linear regression

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linear regression

a statistical procedure in which a straight line is established through a data set that best represents a relationship between two subsets or two methods.

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.


in a line.

linear assessment
a method of expressing an assessment result as a score out of a possible perfect score of 10, or some other number. Used in body condition scoring, showring judging of conformation.
linear dodecyl benzene sulfonic acid teat dip
linear energy transfer
expresses the quality of electronic radiation. It is concerned with the spatial distributions of energy transfers which occur in the tracks of particles as they penetrate matter.
linear program
a management program used to determine the best mix of ingredients or services to be used in a particular situation to maintain the highest level of productivity or profitability or other similar parameter.
linear regression
statistical method used to study the relationship between independent and dependent variables when the dependent variable consists of continuous data.
linear score
for somatic cell counts in milk (SCCs) convert SCC logarithmically from cells per milliliter to a linear score from 0-9. The linear score has a straight line, inverse relationship with milk yield. An increase of one in the linear score is associated with a 400-pound decrease in lactation milk yield or a 1.5 pound drop in daily yield.


1. return to a former or earlier state.
2. subsidence of clinical signs or of a disease process.
3. in biology, the tendency in successive generations toward the mean.
4. the relationship between pairs of random variables; the mean of one variable and its location is influenced by another variable.

regression analysis
see regression analysis.
regression coefficient
is the factor which determines the slope of a regression line; the greater the coefficient the steeper the line.
curvilinear regression
when the relationship between two variables is not linear.
linear regression
the relationship between two variables is a straight line.
References in periodicals archive ?
The truncated regression model is appropriate when the dependent variable is continuous and the population is truncated (Breen, 1996; Greene, 2000; Long, 1997).
Several neural networks provided a level of accuracy equal to the logistic regression models for the high risk driver subgroup or the low risk driver subgroup, but no single neural network provided an equal level of accuracy for both subgroups.
i] = X values (moose seen per minute) used in the regression model, and
Adjustments to appraisals that are generated from regression models can then be made based on these economic conditions.
In the first part of table 1, results of regression model fitting have been presented.
01709 Table 3 Excel Regression Model Output of Share Return on Market Return in Presence of Outlier SUMMARY OUTPUT Regr Statistics Multiple R 0.
The variables identified in the literature review were analyzed separately in relation to performance using the t-test, ANOVA, and/or simple regression, and significant variables were included in the multiple regression model.
He also contends that the direct logit regression model produces a biased estimate of the discrimination coefficient.
Results from the Chow-test provided strong justification for differential effect on Statistics course performance due to different mathematical background and the null hypothesis of equality of two different regression models could be rejected.
Note that the common ordinary least squares regression model, which is often the starting point for coursework in multivariate methods, can only account for observed variables that are entered into the model.
Parameters were estimated for total sperm cells by age of boar classification under a random regression model using DxMRR (Meyer, 1998).
If there is correlation between the EPS and the return performance for the stocks, there must be a regression model that can represent the relationship, because EPS determines the return performance; therefore, it is predicted that randomly selected stocks with a daily dramatic increase greater than 10% will have a regression model of: