Multivariate Tools: Complete Course in Minitab With Examples

In the last video, we had seen the 1st tool of multivariate analysis in Minitab software i.e. Principal Components Analysis with the help of a practical example.

            In this video, we are going to learn the 2nd tool of multivariate analysis in Minitab software i.e. Factor Analysis with the help of a practical example for easy understanding and better clarity.

            This video consists of the following topics:

• What is Factor Analysis (FA)?

• Data considerations for Factor Analysis (FA)

• Example of Factor Components Analysis

• Conduct Factor Analysis (FA) in Minitab with a practical example including

           1. Number of factors to extract

           2. Method of Extraction: Principal components and Maximum likelihood

           3. Type of Rotation:

                    None

                    Varimax

                    Quartimax

                    Equimax

                    Orthomax with γ:

4. Graphs:

            Scree Plot,

            Score plot for first 2 factors,

            Loading plot for first 2 factors, and

            Biplot for first 2 factors

• Detailed interpretation of results from Principal Component Analysis (PCA) including:

            Unrotated factor loadings

            Rotated Factor Loadings and Communalities

            Large loading and Small loading of factors on variables

            Categorization of data

            % of variation explained by each factor

            % of variation explained by all factors together

            Detailed interpretation of Loading plot for the first 2 components, and

• The conclusion from the analysis.

In the last video, we had seen the 2nd tool of multivariate analysis in Minitab software i.e. Factor Analysis with the help of a practical example.

            In this video, we are going to learn the 3rd tool of multivariate analysis in Minitab software i.e. Item Analysis with the help of a practical example for easy understanding and better clarity.

            This video consists of the following topics:

• What is Item Analysis in Multivariate Tools?

• Data considerations for Item Analysis

• Example of Item Analysis

• Conduct Item Analysis in Minitab with a practical example

• Detailed interpretation of results from Item Analysis including:

                   1. Correlation Matrix

                   2. Matrix Plot

                   3. Item and Total Statistics

                   4. Cronbach's Alpha value and its interpretation

                   5. Omitted Item Statistics with Item-adjusted total correlation, Squared multiple correlations and Cronbach's Alpha value, and

• The conclusion from the analysis.

In this video, we are going to learn the 6th tool of multivariate analysis in Minitab software, that is, Cluster Observations Analysis with the help of a practical example for easy understanding and better clarity.

            The Cluster Observations Analysis is used to join observations that share common characteristics into groups. This analysis is appropriate when you do not have any initial information about how to form the groups.

             This video consists of the following topics:

• What is Cluster Observations Analysis?

• Data Considerations (Requirements) for Cluster Observations Analysis

• Example of Cluster Observations Analysis

• Detailed procedure to conduct Cluster Observations Analysis in Minitab

• Selection of various options while conducting Cluster Observations Analysis

• Final Result of an Analysis in Session window and Dendrogram

In the last video on Cluster observations analysis, we had seen the practical application of it, and the detailed procedure to perform this analysis in Minitab.

            In this video, we are going to learn the detailed interpretation of results got after performing Cluster Observations Analysis.

            This video consists of the following topics:

• Detailed interpretation of results from session window including Similarity level, Distance level, Cluster joints, and Number of Clusters

• Detailed interpretation of results from graph window including the selection for Number of Clusters and Observations in each Cluster

In this video, we are going to learn the 7th tool of multivariate analysis, that is, "Cluster Variables Analysis" with the help of a practical example in Minitab for easy understanding and better clarity.

            The Cluster Variables Analysis is used to group variables into clusters that share common characteristics. Clustering variables allows you to reduce the number of variables for analysis. Similar to Cluster observations, this analysis is also appropriate when you do not have any initial information about how to form the groups.

            This video consists of the following topics:

• What is Cluster Variables Analysis?

