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WHAT DOES MULTICOLLINEARITY MEAN IN ENGLISH?
Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy. In this situation the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data themselves; it only affects calculations regarding individual predictors. That is, a multiple regression model with correlated predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with respect to others. A high degree of multicollinearity can also prevent computer software packages from performing the matrix inversion required for computing the regression coefficients, or it may make the results of that inversion inaccurate.
The translations of multicollinearity from English to other languages presented in this section have been obtained through automatic statistical translation; where the essential translation unit is the word «multicollinearity» in English.
List of principal searches undertaken by users to access our English online dictionary and most widely used expressions with the word «multicollinearity».
FREQUENCY OF USE OF THE TERM «MULTICOLLINEARITY» OVER TIME
The graph expresses the annual evolution of the frequency of use of the word «multicollinearity» during the past 500 years. Its implementation is based on analysing how often the term «multicollinearity» appears in digitalised printed sources in English between the year 1500 and the present day.
Examples of use in the English literature, quotes and news about multicollinearity
10 ENGLISH BOOKS RELATING TO «MULTICOLLINEARITY»
Discover the use of multicollinearity in the following bibliographical selection. Books relating to multicollinearity and brief extracts from same to provide context of its use in English literature.
Neural Network Modeling Using Sas Enterprise Miner
Multicollinearity exists in a predictive model when two or more independent or
input variables in the model are related to each other, which leads to the inability
in analyzing the importance of the input variables in the model based on their ...
Randall Matignon, 2005
Introductory Econometrics for Finance
However, a problem occurs when the explanatory variables are very highly
correlated with each other, and this problem is known as multicollinearity. It is
possible to distinguish between two classes of multicollinearity: perfect
Chris Brooks, 2008
A Guide to Econometrics
Multicollinearity is the name given to this phenomenon. Although technically the
fifth assumption of the CLR model is violated only in the case of exact
multicollinearity (an exact linear relationship among some of the regressors), the
Peter Kennedy, 2003
Data Analysis and Decision Making
If the package interrupts the analysis with an error message containing the words
“exact multicollinearity” or “linear dependence,” you should look for a redundant
explanatory variable. The message from StatTools in this case is shown in ...
S. Albright, Wayne Winston, Christopher Zappe, 2010
A Course in Econometrics
Multicollinearity is no more (or less) serious than micronumerosity. Exact
multicollinearity (R~, = 1) is a close analogue of exact micronumerosity (n = 0).
When a research article complains about multicollinearity, readers ought to see
Arthur Stanley Goldberger, 1991
Introductory Econometrics: Using Monte Carlo Simulation with ...
Multicollinearity Workbook: Multicollinearity.xls This section deals with an
important practical issue in multiple regression analysis: multicollinearity. We will
tackle this subject by examining made- up data from the heating oil example.
Humberto Barreto, Frank Howland, 2006
Practical Business Statistics
The consequences of multicollinearity can be statistical or numerical: 1. Statistical
consequences of multicollinearity include difficulties in testing individual
regression coefficients due to inflated standard errors. Thus, you may be unable
Andrew Siegel, 2011
SAS System for Regression: Third Edition
The existence of multicollinearity is not a violation of the assumptions underlying
the use of regression analysis. In other words, the existence of multicollinearity
does not affect the estimation of the dependent variable. That is, the resulting yˆ ...
Rudolf J. Freud, Ph.D., Ramon C. Littell, Ph.D., 2000
Success of University Spin-Offs: Network Activities and ...
Network Activities and Moderating Effects of Internal Communication and
Adhocracy Prof. Dr. Achim Walter, Manoj Gupte. Fourthly, the Conditional Index
indicates strong multicollinearity when values are above 30 (do., pp. 193 -194).
Table 11 ...
9.5 Regression analysis with multicollinearity In Section 7.9. we discussed
multicollinearity (i.e.. when the independent variables are highly intercorrelated)
and showed how it affected the estimate of the standard error of the regression ...
Abdelmonem Afifi, Susanne May, Virginia A. Clark, 2003
10 NEWS ITEMS WHICH INCLUDE THE TERM «MULTICOLLINEARITY»
Find out what the national and international press are talking about and how the term multicollinearity is used in the context of the following news items.
Neonatal care practice and factors affecting in Southwest Ethiopia: a …
Multicollinearity between the independent variables was assessed by using variance inflation factors (VIF >10 were considered as suggestive ... «BMC Blogs Network, Jul 15»
The Push-Pull Dynamic in Gold & Silver
This is important from a statistical perspective because it eliminates the possibility of multicollinearity, which can make regression results ... «Metal.com News, Jul 15»
The BDNF Val66Met polymorphism moderates the relationship …
Prior to analysis, the continuous independent predictor (CR) was mean-centered to reduce potential multicollinearity. Subsequent analyses ... «Nature.com, Jun 15»
Patient-Centered Teamwork in Care Transitions
Multivariate analyses were not undertaken due to the high degree of multicollinearity between the domains of the TPQ and CTS tools. «AJMC.com Managed Markets Network, Jun 15»
Using Hedonic Price Analysis In Food Labeling Class Actions
See Robert S. Pindyck and Daniel L. Rubenfeld, “The Effects of Multicollinearity” Econometric Models and Economic Forecasts, Irwin-McGraw ... «Law360, Jun 15»
Count the tricks and the treats: Tony Allen has his case for Defensive …
Gasol appears on most of those same lineups with Allen that are tops in defensive rating for the Grizzlies, making multicollinearity (Ed. Note: ... «Grizzly Bear Blues, Mar 15»
PT-PM, RPM and the Curious Case of Ersan Ilyasova and Khris …
It is impossible to tell precisely how much credit Middleton deserves, there are always issues of multicollinearity in adjusted plus-minus models ... «Mid Level Exceptional, Mar 15»
New research: How employee emotions' impact on customer …
The measurement of customer satisfaction has become stuck in evaluating statistical methods that deal with multicollinearity and using the ... «Bizcommunity.com, Mar 15»
Who Gets Into the NCAA Tournament?
... logistic regression, neural network, and support vector machine techniques, uses principal component factors to account for multicollinearity, ... «Mid Level Exceptional, Jan 15»
Why Shots Are Made (Or Missed) In The NBA
Several of the variables on the shot logs suffer from multicollinearity issues. In layman's terms, multicollinearity is when the variables used to ... «Mid Level Exceptional, Dec 14»
« EDUCALINGO. Multicollinearity [online]. Available <https://educalingo.com/en/dic-en/multicollinearity>. Aug 2020 ».