Sentence examples for multivariate statistics such from inspiring English sources

Data evaluation was performed by means of multivariate statistics such as multiple linear regression, principal component analysis (PCA), and partial least squares (PLS) modeling.

Data-driven techniques based on multivariate statistics (such as principal component analysis (PCA) and partial least squares (PLS)) have been applied widely to chemical processes and their effectiveness for fault detection is well recognized.

Multivariate statistics such as cluster analysis or tree-based models, two of them are the classification and regression trees [CART] and chi-squared automatic interaction detection (CHAID), serve to spatially differentiate the multiple relationships between geodata stored in a GIS.

Key variables can then be identified using multivariate statistics, such as multi-site generalised dissimilarity modelling (Latombe et al. 2017), to discern different combinations of determinants behind species turnover driven by species of different levels of occupancy.

Consequently, many multivariate statistics such as multivariate analysis of variance, which require more subjects than variables, could not be conducted.

Using exploratory multivariate statistics such as CCA, the effective number of variables can be reduced while preserving the information content.

Twenty MSBA includes PCA, DPCA, FDA.DFDA, CVA, PLS, MS (multivariate statistics) based statistic measurement (such as Hotelling T2 or Q statistic) [17] and average values of correct diagnosis rates of 20 MSBA are listed in Table 8.

Kowalenko (2014) argues that cp-laws are best understood by drawing on methods from statistics, such as multivariate regression analysis (for a discussion of similar statistical approaches see Hüttemann and Reutlinger 2013, Reutlinger 2014, Roberts 2014, and Strevens 2014).

As our survey was not carried out on random samples or subsets of the targeted populations but consisted of a census, covering the whole cross-sectional population of trainees and trainers in PGMT in AM hospitals in Germany and Switzerland, interferential statistics such as multivariate analyses were not planned.

This includes attributes, such as multivariate statistics (for example, regression analysis, principal component or factor analysis); content analysis; historical or policy analysis; uncertainty treatment; modeling and simulation; spatial analysis; spatial or temporal mapping; and indexing.

While we realize that this number of participants is below the recommended number for using multivariate statistics, the results are fairly clear-cut such that the addition of a few more cases would be unlikely to change the outcome.