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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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  • Why are regression problems called regression problems?
    Origin of 'regression' The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean)(Galton, reprinted 1989)
  • regression - What does it mean to regress a variable against another . . .
    As an example, the data is X = 1, ,100 The value of Y is plotted on the Y axis The red line is the linear regression surface Personally, I don't find the independent dependent variable language to be that helpful Those words connote causality, but regression can work the other way round too (use Y to predict X)
  • regression - What is the correct formula to compute R-squared? - Cross . . .
    I'm completely confused about how to calculate R-squared for given lists of predicted and actual values As an example, assume that my predicted values are: [3, 8, 10, 17, 24, 27] and my actual va
  • regression - Trying to understand the fitted vs residual plot? - Cross . . .
    In this example, variances for the first quarter of the data, up to about a fitted value of 40 are smaller than variances for fitted values larger than 40 The middle portion of the fitted values has substantially larger variances than the outer values This indicates that the regression model may have failed to account for heteroscedasticity
  • Assumptions of linear models and what to do if the residuals are not . . .
    For your first question, I don't think that a linear regression model assumes that your dependent and independent variables have to be normal However, there is an assumption about the normality of the residuals For your second question, there is two different things you could consider : Check different kind of models
  • What is the difference between fixed effect, random effect in mixed . . .
    To use it we perform both the fixed and random effects regression, and then apply the Hausman Test to see if their coefficient estimates diverge significantly If they diverge, endogeneity is at play and a fixed effects model is the best choice Otherwise, we’ll go with random effects
  • regression - Why do we say the outcome variable is regressed on the . . .
    The word "regressed" is used instead of "dependent" because we want to emphasise that we are using a regression technique to represent this dependency between x and y So, this sentence "y is regressed on x" is the short format of: Every predicted y shall "be dependent on" a value of x through a regression technique
  • regression - What are best practices in identifying interaction effects . . .
    $\begingroup$ @Brandon: This is standard model diagnostics and exploratory plotting skills I would plot the residuals against one of the covariates I think might be a candidate for an interation, conditioned (in the ggplot2 or lattice way) on the values of the covariate I think is involved in the interaction
  • regression - What is difference-in-differences? - Cross Validated
    The regression model will inherently converge on the correct lift attributable to treatment when conditioning on group assignment and post-start period statuses $\endgroup$ – jbuddy_13 Commented May 25, 2023 at 20:32
  • regression - Interpreting the residuals vs. fitted values plot for . . .
    One of the observable ways it might differ from being equal is if it changes with the mean (estimated by fitted); another way is if it changes with some independent variable (though for simple regression there's presumably only one independent variable available in most cases, so the two will be basically the same thing)





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