Expansion Potential Metrics Correlation Heatmap

General Description of the Dataset
The dataset data_expansion is designed to simulate key metrics that are essential for evaluating a company's potential for expansion. It consists of four primary variables, each representing a different dimension of expansion strategy and operational capacity:
Adoption Rate: This variable quantifies the percentage of customers or users who adopt a new product or service. It is generated using a normal distribution with a mean of 40 and a standard deviation of 12. This indicates that while many observations will cluster around the average adoption rate, there is variability reflecting differences in market acceptance and consumer willingness to engage with new offerings.
Adaptability: This metric assesses how well a company can adjust to changes in the market or its operational environment. Simulated using a uniform distribution ranging from 1 to 5, where higher values indicate greater adaptability, this score helps evaluate a company's responsiveness to evolving consumer needs and market dynamics.
Expansion Cost: This variable represents the financial investment required for expansion efforts, generated from a normal distribution with a mean of $100,000 and a standard deviation of $20,000. This reflects the typical costs associated with scaling operations, including expenses related to marketing, infrastructure development, and workforce expansion.
Scalability: This metric evaluates how easily a business can grow its operations without being hindered by its existing structure or resource limitations. Like adaptability, scalability is generated using a uniform distribution from 1 to 5, where higher scores indicate better potential for scaling operations effectively.
The dataset contains 100 observations for each variable, providing a robust foundation for analysis.