Multivariate Data and Multivariate Analysis

2018 ◽  
pp. 225-237
Author(s):  
Kimmo Vehkalahti ◽  
Brian S. Everitt
Keyword(s):  
Multivariate Analysis ◽  
Multivariate Data

scholarly journals Management Multivariate Analysis Methods for Variables Measurement in Scientific Papers

2019 ◽  
Vol 3 (1) ◽  
pp. 65-72
Author(s):  
Yulianto Yulianto ◽  
Namira Robihaningrum ◽  
Bella Dhea Elinda
Keyword(s):  
Multivariate Analysis ◽  
Data Analysis ◽  
Research Methods ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Scientific Research ◽  
Independent Variables ◽  
Several Variables ◽  
Dependent Variables ◽  
Scientific Papers

The management of writing a scientific papers we already know has important chapters in the writing. And have a way of choosing in a variety of methods. There are problems in this study, namely the absence of the use of research methods in scientific-rich management. Then one of them is needed by multivariate data analysis management to become one of the methods in writing scientific papers. Multivariate data is data collected from two or more observations by measuring these observations with several characteristics. There are 2 (two) methods in multivariate data, namely dependency and interdependence methods. Dependency analysis functions to explain or predict dependent variables by using two or more independent variables. Focused on the dependency method there are 9 (nine) classifications. It is expected that the multivariate data analysis management can help writers to use scientific research methods well and be able to analyze the influence of several variables on other variables at the same time

scholarly journals Multivariate Analysis under Indeterminacy: An Application to Chemical Content Data

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Muhammad Aslam ◽  
Osama H. Arif
Keyword(s):  
Multivariate Analysis ◽  
Multivariate Data ◽  
Classical Statistics ◽  
Chemical Content ◽  
Neutrosophic Statistics ◽  
Uncertainty Environment

The Hotelling T-squared statistic has been widely used for the testing of differences in means for the multivariate data. The existing statistic under classical statistics is applied when observations in multivariate data are determined, precise, and exact. In practice, it is not necessary that all observations in the data are determined and precise due to measurement in complex situations and under uncertainty environment. In this paper, we will introduce the Hotelling T-squared statistic under neutrosophic statistics (NS) which is the generalization of classical statistics and applied under uncertainty environment. We will discuss the application and advantage of the neutrosophic Hotelling T-squared statistic with the aid of data. From the comparison, we will conclude that the proposed statistic is more adequate and effective in uncertainty.

scholarly journals A Review on Outliers-Detection Methods for Multivariate Data

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Sharifah Sakinah Syed Abd Mutalib ◽  
Siti Zanariah Satari ◽  
Wan Nur Syahidah Wan Yusoff
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Outlier Detection ◽  
High Dimension ◽  
Detection Method ◽  
Multivariate Data ◽  
Projection Pursuit ◽  
Detection Methods ◽  
And Cluster Analysis ◽  
Detection Of Outliers

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

scholarly journals GC-FID-MS Based Metabolomics to Access Plum Brandy Quality

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1391
Author(s):  
Stefan Ivanović ◽  
Katarina Simić ◽  
Vele Tešević ◽  
Ljubodrag Vujisić ◽  
Marko Ljekočević ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Alcoholic Beverage ◽  
The World ◽  
Chemical Profiles ◽  
Plum Varieties ◽  
Expert Commission ◽  
Plum Brandy

Plum brandy (Slivovitz (en); Šljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.

Sign in / Sign up

Export Citation Format

Share Document