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regression    音标拼音: [rəgr'ɛʃən]
n. 回归
n. 复原,逆行,退步

回归复原,逆行,退步

regression
回归

regression
n 1: an abnormal state in which development has stopped
prematurely [synonym: {arrested development}, {fixation},
{infantile fixation}, {regression}]
2: (psychiatry) a defense mechanism in which you flee from
reality by assuming a more infantile state
3: the relation between selected values of x and observed values
of y (from which the most probable value of y can be
predicted for any value of x) [synonym: {regression}, {simple
regression}, {regression toward the mean}, {statistical
regression}]
4: returning to a former state [synonym: {regression}, {regress},
{reversion}, {retrogression}, {retroversion}]

Regression \Re*gres"sion\ (r?*gr?sh"?n), n. [L. regressio: cf.
F. r['e]gression.]
The act of passing back or returning; retrogression;
retrogradation. --Sir T. Browne.
[1913 Webster]

{Edge of regression} (of a surface) (Geom.), the line along
which a surface turns back upon itself; -- called also a
{cuspidal edge}.

{Regression point} (Geom.), a cusp.
[1913 Webster]

135 Moby Thesaurus words for "regression":
Brownian movement, Freudian fixation, about-face, advance,
angular motion, arrested development, ascending, ascent, atavism,
axial motion, backflowing, backing, backset, backsliding,
backward deviation, backward motion, career, climbing, comedown,
course, current, debasement, decadence, decadency, declension,
declination, decline, deformation, degeneracy, degenerateness,
degeneration, degradation, demotion, depravation, depravedness,
depreciation, derogation, descending, descent, deterioration,
devolution, disenchantment, downtrend, downturn, downward mobility,
downward motion, downward trend, drift, driftage, drop, dying, ebb,
ebbing, effeteness, fading, failing, failure, failure of nerve,
fall, falling back, falling-off, father fixation, fixation, flight,
flip-flop, flow, flux, forward motion, infantile fixation,
involution, lapse, libido fixation, loss of tone, mother fixation,
mounting, oblique motion, ongoing, onrush, parent fixation,
passage, plunging, pregenital fixation, progress, radial motion,
random motion, recidivation, recidivism, reclamation, reconversion,
recrudescence, recurrence, reflowing, refluence, reflux, regress,
rehabilitation, reinstatement, relapse, renewal, restitution,
restoration, retreat to immaturity, retrocession, retrogradation,
retrogression, retroversion, return, returning, reversal, reverse,
reversion, reverting, revulsion, rising, run, rush, set, setback,
sideward motion, sinking, slippage, slipping back, slump, soaring,
sternway, stream, subsiding, throwback, traject, trajet, trend,
turn, turnabout, upward motion, wane


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  • Multivariable vs multivariate regression - Cross Validated
    Multivariable regression is any regression model where there is more than one explanatory variable For this reason it is often simply known as "multiple regression" In the simple case of just one explanatory variable, this is sometimes called univariable regression Unfortunately multivariable regression is often mistakenly called multivariate regression, or vice versa Multivariate
  • In linear regression, when is it appropriate to use the log of an . . .
    This is because any regression coefficients involving the original variable - whether it is the dependent or the independent variable - will have a percentage point change interpretation
  • Regression with multiple dependent variables? - Cross Validated
    Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't seem like it
  • What is the lasso in regression analysis? - Cross Validated
    LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously This method uses a penalty which affects they value of coefficients of regression
  • Explain the difference between multiple regression and multivariate . . .
    There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables
  • How does the correlation coefficient differ from regression slope?
    The regression slope measures the "steepness" of the linear relationship between two variables and can take any value from $-\infty$ to $+\infty$ Slopes near zero mean that the response (Y) variable changes slowly as the predictor (X) variable changes
  • How to derive the standard error of linear regression coefficient
    another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X) df from Wikipedia: " In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself "
  • Linear regression when independent variable are count data
    The Ys, on the other hand, are continuous and can assume any numerical value, either positive or negative Initially, my approach was to apply linear regression to model this relationship However, given the specific nature of the Xs as count variables, I've grown uncertain about the appropriateness of using linear regression





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