Examples of 'linear regression model' in a sentence
Meaning of "linear regression model"
Linear regression model: A statistical method used to analyze the relationship between two or more continuous variables. It aims to find the best-fitting straight line (or plane) to describe the relationship between the variables
How to use "linear regression model" in a sentence
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linear regression model
You can run a linear regression model.
A linear regression model is fitted to the data.
An analysis of residues in the linear regression model was performed.
Linear regression model for assessment of uncertainties measurement.
Specification and estimation of the linear regression model.
A simple linear regression model therefore was assumed.
Coefficients of multiple linear regression model.
A simple linear regression model was used.
Statistical analyses were conducted using linear regression model.
A simple linear regression model was used to obtain a predictor model.
There are a number of equivalent ways to write a linear regression model.
For statistical analysis a linear regression model or variance analysis we.
Residue analysis showed good adjustment of multiple linear regression model.
Multiple linear regression model.
A basic tool for econometrics is the multiple linear regression model.
See also
A multivariable linear regression model was fitted to the data.
The estimation of this model is based on a linear regression model in which.
The linear regression model is a very simple regression model.
The equation of the final linear regression model is described below.
Here are three equivalent ways to mathematically describe a linear regression model.
The best fit linear regression model is also shown.
The basic tool for econometrics is the linear regression model.
Multiple linear regression model and least squares estimation.
These eight independent variables were included in the multiple linear regression model.
Then the multiple linear regression model was developed in two stages.
One application of normality tests is to the residuals from a linear regression model.
Suppose you are training a linear regression model using gradient descent.
The linear regression model of monthly income is given by the following equation.
Data were analysed using univariate statistics and multiple linear regression model.
Simple linear regression model was used to analyze and compare mortality trends.
The evaluation of the proposed correlation is given by applying a multiple linear regression model.
Multivariate linear regression model was used to identify factors independently associated to HRQoL.
The least squares estimators are point estimates of the linear regression model parameters β.
A linear regression model was then applied to estimate the pathway activation levels.
The primary focus is on the classical linear regression model and violations of its assumptions.
The linear regression model was considered appropriate when used for continuous variables.
The scatterplot shows a set of data for which a linear regression model appears appropriate.
The linear regression model is commonly used in empirical applications in many dierent fields.
Our discussion of the assumptions underlying the classical linear regression model is now completed.
The multiple linear regression model was developed with the stepwise forward procedure.
Correlation analysis of plasma hormone concentrations was carried out using a general linear regression model.
A multivariate logistic and a linear regression model were built to control for confounding variables.
The relationship between these biological and airborne measurements is then estimated through a linear regression model.
A linear regression model is a more general and interesting case than previous ones.
The statistical analysis used a linear regression model with mixed effects and tukey test p.
Linear regression model for the prediction of cranial capacity were formulated for both sexes.
The assumptions of the classical linear regression model remain in place for GWR.
The linear regression model can only work with data that are independent.
In our work we are using a Bayesian linear regression model for demand forecasting.
A mathematic linear regression model is developed to represent the growth evolution of each company.
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