Higher standard deviation means having large variations in the data, and lower standard deviation means having small variations in the data.
Cooper et al. (2001) have studied another research for the sensitivity of DEA results to variations in the data.
This "smooths" the peaks and troughs from the numbers, and allows ONS to analyse and better explain monthly variations in the data.
No hypothesis explains perfectly all variations in the data, however and all Bayes Factors for all pairs of hypotheses are decisive.
These components jointly explained about 60% of variations in the data with component 1 explaining over 17% of the variance (Table 1).
Unlike PCA which looks for major metabolic variations in the data, PLS-DA and OPLS-DA look for metabolic variations that are specifically related to the study classes.
When the buffer sizes are small, the key use intervals have to be small too, and they are more susceptible to variations in the data rate.
PCA detects major variations in the data without using sample classes, therefore being suitable for data exploration, and detection of outliers in the dataset.
Having identified efficient and inefficient DMUs in a DEA analysis, one may want to know how sensitive these identifications are to possible variations in the data.
The numerical results are close to that obtained experimentally and little variations in the data are due to ineptness of the experimental set-up and the input parameters.
The methodology used was designed to account for a range of theorised variations in the data, many of which are often overlooked in current research.
