Sentence examples for linear regression setting from inspiring English sources

We show in a least-squares linear regression setting that this intuition is wrong.

A monitoring approach based on combining these nonlinear dynamic features with conventional statistical descriptors of sensor signals as well as process parameter settings is found to improve the tracking of MRR which is one of the most important performance variables in the CMP process by over 20% (linear R2>80%) compared to the use of conventional features in a linear regression setting.

Then we included this methylation level of TEs found within each gene as a separate variable in a multiple linear regression setting.

Using a model of multiple linear regression, setting the age as the predictive variable and adjusting by ethnicity and gender, the following correlations were still significant: PTH (p = 0.01), ionized calcium (p = 0.048) and month of the year (p = 0.01).

Using a model of multiple linear regression, setting the age as the predictive variable, 25(OH D (p = 0.001), creatinine (p = 0.001) and ionized calcium (p = 0.011) were still significant.

This method, which follows Dennis et al. (1991), involves two steps: (i) calculating simple transformations of the counts and years in which the counts were taken; and (ii) performing linear regression, setting the regression intercept to zero and obtaining estimates of µ and σ (Morris et al. 1999).

In the linear regression set up, Murtaugh (2009) evaluated the prediction power of various variable selection methods for ecological and environmental data sets.

Raw data were converted into percent inhibition through linear regression by setting the high-inhibition control as 100% and the no-inhibition control as 0%.

As GGT levels showed the highest increase in the patients, a stepwise multiple linear regression model was set to determine components that significantly predicted plasma GGT level variations in ACS patients (Table 3).

To examine factors influencing the random walk like behavior of genomic DNA sequences, a linear regression model was set up with ZOM, FOM and SOM OUV as response variables to the following predictor variables: growth temperature, AT content, chromosome size, habitat and phyla.

Multiple linear regression models were set up.