linear regression

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Related to multiple regression: Multiple linear regression

linear regression

A statistical method defined by the formula y = mx = b which is used to "best-fit" straight lines to scattered data points of paired values Xi, Yi, where the values of Y—the ordinate or vertical line—are “observations” or values of a variable (e.g., systolic blood pressure) and the values of X—the abscissa or horizontal line—increased in a relatively nonrandom fashion (e.g., age). Linear regression is a parametric procedure for determining the relationship between one or more (multiple) continuous or categorical predictor (or independent) variables and a continuous outcome (or dependent) variable.

In the equation y = mx = b:
m = slope
b = y - intercept

linear regression

Statistics A statistical method defined by the formula y = a + bx, which is used to 'fit' straight lines to scattered data points of paired values Xi, Yi, where the values of Y–the ordinate or vertical line are observations of a variable–eg, systolic BP and the values of X–the abscissa or horizontal line ↑ in a relatively nonrandom fashion–eg, age

linear regression

A statistical method of predicting the value of one variable, given the other, in a situation in which a CORRELATION is known to be significant. The equation is y = a + bx in which x and y are, respectively, the independent and dependent variables and a and b are constants. This is an equation for a straight line.
References in periodicals archive ?
Collinearity, power, and interpretation of multiple regression analysis.
Multiple regression analysis is complex to develop but, once developed, is very easy to use.
The eligibility of the multiple regression analysis was detected by the coefficient of determination (R2) and Root of Mean Square Error (RMSE) (Tariq et al., 2012b).
Multiple Regression assumes that variables are normally distributed, homoscadistic, and measures are reliable.
Use of factor scores in multiple regression analysis for estimation of body weight by several body measurements in brown trouts (Salmo trutta fario).
The path coefficient from an independent variable (X,) derived from the stepwise multiple regression analysis to a dependent variable (Y) was calculated using the following equation (Huo et al.
In applying multiple regression based on a given data set, one can determine whether the impact is negative, positive, and/or statistically significant.
We carried out further multiple regression analysis to investigate which combination would show the best correlation with the VFA calculated by CT (Tables 3(a) and 3(b)).
Therefore, in multiple regression analysis using explanatory variables as Rotation X and EOG elements for gaze estimation, it means estimating gaze considering sharing ratio.
Figure 4 presents rows 1-20 of the multiple regression results for monthly supplies expense (Y) with sales (in units) per month ([X.sub.1]), sales dollars per month ([x.sub.2]), and December ([X.sub.3]) as explanatory variables.
A simple answer to this question is the different assumptions between the univariate and multiple regression models.
Results: The results showed that the Conditional Autoregressive model yield more reliable results as compared to the multiple regression model having lower value of Akaike Information Criterion i.e.

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