STATISTICAl analysis was performed using Excel 2016 and STATISTICA (Statsoft Inc., Tulsa, OK, USA). Data was subjected to analysis of variance (ANOVA) for multiple mean comparisons of content of fruit and oil phenolics along the ontogeny of the fruit according to the Fisher’s LSD test (p < 0.05). Pearson correlations were performed at p < 0.001 to study the relationship between the main groups of phenolic compounds from the olive fruit and the oil.
Excel 2016
Manufactured by StatSoft
Sourced in United States
Excel 2016 is a spreadsheet software program developed by Microsoft. It provides users with tools to organize, analyze, and visualize data through the use of worksheets, formulas, and various charting options.
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Lab products found in correlation
4 protocols using excel 2016
1
Phenolic Compounds in Olive Fruit Ontogeny
2
Statistical Analysis of Experimental Data
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Statistical analysis was made using Microsoft Excel 2016 and StatSoft Statistica v10.0. Comparisons between intergroups were made using the Mann-Whitney U-test. The level of significance was P < 0.05.
3
Phenolic Profiling of Virgin Olive Oil
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The whole set of data were imported to Excel 2016 from the instrument, and STATISTICA v.8 package (StatSoft, Tulsa, OK, USA) was used to carry out the data treatment and the statistical analysis. Brown–Forsythe test was used, and it allowed to select compounds that showed significant differences in their relative concentration (p < 0.05) among the groups.
Cluster analysis of the control samples (VOO) characterized by 15 phenols was performed by a complete standardization of the data and was implemented with the City-Block (Manhattan) distance measure and the single linkage amalgamation rule.
In addition, a principal component analysis (PCA) was carried out as unsupervised procedure to explore the result and determine the discrimination power. Firstly, we applied PCA to all the variables, and removed those with very low correlation with the first two factors as it can be supposed that they do not contribute to distinguish the whole set of samples in any manner.
Cluster analysis of the control samples (VOO) characterized by 15 phenols was performed by a complete standardization of the data and was implemented with the City-Block (Manhattan) distance measure and the single linkage amalgamation rule.
In addition, a principal component analysis (PCA) was carried out as unsupervised procedure to explore the result and determine the discrimination power. Firstly, we applied PCA to all the variables, and removed those with very low correlation with the first two factors as it can be supposed that they do not contribute to distinguish the whole set of samples in any manner.
4
Statistical Analysis of Experimental Data
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+ Expand
STATISTICAl analysis was performed using Excel 2016 and STATISTICA (Statsoft Inc., Tulsa, OK, USA). A p value less than 0.05 was considered to be STATISTICAlly significant.
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