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
安装中文字典英文字典查询工具!
中文字典英文字典工具:
复制到剪贴板
英文字典中文字典相关资料:
regression - What does it mean to regress a variable against another . . . Those words connote causality, but regression can work the other way round too (use Y to predict X) The independent dependent variable language merely specifies how one thing depends on the other Generally speaking it makes more sense to use correlation rather than regression if there is no causal relationship
regression - When is R squared negative? - Cross Validated Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values Hence, it must be non-negative For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative
regression - Converting standardized betas back to original variables . . . I have a problem where I need to standardize the variables run the (ridge regression) to calculate the ridge estimates of the betas I then need to convert these back to the original variables scale
regression - When should I use lasso vs ridge? - Cross Validated Ridge regression is useful as a general shrinking of all coefficients together It is shrinking to reduce the variance and over fitting It relates to the prior believe that coefficient values shouldn't be too large (and these can become large in fitting when there is collinearity) Lasso is useful as a shrinking of a selection of the coefficients
How should outliers be dealt with in linear regression analysis . . . What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
regression - Trying to understand the fitted vs residual plot? - Cross . . . A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line This suggests that the assumption that the relationship is linear is reasonable The res
correlation - What is the difference between linear regression on y . . . The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x) This suggests that doing a linear regression of y given x or x given y should be the
regression - Linear vs Nonlinear Machine Learning Algorithms - Cross . . . Three linear machine learning algorithms: Linear Regression, Logistic Regression and Linear Discriminant Analysis Five nonlinear algorithms: Classification and Regression Trees, Naive Bayes, K-Nea
regression - Interpreting the residuals vs. fitted values plot for . . . Consider the following figure from Faraway's Linear Models with R (2005, p 59) The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a