linear regression model

USAGE SUMMARY

"linear regression model" is a correct and usable phrase in written English.
You can use it when discussing topics such as machine learning and data analysis. For example, "We can use a linear regression model to predict future trends in the market."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Consider a linear regression model.

Journal of Statistical Planning and Inference

In statistics, we focus on the linear regression model.

Stanford University

This amount is the minimal number of observations required to fit a linear regression model.

Nature

A linear regression model was used to determine the difference between conditions.

Nature

The r2 represents the fraction of the variation explained by a linear regression model.

Nature

This fit accounts for 34% of the total variance using a linear regression model (Fig. 2b).

Nature

A linear regression model was fitted for tumorgrafts TGI against PAS.

Nature

A difference in quality of anti-Pfs25 antibodies judged by SMFA was evaluated using a linear regression model.

Nature

We consider inference about a scalar coefficient in a linear regression model.

Princeton University

Analysis of Randomized Experiments, Linear Regression Model, Instrumental Variables, Methods for Causal Effects.

Stanford University

linear regression model.

Journal of Applied Volcanology

Expert writing Tips

Best practice

When reporting the results of a "linear regression model", always include key statistics such as R-squared, p-values, and coefficients to provide a comprehensive understanding of the model's performance and significance.

Common error

Avoid blindly applying a "linear regression model" without first checking if the relationship between variables is actually linear. Use scatter plots and residual plots to visually assess linearity, and consider transformations or non-linear models if necessary.

Linguistic Context

The phrase "linear regression model" functions as a noun phrase that identifies a specific type of statistical model used for predicting the value of a dependent variable based on one or more independent variables. As Ludwig AI confirms, it is a grammatically sound term widely used in academic and scientific contexts.

Expression frequency: Very common

Frequent in

Science

75%

Academia

20%

News & Media

2%

Less common in

Formal & Business

1%

Encyclopedias

1%

Wiki

1%

Ludwig's WRAP-UP

The phrase "linear regression model" is a common and grammatically correct term, as confirmed by Ludwig AI, used primarily in scientific and academic contexts. It refers to a specific statistical technique for modeling the relationship between variables. The term's register is formal and scientific, typically appearing in research papers and technical documentation. When using this term, it's crucial to understand the model's assumptions and to verify linearity before application. Related phrases include "ordinary least squares regression" and "multivariate regression analysis". The sources analyzed highlight its prevalence in scientific research, emphasizing the importance of understanding its proper application and interpretation. Ludwig's examples showcase the breadth of contexts in which "linear regression model" is used, reinforcing its importance in statistical analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

generalized linear model

Expands the model to include non-normal distributions and a link function, broadening the applicability beyond standard linear regression.

ordinary least squares regression

Replaces "linear regression model" with a specific type of linear regression focusing on minimizing the sum of squared errors.

statistical regression modeling

Adds "statistical" and changes "model" to "modeling", emphasizing the statistical nature and process of regression.

multivariate regression analysis

Broadens the scope to include multiple dependent variables instead of just one, compared to a standard "linear regression model".

regression with linear constraints

Highlights the presence of linear constraints within the regression framework, adding a specific detail.

regression analysis

Simplifies the term by removing "linear", indicating a general regression approach that may or may not be linear.

econometric model

Specifies the application of a linear regression model within the field of econometrics.

linear model

Shortens the phrase, focusing on the linear aspect of the model while omitting the specific term "regression".

least squares estimation

Refers to the estimation method often used in linear regression, changing the focus from the model to the estimation technique.

predictive modeling

Shifts the focus to the predictive aspect of the model rather than the specific statistical method.

FAQs

How do I interpret the results of a "linear regression model"?

Interpreting a "linear regression model" involves examining the coefficients to understand the relationship between independent and dependent variables, assessing the p-values to determine statistical significance, and evaluating the R-squared value to gauge the model's explanatory power. Also, always remember to validate the assumptions of linearity, independence, homoscedasticity and normality.

What are some assumptions of the "linear regression model"?

Key assumptions include linearity (the relationship between variables is linear), independence of errors (residuals are uncorrelated), homoscedasticity (constant variance of errors), and normality of errors. Violations of these assumptions can affect the validity of the model's results.

What is the difference between a "linear regression model" and a "logistic regression model"?

A "linear regression model" predicts a continuous outcome variable, while a "logistic regression model" predicts a binary or categorical outcome. Logistic regression uses a sigmoid function to model the probability of the outcome.

What are some alternatives to using a "linear regression model"?

Alternatives include "polynomial regression" (for non-linear relationships), "multiple regression" (for multiple independent variables), or non-parametric methods (when assumptions of linear regression are violated).