multiple regression models

USAGE SUMMARY

"multiple regression models" is correct and usable in written English.
You can use it when you are discussing regression analysis, a statistical method used to understand the relationships between variables. For example, "By utilizing multiple regression models, we were able to analyze the relationship between demographic characteristics and customer satisfaction levels."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Teaches students how to apply multiple regression models as used in much of political science research.

Stanford University

Fit simple ANOVA models in R, treating them as special cases of multiple regression models.

Stanford University

Teaches students how to apply multiple regression models as used in much of political science and public policy research.

Massachusetts Institute of Technology

The multiple regression models for the prediction of mechanical properties are developed.

Construction and Building Materials

Multiple regression models are developed to analyze these relationships at individual level.

Journal of Transport Geography

The completion time (in seconds) of neither TMT-B nor B-A significantly contributed to multiple regression models predicting loading coefficients of any structural component.

Nature

Multiple regression models were developed for predicting heat affected zone and taper angle.

Optics & Laser Technology

We also examined the influence of local vegetation using multiple regression models.

Biological Conservation

Multiple regression models examined the relationship between individual BMI and BE measures.

Preventive Medicine

The GMM results are compared with those from multiple regression models.

Journal of Pharmaceutical and Biomedical Analysis

Multiple regression models including 3 to 5 genes explained up to 59% of MQ trait variability.

Meat Science

Expert writing Tips

Best practice

When using "multiple regression models", clearly define the dependent and independent variables to ensure clarity and avoid ambiguity in your analysis.

Common error

Ensure that independent variables in your "multiple regression models" are not highly correlated with each other (multicollinearity). Multicollinearity can distort the coefficients and lead to incorrect interpretations. Check for multicollinearity using variance inflation factor (VIF) and address it by removing one of the correlated variables or using techniques like principal component regression.

Linguistic Context

The phrase "multiple regression models" functions as a noun phrase, typically serving as the subject or object in a sentence describing statistical analysis. Ludwig examples demonstrate its use in academic and scientific contexts, where it's used to denote a specific set of statistical techniques.

Expression frequency: Very common

Frequent in

Science

61%

Academia

39%

News & Media

0%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "multiple regression models" is a common and grammatically correct term used in statistical analysis, particularly within scientific and academic fields. Ludwig provides numerous examples demonstrating its use in contexts ranging from political science to engineering and medicine. As Ludwig AI confirms, it is suitable for describing the application of regression analysis with multiple independent variables. When employing "multiple regression models", it's crucial to address potential issues like multicollinearity to ensure the validity of the results. Alternatives such as "several regression models" or "multivariate regression models" can be used depending on the specific context. Always define variables clearly to maintain clarity and avoid ambiguity in your analysis. Remember that understanding the underlying assumptions and limitations of "multiple regression models" is essential for accurate interpretation and reporting of findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Several regression models

Uses a different quantifier while retaining the core concept of using multiple models.

Numerous regression models

Employs a synonym for "multiple", still referring to several regression models.

Many regression models

Replaces "multiple" with "many", indicating a quantity of regression models.

Various regression models

Substitutes "multiple" with "various", highlighting the diversity of regression models used.

Multivariate regression models

Replaces "multiple" with the more technical term "multivariate", emphasizing the multiple variables involved.

Regression model ensembles

Focuses on the ensemble aspect of regression models, implying the use of multiple models.

Multiple linear regression models

Adds the qualifier "linear" to specify the type of regression models being used.

A set of regression models

Rephrases the concept to focus on a set, indicating a collection of models.

Regression models with multiple predictors

Shifts the focus to the predictors used in the regression models.

Combined regression models

Highlights the combination aspect of multiple regression models.

FAQs

How do I properly use "multiple regression models" in a sentence?

You can use "multiple regression models" to describe the statistical method being applied, such as, "We employed "multiple regression models" to analyze the relationship between several independent variables and a dependent variable."

What are some alternatives to saying "multiple regression models"?

Depending on the context, you can use alternatives like "several regression models", "multivariate regression models", or "regression model ensembles".

When should I use "multiple regression models" instead of simple linear regression?

Use "multiple regression models" when you want to examine the relationship between one dependent variable and two or more independent variables. Simple linear regression is suitable for examining the relationship between one dependent variable and only one independent variable.

What are some common pitfalls to avoid when using "multiple regression models"?

Some common pitfalls include multicollinearity (high correlation between independent variables), overfitting (model fits the training data too closely and performs poorly on new data), and violating the assumptions of linear regression (linearity, independence of errors, homoscedasticity, and normality of residuals).