Sentence examples for linear regression process from inspiring English sources

This paper presents a sequential estimation procedure for the unknown parameters of a continuous-time stochastic linear regression process.

To this end, a linear regression process that models the relationship between a set of 2D occluding contours and a set of 3D vertices is applied onto the corresponding training sets using Partial Least Squares.

Some numbers of top ranked features were then selected and gradually increased in a multiple linear regression process employed for building a linear quantitative structure-activity relationships (QSARs) for both human and pig FMOs.

For the sake of explanation, a first-order linear regression process has been applied on the obtained raw dataset.

The forward entry multiple linear regression process led to the identification of four best models, one for each of the data sets and a best model for the KSC average that included the same polymorphisms as the best model for S. The best model for the K8 results, which included genotypes at two SNPs, rs3807306 and rs17424179 (Table 2), explained 0.31 of the variance in IRF5 expression.

Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [ 9, 17].

It can be used in the reduction of a multiple linear regression model process, identifying those terms in the original model that explain the most significant amount of variance.

The multiple linear regression selection process for the decision-making subscale (Table  3), which had age, education and study group adjusted for, selected lower age, female gender, marital status, API information-seeking scores and white ethnicity background at 5%% significance level, in favour of greater autonomy in decision-making.

The multiple linear regression selection process for the information-seeking scores (Table  3), which had age, education and study group adjusted for, selected lower age, post high school education, marital status and per category increase in BDI score at the 5%% significance level as significantly associated with information-seeking.

Linear regression, Gaussian processes, and support vector machine learning have been used to model sequence-function relationships and predict useful chimeras.

Multiple linear regression analysis for process optimization revealed that the acceptable OMT PLC was obtained wherein the optimal values of X1, X2 and X3 were 3, 60 °C and 3 h, respectively.