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For this case, we finally present just the following particular result which applies to both estimating and testing.
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Below, we will derive a statistical algorithm for estimating and testing these parameters.
The tagwise dispersions were estimated and then used for logFC (log2 fold change) estimating and testing.
By estimating and testing these parameters, the genetic architecture of a trait can well be studied.
The GLM function in R was used to estimate and test both the replicate effect and the resistance effect for each quartet of Poisson counts, with offsets determined by the total mapped reads for each sample.
To estimate and test the association between adducts and behavioral outcomes, both CBCL continuous raw scores and dichotomized T-scores were analyzed.
Separate coefficients for each equation should be estimated and tested.
Variance of ω was estimated and tested using an F-test as established in Excel.
The hazard ratio comparing control and treatment group will be estimated and tested within this model.
Model-adjusted means and their difference between the two treatment groups will be estimated and tested.
Contrasts were constructed to estimate and test expression differences between genotypes within and across days.
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