Heat Map Example

Heat Map Example

Problem Statement

We have collected data on several different models of cars with respect to several parameters. Create a heat map for cyl, disp, hp and wt. Data is attached in attachement.

How to perform analysis

Step 1: Open Sigma Magic
  1. Click on the Sigma Magic button on the Excel toolbar.
  2. Click on the New button to create a new project.
Step 2: Add the analysis template
  1. Click on the Tool Wizard to add the analysis template.
  2. Click on Analytics and then Heat Map.


Step 3: Specify analysis options
A new worksheet will be added to your workbook. Analysis Setup will be automatically opened, in the setup tab specify the survey results.



Click on the Data to specify the data required for this analysis.



If you need to make changes to the charts, specify the optional settings in the Charts tab.

 Labels:

  • Add a title for the chart.
  • Label the X-axis and Y-axis appropriately.

Appearance:

  • Adjust colors, font sizes, or other visual elements as needed.
  • Enable/disable gridlines or background shading.


Click the Verify tab to ensure all the inputs are okay and shown in a green checkmark.



Step 4: Generate analysis result
Click OK and then click Compute Outputs to get the final results.


Interpretating Results


    • The dataset consists of continuous numerical values.
    • The heat map is generated based on four variables: disp (displacement), hp (horsepower), wt (weight), and cyl (cylinders).
    • Both row and column dendrograms are used, meaning hierarchical clustering has been applied to group similar data points together.
    • This helps in identifying patterns and relationships between observations.
    • The lighter colors (yellow/white) indicate lower values, while the darker colors (red/maroon) represent higher values.
    • This makes it easy to spot high and low values in the dataset visually.
    • The column dendrogram clusters wt, cyl, hp, and disp, indicating some correlation between these variables.
    • disp and hp seem to have higher values compared to wt and cyl.
    • The $rowInd variable suggests that the dataset rows have been rearranged based on similarity.
    • The clustering helps in grouping similar observations together.
    • The $colInd variable [1 3 4 2] indicates that the columns were reordered during clustering.
    • This means disp, hp, cyl, and wt were placed in a different order than their original sequence.
    • The hierarchical tree (dendrogram) suggests that certain values of disp and hp cluster together, possibly showing a relationship between engine displacement and horsepower.
    • Closer branches in the dendrogram indicate higher similarity between data points.
    • The dataset has variations across different rows, with some observations having higher horsepower and displacement values while others remain lower.
    • Weight (wt) and cylinder count (cyl) seem to be relatively lower in variation compared to hp and disp.
    • The absence of specific row and column values (NULL values) suggests no additional constraints were applied to force a particular ordering.
    • The clustering is purely based on data similarity rather than predefined sorting.A heat map has been successfully created with hierarchical clustering.
    • The visualization helps in identifying relationships, grouping similar data points, and spotting patterns in the dataset effectively.

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