multivariate logistic regression

USAGE SUMMARY

"multivariate logistic regression" is a correct and usable term in written English.
You can use this term when discussing methods of statistical analysis used to predict the likelihood of a binary outcome based on several independent variables. For example, "When examining the relationship between multiple independent variables and the likelihood of a binary health outcome, a multivariate logistic regression is the most appropriate method to use."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

hierarchical multivariate logistic regression.

Injury Epidemiology

Multivariate logistic regression analysis.

Intensive Care Medicine Experimental

Table 1 Multivariate logistic regression analysis.

Intensive Care Medicine Experimental

Table 1 Multivariate logistic regression for hsTnT.

Intensive Care Medicine Experimental

Two multivariate logistic regression models were constructed.

Clinical Gastroenterology and Hepatology

Multivariate logistic regression was used to adjust for confounding variables.

Annals of Intensive Care

Independent predictors were identified with multivariate logistic regression.

Insights into Imaging

To adjust the results, we performed a multivariate logistic regression.

Sports Medicine - Open

Independent risk factors were analysed using a multivariate logistic regression.

Annals of Intensive Care

Multivariate logistic regression identified factors associated with hospital admission.

International Journal of Emergency Medicine

The latter finding was confirmed by multivariate logistic regression analysis.

International Journal of Bipolar Disorders

Expert writing Tips

Best practice

When reporting results from a "multivariate logistic regression", always specify the odds ratios, confidence intervals, and p-values for each predictor variable to provide a complete picture of the findings.

Common error

Avoid interpreting odds ratios from a "multivariate logistic regression" as direct probabilities. Odds ratios represent the change in the odds of the outcome, not the probability of the outcome itself. Report probabilities for the outcome separately.

Linguistic Context

The phrase "multivariate logistic regression" functions as a statistical term, specifically identifying a type of regression analysis used to predict the probability of a binary outcome based on multiple predictor variables. As Ludwig AI confirms, this term is correct and widely used in the scientific community.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Reference

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Ludwig's WRAP-UP

In summary, "multivariate logistic regression" is a statistically sound and very common term primarily used within formal and scientific domains. As validated by Ludwig AI, it is used to specify a regression analysis method for predicting binary outcomes from multiple independent variables. When using "multivariate logistic regression", it's important to understand and accurately interpret the resulting odds ratios, confidence intervals and p-values. Remember to consider the underlying assumptions of the model to ensure the validity of your findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

multivariable logistic regression

Synonymous, simply using 'variable' instead of 'variate'.

multidimensional logistic regression

Emphasizes the multiple dimensions or variables involved in the logistic regression analysis.

multifactorial logistic regression

Highlights the presence of multiple factors influencing the outcome in the logistic regression model.

multiple logistic regression analysis

A more general term indicating the use of logistic regression with multiple independent variables.

logistic regression with multiple variables

Focuses on the aspect of handling several variables within the logistic regression framework.

logistic regression for multiple predictors

Highlights the use of multiple predictor variables in the logistic regression model.

regression analysis with multiple logistic variables

Reverses the order of 'logistic' and 'variables'.

logistic regression with covariate adjustment

Specific adjustment method to account for confounding variables

adjusted logistic regression model

Highlights that the model is adjusted for confounding variables.

complex logistic regression

Emphasizes the complexity of logistic regression due to the multiple variables.

FAQs

How do I interpret the results of a "multivariate logistic regression"?

The results of a "multivariate logistic regression" are typically interpreted by examining the odds ratios (OR) and their associated confidence intervals (CI) for each predictor variable. An OR greater than 1 indicates a positive association with the outcome, while an OR less than 1 indicates a negative association. The CI provides a range of plausible values for the OR. Statistical significance is determined by the p-value; a p-value less than a predetermined significance level (e.g., 0.05) suggests that the association is statistically significant.

What is the difference between "multivariate logistic regression" and "multivariate linear regression"?

"Multivariate logistic regression" is used when the outcome variable is binary (e.g., yes/no, presence/absence), while "multivariate linear regression" is used when the outcome variable is continuous (e.g., height, weight, temperature). Logistic regression models the probability of the binary outcome, while linear regression models the mean of the continuous outcome.

When should I use a "multivariate logistic regression" instead of a simple logistic regression?

Use a "multivariate logistic regression" when you want to examine the relationship between multiple independent variables and a binary outcome variable simultaneously. This allows you to control for the effects of other variables and assess the independent contribution of each predictor. Simple logistic regression is appropriate when you have only one independent variable of interest.

What are some assumptions of "multivariate logistic regression"?

Some key assumptions of "multivariate logistic regression" include linearity of the log-odds with respect to continuous predictors, independence of observations, absence of multicollinearity among predictors, and a sufficiently large sample size. Violations of these assumptions can affect the validity of the results.