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Further, because effects on the free energy of binding by independent factors are additive for reversible interactions, these measurements allowed us to use linear regression to rigorously estimate the magnitude and significance of main and epistatic effects on TF-RE recognition.
In our simulation study we use linear regression because it allowed us to directly control the variance independently of the regression coefficient and thus to control the effect size.
We use linear regression to fit the following: (T_{ij}=betalogtau _{ij}+alpha) to the data.
We then use linear regression to learn the entire model based on the training data.
It is common to use linear regression models for the log of costs in healthcare expenditures.
For two continuous variables (e.g., respiratory rate versus age), one can use linear regression or correlation.
Similar(8)
Detector response linearity was checked using linear regression.
Data were analysed using linear regression and logistic regression models.
Then, using linear regression, we find the best β.
Data were collected and modeled using linear regression.
The model was fitted using linear regression analysis.
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