general linear regression model
standard linear regression mode
simple linear regression
simple linear regression model
multiple normal linear regression mode
classical normal linear regression model
And I would ban linear regression.
Simple linear regression is also a versatile modeling technique.
Some problems on heteroscedasticity in multi-linear regression models;
The concentration factor (CF) and time (t) was linear regression relation.
One of r's strengths is its ability to calculate linear, as well as nonlinear regression models.
Method Statistical methods of linear regression, multiple stepwised regression, two-way ANOVA were used to analysis the relation of TTS with audition, noise level, working-year and age.
The linear regression equation on the relationship between peroxide content in acrylonitrile and PAN fiber yellowness was obtained by regression analysis.
These transformations can linearize the data so that simple linear regression can be used to model the data.
This paper considers the estimation in the linear regression model under the criterion of minimizing the sum of absolute errors.
By using multivariate linear regression analysis, the mathematical model to classify and evaluate reservoir with well logging data was established.
To know preschooler's constitution and to study methods of making normal values of preschooler's physical exertion using linear regression model.
The correlative coefficients of linear regression function for isochronous stress-strain curve are all higher than 0.
In addition, results of the linear regression for stress and instantaneous strain show that the instantaneous axial strain and axial load approximated to grow proportionally.
To study the relation between the content of hemoglobin and calcium, iron and cuprum in children's blood, we use multiple linear regression analysis.
Method According to the theory of detecting outliers in linear regression analysis, the measured value of the samples in quality control was processed by EXCEL.
Presents a robust estimate for the linear regression of sensor output characteristic based on the robustness of least absolute deviation estimate.
This paper first assort the topography of different district with the fuzzy classification, and then the expanding coefficient is gotten by the model of linear regression, so we can get more accurate information for the network planning.
This study using linear regression model to examine if there are relations between independent and dependent variables, and the results indicate that two of these factors( interest coverage ratio and risk) have a significant association with the firm's discretionary accruals.
The linear regression model is used to calculate and measure the effect of investment to economic growth in regions of the Bohai, Yangtze River Delta, Pearl River Delta and the Central Region of China, and different degrees of investment dement in the interior of the four regions.
On the basis of our mathematic models of the titration, the content and conditional dissociation constant may be determined by using nonlinear regression.
Afterwards the results are analyzed through nonlinear regression in order to get the function between the amount of dust particles and time of vibration and vibration frequency.
A kind of nonlinear regression estimator for limited Population is given and the quality and the sampling variance about is discussed.