• Data Considerations (Requirements) for Cluster Variables Analysis

• Example of Cluster Variables Analysis

• Detailed procedure to conduct Cluster Variables Analysis in Minitab

• Selection of various options while conducting Cluster Variables Analysis

• Detailed interpretation of results from session window including Similarity level, Distance level, Cluster joints, and Number of Clusters

• Detailed interpretation of results from graph window including the selection for Number of Clusters and Variables in each Cluster

In this video, we are going to learn the 8th tool of multivariate analysis, i.e. "Cluster K-Means Analysis" with the help of a practical example in Minitab for easy understanding and better clarity.

            The Cluster K-Means Analysis is used to group observations into clusters that share common characteristics. This is the next step after Cluster Observations Analysis. This method is appropriate when you have sufficient information to make good starting cluster designations for the clusters.

            This video consists of the following topics:

• What is Cluster K-means Analysis?

• Data Considerations (Requirements) for Cluster K-means Analysis

• Example of Cluster K-means Analysis

• Detailed procedure to conduct Cluster K-means Analysis in Minitab

• Selection of various options while conducting Cluster K-means Analysis

• Detailed interpretation of results from session window including Number of Clusters, Standardize variable, Initial partition level, etc.

In the last video, we had seen the 3rd tool of multivariate analysis in Minitab software i.e. Item Analysis with the help of a practical example.

            In this video, we are going to learn the 4th tool of multivariate analysis in Minitab software i.e. Discriminant Analysis with the help of a practical example for easy understanding and better clarity.

            This video consists of the following topics:

• What is Discriminant Analysis in Multivariate Tools?

• Data considerations for Discriminant Analysis

• Example of Discriminant Analysis

• Conduct Discriminant Analysis in Minitab with a practical example

• Detailed interpretation of results from Discriminant Analysis including:

                  1. Summary of Classification

                  2. Correct Classifications

                  3. Squared Distance between Groups

                  4. Linear Discriminant Function for Groups

                  5. Summary of Misclassified Observations, and

• The conclusion from the Discriminant analysis.

In the last video, we had seen the 4th tool of multivariate analysis in Minitab software i.e. Discriminant Analysis with the help of a practical example.

            In this video, we are going to learn the 5th tool of multivariate analysis in Minitab software i.e. Simple Correspondence Analysis with the help of a practical example for easy understanding and better clarity.

            This video consists of the following topics:

• What is Simple Correspondence Analysis in Multivariate Tools?

• Data considerations for Simple Correspondence Analysis

• Example of Simple Correspondence Analysis

• Conduct Simple Correspondence Analysis in Minitab with a practical example

• Detailed interpretation of results from Simple Correspondence Analysis including:

                   1. Analysis of Contingency table with a number of components, Inertia, Proportion, and Cumulative Proportion value

                   2. Row contributions with Principal Components

                   3. Supplementary rows with Principal Components

                   4. Column contributions with Principal Components

                   5. Symmetric plot showing rows

                   6. Asymmetric row plot showing rows and columns, and

• The conclusion from the Simple Correspondence analysis.

In this video, we are going to learn the 9th and the last tool of multivariate analysis in Minitab software i.e. "Multiple Correspondence Analysis" with the help of a practical example for easy understanding and better clarity.

            Multiple Correspondence Analysis is used to explore the relationships of three or more categorical variables. Multiple correspondence analysis performs a simple correspondence analysis on a matrix of indicator variables, where each column of the matrix corresponds to a level of the categorical variable.

            This video consists of the following topics:

• What is Multiple Correspondence Analysis?

• Data Considerations (Requirements) for Multiple Correspondence Analysis

• Example of Multiple Correspondence Analysis

• Detailed procedure to conduct Multiple Correspondence Analysis in Minitab

• Selection of various options while conducting Multiple Correspondence Analysis

• Detailed interpretation of results from session window including Quality Level, Mass, Correlation, Inertia, Coordinates, etc.

• Detailed interpretation of results from graph window indicating components and categories on axes.