Formation xlstat software#

These results demonstrate a high accuracy and reproducibility of sensory information obtained by consumers applying descriptive sensory methods in foods. The RV coefficient was 0.94, indicating a very high similarity between both methods. The results of the flash profile and CATA questions showed the formation of four groups, the first group consisting of samples D and E the second, third and fourth groups, A, B and C, respectively.

Formation xlstat software#

In addition, the RV coefficient was used to compare the sensory maps generated by both methods, using the R software and XLSTAT-test version. For data analysis, generalized procruster analysis (GPA) was used for the PF and the Cochran Q test with correspondence analysis for the CATA questions. Participants evaluated five Andean Mint beverages using flash profile (FP) methodology (n=20) and Check-All-That-Apply (CATA) questions (n=105). This study compares two descriptive methods based on consumers' perceptions of the sensory profile of Andean Mint (MuГ±a) herbal teas. Comparison of descriptive sensory methods: flash profile and CATA questions to characterize muГ±a infusions (Minthostachys mollis). Click here to check out other exploratory methods.Д«y height, weight and time spent on site.SILVA PAZ, Reynaldo J. Looks like there are two clusters in the data And so on. Time spent on site decreases with weight & height Derrick has big feet. Weight increases with height Shoe size is unrelated to weight / height

How PCA looks like in realitењњhart 1: correlation circle chart 2: observations Interpreting the axesChart 1: correlation circle Right angle: uncorrelated variables (e.g. Acute angle: positively-linked variables How PCA looks like in realitењњhart 1: correlation circle Second thing you can do: classification ( = clustering = segmentation)Examples: Agglomerative Hierarchical Clustering, k-meansДЄnalysis (PCA)Id like to summarize a big data set in a Exploratory tools are part of Data Mining.ДЇirst thing you can do: concentrate the information of bigДatasets in a few dimensionsExamples: Principal Component Analysis, Correspondence Analysis Look for information in a multi-variables data set, without having very StatisticsI want to easily extract information Time for Exploratory statistics (or ExploratoryД®xample: Principal Component Analysis (PCA)We want to analyze multiple variables (dimensions) at a time the same way we did with the 2D scatter plot. Imagine having the same kind of reasoning Plutonians spend more money on the website Invoice amount decreases with time spent Toward exploratory data analysis: scatter plot To the categories of the qualitative variable

Scatter plot with points colored according variableQuantitative descriptive statistics perД¬ategory of the qualitative variable multiple variableCross tabulation (contingency table)ДЎ quant. variableCenter (mean / median) dispersion variableFlat sorting, mode, pie chartsДЎ quant. Examples: customers, surveyed people,Дescriptive statisticsCommonly used tools according to the situationДЎ qual. Examples: invoiceДЄmount, number of likes on Facebook, sugarД®lementary statistical unit. Socioprofessional category, geographical origin,ДЄ variable that can be quantified. Companies, education, researchДЄn element that can take different valuesДЄ variable that cannot be quantified. Principal Component Analysis (PCA): concept and practiceДЄgglomerative Hierarchical Clustering (AHC): concept and practiceДЄll the data in this class were made up unlessДЄcross the world. Toward exploratory data analysis: scatter plot colored by group The analysis showed the formation of four groups of beverages according to preference by three different segments. Reminder: Variables, individuals, Descriptive Statistics