# linear regression

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## 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
McGraw-Hill Concise Dictionary of Modern Medicine. © 2002 by The McGraw-Hill Companies, Inc.

## 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.
Collins Dictionary of Medicine © Robert M. Youngson 2004, 2005
References in periodicals archive ?
Figure 17(c) depicts the best fit lines for ideal and actual transfer characteristics of this ADC to evaluate the offset and gain errors.
After creating a chart in Microsoft Excel, a best fit line can be found as follows:
The rate of crater formation versus irradiance level slopes listed in Table 1 for all of the polymers given a C rating with regard to UV resistance (Kauffman 2011) had best fit lines with [R.sup.2] > 0.9 and y-intercept = 0.001 - 0.002.
Since the X and Y axes were plotted symmetrically, an obtuse best fit line indicates a relationship where the absolute values do not match.
After sufficient failure data have been collected at each temperature, the median value at each temperature is calculated and used in a least squares regression analysis to determine the slope of the best fit line representing the data points.

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