Develop an instinct for mapping the variation in the data to the business questions.
For example, the three-variable probability model that I referred to above explains less than ten per cent of the variation in the data.
In a principal component analysis (PCA), based on 4,296 SNPs, the first three axes explained 11.3% of the variation in the data (Fig. 3).
In fact, in the native vegetation there was a significant linear relationship between above-ground biomass and SOC that explained 88.8% of the variation in the data.
The first four PCs, explaining 76% of the variation in the data, described the size, charge distribution and symmetric elements of the congeners.
The PCA reduced the number of variables from 27 to 5. A five-component PCA solution explained between 81 and 89% of the variation in the data set.
And alternative splice variants are going to be a lot of the variation in the data that comes into play in terms of assembling it and interpreting what that assembly means.
A greater F-value indicates that the factors adequately explain the variation in the data.
The first canonical axis accounted for 77%% of the variation in the data set.
Figure 5b depicts the variation in the data rate achieved for different time slots.
The combination of all hypotheses explains most of the variation in the data (15%).
