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 ?
Thus the regression model of GRDP at CMV, GRDP at CP, and population has significant effect to per capita income.
Table 1 shows the estimated coefficients, standard error for coefficients, Wald Statistic, degrees of freedom and level of significance for Wald Statistic and partial correlation of the logistic regression model.
This study aimed to compare the functions of Wilmink and Ali and Schaeffer with Legendre polynomials in random regression models with different residual variance structures, in the estimation of genetic parameters for test-day milk production of Holstein Friesians reared in Rio Grande do Sul.
The posterior summaries of the parameters of the bivariate zero-inflated Poisson regression model, including mean, median, standard deviation, and 95% credible intervals, based on 30,000 simulated values in presence and absence of independent variables can be seen in Tables 1 and 2, respectively.
There are two basic transition functions in this case; the Logistic Smooth Transition Regression model and the Exponential Smooth Transition Regression model.
Most major software such as R and SAS has the capability of fitting the semi-parametric linear regression model.
Given this challenging backdrop, we posit four reasons consumer researchers tend to disregard complex sample design when using data collected via complex samples: (1) researchers do not know that it may be necessary to incorporate sample design into their analyses, (2) researchers know that it may be necessary but do not know how to incorporate it, (3) researchers believe that it may not be necessary because unweighted regression models render accounting for sampling design unnecessary, and (4) researchers know that it may be necessary but their particular analysis precludes it due to data or software limitations.
Y = the dependent, A to L = the independent variables, p = the number of unknown parameters in regression model, [[beta].
On the other hand cubic spline regression model fit rather than from the position of theknots the number ofknotseffect.