Examples of 'linear regression models' in a sentence
Meaning of "linear regression models"
Linear regression models are statistical models used to describe the relationship between two or more variables. They are commonly used in data analysis and predictive modeling to understand the linear relationship between a dependent variable and one or more independent variables
How to use "linear regression models" in a sentence
Basic
Advanced
linear regression models
To do this we used multiple linear regression models.
Linear regression models were estimated with logistic regression models.
We consider more particularly linear regression models.
Multivariate linear regression models were used to estimate income elasticity coefficients.
In the first experiment two linear regression models are used.
The linear regression models are primarily the one variable ones.
Relationship with linear regression models.
Linear regression models provide a simple approach towards supervised learning.
This shows that the linear regression models were adequate.
Linear regression models for repeated measures were used to analyze the data.
The answers were analyzed using linear regression models.
The multiple linear regression models initially included all variables with p.
Survey data are often used to fit linear regression models.
The multiple linear regression models are an extension of the simple linear regression.
Similar phenomenon occurred with the five linear regression models.
See also
The assumptions of linear regression models were analyzed graphically and through statistical tests.
The graph shows coefficients of three different simple linear regression models.
The data was studied and linear regression models were contributed.
A trend analysis of mortality rates was performed using simple linear regression models.
Cox proportional hazard and linear regression models were used in the analyses.
It supplied criteria for choosing method for adjustment of linear regression models.
We then used multiple linear regression models to determine associations between these variables.
The significance of the momentum and momentum spillover returns are studied using linear regression models.
Analyses of cognitive trajectory used linear regression models with generalized estimating equations.
We used linear regression models to examine independent relationships between pGSN and other covariates.
The association between late preterm birth and psychiatric symptoms was examined with linear regression models.
Main results did not differ from linear regression models and therefore were not presented.
Analyses were performed using principal component factor analysis and ordinary least squares linear regression models.
The first are simple linear regression models with and without controls for observable factors.
MARS models are more flexible than linear regression models.
Linear regression models are based on the Microsoft Decision Trees algorithm.
It is therefore recommended for use in estimating the parameters of Linear Regression Models.
First, linear regression models are easy to interpret.
To predict patient satisfaction, linear regression models were built.
Simple linear regression models are of the form,.
To evaluate the effect of polymorphism, linear regression models were used.
Linear regression models are generally based on the following assumptions,.
Fat distribution-related factors were analyzed in linear regression models.
In this sense, simple linear regression models were adjusted for each temporal series.
Variables showing significance at the 5 % level were entered into multiple linear regression models.
In this thesis, we consider linear regression models to deal with these three characteristics.
Let us talk about something a little more ethereal, such as multivariate multiple linear regression models.
For continuous measures, we used linear regression models to estimate mean change from baseline.
Linear regression models that gave the best-line-fit with minimal residual variance were developed.
Table 7 shows the results for the dynamic multiple linear regression models estimated by Sys-GMM.
Separate simple linear regression models of y on x within the levels of A, with.
Associations with BMI at 42 months of age were assessed using multivariate linear regression models.
To this end, dynamic multiple linear regression models were tested based on Equation 1.
Functional associations were derived from KEGG orthologies (KO) with linear regression models.
For the first objective, linear regression models and Cox models were used.
You'll also be interested in:
Examples of using Models
Show more
The different possible models are detailed below
Models formulae engineering designs and specifications
Parents can be better models to their children
Examples of using Regression
Show more
Your regression models are the stuff of legend
Inhibition results in the regression of prostatic tumours
Regression analysis was used to estimate this effect
Examples of using Linear
Show more
Rotary tables linear system handling software
Linear guideways and cross roller guides
Glass scales as linear length measuring system
