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The outcome variable HD-rate has a Poisson distribution that approximates a normal distribution when the probability for the outcome is high (> 5%), thus allowing us to use a linear regression model in SPSS (Statistical Package for Social Sciences) v. 16 and SAS (Statistical Analysis System) v. 9.2.
We propose to use a linear regression to model CPU, Memory, disk space and Bandwidth.
We also use a linear regression model to analyze factors affecting elderly people's satisfaction level on their current housing.
By using a linear activation function for the output neuron in the CasCor algorithm, it means that, initially, we use a linear regression to fit the transformation function.
In this study, we use a linear regression based statistical downscaling method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25° and 0.50°, and compare them.
For the final normalization of the sample RCs, we use a linear regression over the baseline.
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Predicted ages from portable luminescence reader signals using a linear regression appear to be indicative and useful.
Equation 5 indicates that given the QTL genotype we can regress y on the covariate z using a linear regression model.
The AUC was modelled using a linear regression model.
The adaptation is performed using a linear regression transformation matrix based on an NMF framework.
We also used a linear regression model and obtained similar results, but a loss of power.
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CEO of Professional Science Editing for Scientists @ prosciediting.com