Examples of 'linear models' in a sentence
Meaning of "linear models"
Linear models are statistical techniques used to study the relationship between a dependent variable and one or more independent variables. They assume a linear relationship between the variables
How to use "linear models" in a sentence
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Advanced
linear models
Linear models imply simplistic policy approaches.
Generalization to nonlinear or generalized linear models is indicated.
The old linear models of development are obsolete.
Regression analysis and introduction to linear models.
Generalised linear models were applied to the data.
Such models are called linear models.
It is two linear models stacked on top of each other.
Actuarial applications of generalized linear models.
Estimation in generalized linear models with random effects.
On quantile quantile plots for generalized linear models.
A common use of linear models is to describe a nonlinear system by linearization.
Proper and improper linear models.
In generalized linear models each parameter corresponds to one covariate.
Exponential family of distributions and generalized linear models.
Extended filters based on linear models are applied for diagnosis.
See also
The thesis focuses mostly on generalized linear models.
Linear models and applications.
A unified approach to mixed linear models.
Linear models are becoming increasing inadequate for this purpose.
There are various types of linear models.
Linear models are relatively simple.
Support with generalized linear models.
Linear models we looked at.
Now the models we are going to do in this unit are not linear models.
General linear models.
Then we moved on from categories to talk about linear models.
To this was employed linear models with gamma distribution and identity link.
This model is more technological than other linear models.
We estimated linear models for a measure of the severity of the workplace accident.
We can use this metric to compare different linear models.
These linear models provide an elegant way to do performance evaluation for TEGs.
The work was based on the theory of generalized linear models.
What linear models will do is they will explain some percentage of the variation.
It focus a lot in linear models.
These linear models will also tell us the sign and magnitude of coefficients.
Remember in the last lecture we talked about simple linear models.
Linear models are just better than just sort of thinking off the cuff.
It is sometimes convenient to expand the architecture to include local linear models.
Data were analyzed using the generalized linear models for repeated measures.
Interpretation of interaction effects is typically more complicated than in linear models.
For linear models it is easy to see the intuitive appeal of such piecemeal strategies.
It plays an important role in exponential dispersion models and generalized linear models.
Most of the control strategies suggested use linear models to calculate discharge routing.
Matrices provide a very powerful tool for dealing with linear models.
The statistical analysis employed mixed linear models with variance and covariance structures.
One thing you can do is you can fit that data to linear models.
You can reason by linear models and people can have different linear models.
Univariate and multivariate analyses were conducted by generalized linear models for discrete variables.
The linear models failed to capture arbitrary differences between levels of input and output signal.
Market research problems can also be analyzed by using hierarchical generalized linear models.
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