multivariate stepwise regression

USAGE SUMMARY

The phrase "multivariate stepwise regression" is correct and usable in written English.
It can be used in statistical contexts, particularly when discussing methods for selecting variables in regression analysis. Example: "In our study, we employed multivariate stepwise regression to identify the most significant predictors of patient outcomes."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

54 human-written examples

For the definition of the regional models a multivariate stepwise regression analysis was used, while a jack-knife procedure in the validation phase was performed.

Journal of Hydrology

A multivariate stepwise regression analysis (regression, SPSS) was performed to determine the correlation between the structural and/or numerical abnormalities found for both iFISH, SNP-array techniques and their relationship with the expression of those genes analyzed by RQ-PCR.

Plosone

Afterwards, multivariate stepwise regression analyses were carried out.

BMC Family Practice

Univariate and multivariate stepwise regression analyses were undertaken to assess predictors of no AKI after CPB.

Critical Care

Univariate and multivariate stepwise regression analyses were undertaken to assess predictors of AKI after CPB.

Critical Care

Significant interactions were further analyzed by including the interaction terms in multivariate stepwise regression analyses.

Arthritis Research and Therapy

Human-verified similar examples from authoritative sources

Similar Expressions

6 human-written examples

Variation in the nearshore data was analyzed using multivariate stepwise regressions with respect to landscape characteristics of the adjacent coastal watersheds (US only).

Journal of Great Lakes Research

Multivariate stepwise regressions on total as well as on respiratory complaints were carried out.

Environmental Health Perspectives

To determine factors associated with mother's satisfaction bivariate binary logistic regression and multivariate stepwise logistic regression were applied.

BMC Pregnancy and Childbirth

A multivariate stepwise logistic regression was then performed to evaluate each of these as independent predictors.

American Journal of Obstetrics and Gynecology

Results: A multivariate stepwise linear regression equation revealed that episiotomy adds nearly 3 cm to perineal lacerations.

American Journal of Obstetrics and Gynecology

Expert writing Tips

Best practice

When reporting results, clearly state the direction of selection (forward, backward, or stepwise) and the criteria used for variable entry and removal (e.g., p-value threshold).

Common error

Avoid blindly accepting the model produced by "multivariate stepwise regression". Always critically evaluate the selected variables for theoretical justification and potential for overfitting, especially with small datasets.

Linguistic Context

The phrase "multivariate stepwise regression" functions as a noun phrase that identifies a specific statistical technique. Ludwig AI confirms its usage in various scientific contexts, describing a method used for variable selection in regression models.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, "multivariate stepwise regression" is a well-established term in statistics, referring to a method for selecting variables in a regression model. Ludwig AI's analysis confirms that it's grammatically sound and very common, primarily used in scientific contexts. When employing this technique, remember to critically evaluate the selected variables to avoid overfitting. While the phrase is accurate, alternative phrasing like "stepwise multivariate regression" and "stepwise regression analysis" offer similar meanings with slightly different emphases.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

stepwise multivariate regression

Reorders the words while maintaining the core meaning; emphasizes the 'stepwise' aspect.

stepwise regression analysis

Shortens the phrase by omitting "multivariate", implying it when multiple variables are involved.

variable selection in multiple regression

Focuses on the function of stepwise regression: selecting variables in a multiple regression model.

multivariable stepwise selection

Replaces "regression" with "selection", highlighting the variable selection process.

forward selection regression

Highlights a specific type of stepwise regression (forward selection).

backward elimination regression

Highlights a specific type of stepwise regression (backward elimination).

automatic variable selection

Generalizes the method as an automated process of choosing variables.

model building with stepwise methods

Describes the broader context of model building using stepwise approaches.

stepwise model reduction

Emphasizes the process of reducing a model's complexity in a stepwise manner.

data-driven variable selection

Highlights that the variable selection is based on the data.

FAQs

What is the purpose of "multivariate stepwise regression"?

The purpose of "multivariate stepwise regression" is to identify a subset of predictor variables that have the strongest relationship with a dependent variable in a multiple regression model. It automates the variable selection process, reducing the complexity of the model.

What are the different types of "multivariate stepwise regression"?

The main types are forward selection (starting with no predictors and adding them one at a time), backward elimination (starting with all predictors and removing them one at a time), and stepwise selection (a combination of both forward and backward steps).

What are some limitations of using "multivariate stepwise regression"?

Limitations include potential for overfitting, instability of variable selection (small changes in the data can lead to different models), and underestimation of standard errors. Consider alternative methods like regularization or cross-validation to validate the results.

How does "multivariate stepwise regression" differ from standard multiple regression?

"Multivariate stepwise regression" automates the selection of predictor variables, whereas standard multiple regression requires the researcher to specify all predictor variables in advance. Stepwise methods are exploratory, while multiple regression is often used for confirmatory analysis.