Browne, M. W. (1975b). Comparison of single sample and cross-validation methods for estimating the mean squared error of prediction in multiple linear regression. British Journal of Mathematical and Statistical
To inspect the effects of the various parameters on clustering performance, we performed multiple linear regression between the parameters and the ARI. The regression coefficients are shown in Fig. 5a. Overall, σ appears to be the most dominant factor, and the proportion of the rare population ...
We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patien...
It is, however, designed to provide the elements of econometric theory essential for a researcher to develop and evaluate regression models. Multiple regression is not a multivariate technique in the strictest sense because the focus of the analysis is a single dependent variable. Nevertheless, the...
___ linear regression cannot be used with more than two predictors. a. Multiple b. Simple c. Complex d. Single Linear Regression: If one dependent variable and one independent variable share an appropriate relationship level, then one variable can be estimated or...
Background: In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response ...
We then reasoned that the tissue level expression of a given gene could be modeled as a weighted average of cell type-specific expression, where the weights are proportional to the relative abundance of each cell type. We used multiple linear regression to estimate the expected fraction of each...
of SCED literature: Shadish and Sullivan (2011) report a median series length of 20 with 90% of the data having 49 measurements or fewer; Smith (2012) reports that the mean number of baseline observations is between 10 and 12 for reversal and multiple-baseline designs; Pustejovsky et al. ...
Instead, combining multiple analytic approaches is often necessary [7,9,12,32]. However, it is unclear whether the challenge of identifying cell types arises from the computational complexity of the data or whether cells exist in a continuum of states and discrete cell-type labels are merely an...
[29] considered a single machine scheduling problem with the learning effect and multiple availability constraints that minimizes the total completion time. Li and Zhao [30] studied single machine scheduling with a fixed non-availability interval, where the processing time of a job is a linear ...