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For example, in the estimation of dog genome, it can estimate the global parameters, such as neutral mutation rate and repeat content. [3] GSS is also an effective way to large-scale and rapidly characterizing genomes of related species where there is only little gene sequences or maps. [4]
The online GSS Data Explorer [5] allows users to download GSS data that can be imported to statistical programs (e.g., R/SAS/SPSS/Stata). It also allows searching for information about GSS questions, variables, and publications, testing hypotheses, as well as conducting basic analyses.
Because a cluster-based frame contains less information about the population, it may place constraints on the sample design, possibly requiring the use of less efficient sampling methods and/or making it harder to interpret the resulting data. Statistical theory tells us about the uncertainties in extrapolating from a sample to the frame.
It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. [1] Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element.
GSS (Graph Style Sheets) in mathematics and computing, is an RDF (Resource Description Framework) vocabulary for representation of data in a model of labeled directed graph. [1] Using it will make a relatively complex data resource modeled in RDF , much easier to understand by declaring simple styling and visibility instructions to be applied ...
The sample comprises people born on one of four selected dates of birth and therefore makes up about 1% of the total population. The sample was initiated at the time of the 1971 Census, and the four dates were used to update the sample at the 1981,1991, 2001 and 2011 Censuses and in routine event registrations.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
For example, a database of SNPs used in Douglas fir breeding programs was created by de novo transcriptome analysis in the absence of a sequenced genome. [159] Similarly, genes that function in the development of cardiac, muscle, and nervous tissue in lobsters were identified by comparing the transcriptomes of the various tissue types without ...