Sentence examples for many different statistical from inspiring English sources

Missing values occur frequently in many different statistical applications and need to be dealt with carefully, especially when the data are collected spatio-temporally.

The information about single trees, stands and sites to predict windthrow probability is crucial to many different statistical model approaches such as the binary additive regression model used in this study, a discriminant model by Valinger et al. (1993), a logistic regression model by Kellomäki and Peltola (1998), and a neural network approach by Hanewinkel et al. (2004).

As there are many different statistical approaches and designs that are available for heritability studies, one may need to be aware of the limitations of different heritability estimates.

Only very few tools integrate both microarray and RNA-Seq data and provide a user-friendly interface to many different statistical and data-mining as well as visualization methods.

There are many different statistical and computational methods for determining the quality of attributes.

Such models are implemented in many different statistical packages, all with different features and using different computational approaches.

However, these two types of study design have many differences, including the different statistical estimate (hazard ratio versus odds ratio) and different biases.

The fact that the standard code minimizes the effects of translational error in comparison to the vast majority of randomly reshuffled codes has been demonstrated convincingly by many different authors using statistical arguments.

Many different mathematical and statistical techniques have therefore been proposed to analyse incoming disease incidence reports and detect a signal that is distinct from the unavoidable noise as early as possible after the onset of any event.

We believe such methods are unlikely to succeed because the underlying biological processes are highly nonlinear, and nonlinear in many different ways, whereas statistical learning approaches to parameter estimation depend on linear models or on models that allow only a restricted type of nonlinearity.

In general, the observed differences between iASeq and the d, z, b and B statistics could be caused by many factors such as use of different statistical models, ranking statistics, or methods for parameter estimation.