regression model

The networks plan to use what they call a linear regression model.

A linear regression model is a more general and interesting case than previous ones.

The hypothesis that a proposed regression model fits the data well.

Shared care did not contribute to the multiple linear regression model.

The simple regression model does not allow for within study variation, this yields in to significant results too easy.

Stepwise multiple regression models were used to address research questions two and three.

Many of the published regression models are quite successful in analysing past data.

Suppose also that a regression model of nominally the same form is proposed.

For this test, a single regression model is fitted to the complete dataset.

Certain types of regression model may include threshold effects.

Such "prior decisions" become dependent dummies in the regression model.

No regression modeling technique is best for all situations.

The interpretation of this output layer value is the same as a regression model in statistics.

The basic tool for econometrics is the linear regression model.

To control for the effects of external factors, such as precipitation and soil conditions, they used a regression model.

Leverage points do not necessarily have a large effect on the outcome of fitting regression models.

The specification of such regression models necessarily involves many assumptions.

There are points for each , so we can fit a regression model to estimate more accurately.

In the more general multiple regression model, there are p independent variables:

These covariates are then used in the standard regression models for model fitting and prediction.

In a typical linear regression model we observe data on n statistical units.

Polynomial regression models are usually fit using the method of least squares.

Regression models are used to obtain hazard ratios and their confidence intervals.

We obtained similar results in a linear regression model with adjustment for confounding variables.

A multiple logistic regression model was used to analyze independent predictor variables.