linear regression


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Related to linear 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 ?
These uncorrelated factor scores welded from explanatory factor analysis were used as new predictors for predicting live weight of the sheep in the multiple linear regressions (Eyduran et al., 2009).
Multivariate linear regression of the BSIN IgG2 measurements with the 4 LC-MS/MS subclasses (Table 2) showed that the LC-MS/MS IgG4 concentration coefficient (0.94) was equal to or greater than the corresponding LC-MS/MS IgG2 coefficient (0.89).
Image Correct Wrong Accuracy Inside Normal situation (15) 14 1 93.33% Minor disaster (15) 13 2 86.67% Medium disaster (15) 12 3 80% Serious disaster (15) 12 3 80% Outside Random situation (10) 9 1 90% Table 5: Comparison of accuracy of multiple linear regression and minimum distance method.
This article presents a new method to estimate physico-chemical characteristics of suspended particulate matter by determining the corresponding parameters of fine inert matter by simple linear regression models.
The multiple linear regression was successfully used in species like wheat (Leilah & Khateeb, 2005), rice (Dalchiavon et al., 2012; Godoy et al., 2015), maize (Balbinot Junior et al., 2005) and soybean (Mercante et al., 2010) for the simulation of grain yield and identification of potential variables of alteration by the management on the species.
After assessing two independent variables [rho] and v as one variable [rho]v, multiple linear regression analysis expression applied for the solution of the task becomes a linear regression analysis equation of one variable
The values predicted by the fuzzy linear regression model based on adaptive-clustering and error-correction are consistent with the actual values of chlorophyll a concentration, and the model has good prediction precision.
Then, the proposed identification scheme for the multiplemode linear regression model consists of the following steps.
Edition, "Applied linear regression models," Technometrics, no.
Table 3 shows the correlation of the S-PEF with age, height, and weight in simple and multiple linear regressions among the 2 sexes.
This violates the fundamental assumption used in linear regression model.
Linear regression is the least squares estimator of a linear regression model with a single explanatory variable.