4 - PCA estimation, centering/scaling, variance explained and biplot
This video conceptually shows the estimation of principal components, go through the math of centering and scaling and gives intuition on interpretation of biplot and global- vs local (variable wise) variance explained. Data used in this series can be downloaded as an R packages: devtools::install_github('mortenarendt/DataAnalysisinFoodScience') library(ggplot2) qplot(data = DAinFoodScience::coffeetemppanel, Sample, Intensity,color = factor(Assessor), group = factor(Assessor):factor(Replicate)) + geom_line()

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5 - Correlation and Covariance - Nuts and bolt

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StatQuest: Principal Component Analysis (PCA), Step-by-Step

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Principal Component Analysis (PCA) - easy and practical explanation

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Principal Component Analysis (PCA)

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1 - Descriptive Statistics

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3 - PCA concept

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StatQuest: PCA main ideas in only 5 minutes!!!

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Principal Component Analysis (PCA)

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Principal Component Analysis (PCA) in R (presence-absence data)

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PCA vs. LDA

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PCA (course 1/3): description of the method in a French way

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Principal Component Analysis (PCA): With Practical Example in Minitab

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Preprocessing 1. Centering & Scaling
![Principal Component Analysis (PCA) [Matlab]](https://i.ytimg.com/vi/VqjJ5YYt78Y/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLAvqSghAwnogJ3KbkrYBVFF14UveQ)
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Principal Component Analysis (PCA) [Matlab]

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Dimensionality Reduction: Principal Components Analysis, Part 1

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StatQuest: PCA in R

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Nobody Breaks Celebrities Like Rowan Atkinson

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Principal Component Analysis: Computation and interpretation

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How to Answer ANY Question (Even If You Don't Know The Answer!)

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