corresponding value of an independent variable
Also known as linefitting. A method that finds the best 'line' through a set of plotted points, used to model an outcome variable in terms of a linear combination of predictor variables (also called independent variables). See also Multiple regression.
A mathematical approach used to establish causal relationships between variables.