Introduction to Introduction to Sensory Data Analysis - Camo

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4. Principal Component Analysis: PCA / a) Theory Principal Component Analysis (PCA) Adhesive e Average = most typical example Principal Component 1 (PC 1) Adhesive e PC2 PC1
4. Principal Component Analysis: PCA / a) Theory Varimax Rotation The aim is to enhance interpretation Rotation works on the structured part of the data only (depends on the selected number of PCs) Total explained variance is not changed (But more evenly distributed among PCs) Scores PC2 Loadings PC2 1 4 PC1 2 3 PC1 Scores Loadings
Page 1 and 2: Introduction to Sensory Data Analys
Page 3 and 4: Sensory Data Analysis: Course outli
Page 5 and 6: 1. Why sensory data analysis Can we
Page 7 and 8: 1. Why sensory data analysis Sensor
Page 9 and 10: 2. Data collection and experimental
Page 13 and 14: 3. Inspection and preparation of th
Page 31: 4. Principal Component Analysis: PC
Page 35 and 36: 4. Principal Component Analysis: PC
Page 47 and 48: 4.Principal Component Analysis: PCA
Page 49 and 50: 5. PLS regression / a) Theory Regre
Page 51 and 52: 5. PLS regression / a) Theory Examp
Page 53 and 54: 5. PLS regression / a) Theory Do a
Page 55 and 56: 5. PLS regression / a) Theory Sampl
Page 57 and 58: 5. PLS regression / a) Theory Predi
Page 63 and 64: 6. Summary CAMO Products Product Op

Introduction to Introduction to Sensory Data Analysis - Camo

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