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

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Principal Component Analysis (PCA), Principal Component Analysis in Minitab, PCA Example, Multivariate Tools, Multivariate Analysis

Hello Friends,
In the last video on Multivariate Analysis, we saw the Introduction of Multivariate analysis, some of the essential concepts used in it and the introduction of various tools and techniques as a part of it.
In this video, we will learn the 1st tool in multivariate analysis, i.e. Principal Components Analysis (PCA) in Minitab software, with the help of a practical example for easy understanding and better clarity.
This video consists of the following topics:
• What is the Principal Components Analysis (PCA)?
• Data considerations for Principal Components Analysis
• Example of Principal Components Analysis
• Conduct Principal Component Analysis (PCA) in Minitab with a practical example, including-
1. Number of components to compute
2. Type of Matrix: Correlation and Covariance
3. Graphs:
 Scree Plot,
 Score plot for the first 2 components,
 Loading plot for first 2 components,
 Biplot for the first 2 components, and
 Outlier plot
• Detailed interpretation of results from Principal Component Analysis (PCA), including:
1. Cumulative Proportion
2. Acceptable level of variance
3. The eigenvalue to determine the number of principal components
4. Detailed interpretation of:
 Scree Plot,
 Score plot for the first 2 components,
 Loading plot for first 2 components,
 Biplot for the first 2 components, and
 Outlier plot
• The conclusion from the analysis.
I am sure you will be liked it.

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0:00 Introduction of Principal Component Analysis (PCA)
1:51 Data Considerations for Principal Component Analysis
2:19 Example for Principal Component Analysis in Minitab
3:13 Conduct a Principal Component Analysis in Minitab
3:46 Selection of all options while performing Principal Component Analysis in Minitab
6:04 Interpretation of Principal Component Analysis in Minitab

And finally, thank you for watching…

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