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How to Find Correlation Between Two Variables Using Kendall's Tau

Kendall correlation output

Condition for Using Kendall's Tau for Correlation

  • Description:
    The Kendall rank correlation coefficient (also known as Kendall's tau) is a measure of correlation between two variables that assesses how the ranks of the data points align. It is a non-parametric test, meaning it does not assume any specific distribution for the data.

    Kendall's tau coefficient ranges between -1 and 1:
    • 1: Indicates a perfect positive relationship (as one variable increases, the other also increases in rank order).
    • -1: Indicates a perfect negative relationship (as one variable increases, the other decreases in rank order).
    • 0: Indicates no correlation.
Step-by-Step Process
  • Import Required Libraries:
    Use Pandas for handling the data.
    Use SciPy to compute the Kendall correlation coefficient using the kendalltau() function.
  • Prepare the Data:
    Ensure the data is in a numerical format (either integer or float).
  • Use the kendalltau() Function:
    This function from SciPy computes the Kendall rank correlation coefficient between two variables.
  • Interpret the Result:
    The result will include the correlation coefficient (tau) and a p-value to assess the statistical significance of the correlation.
Sample Source Code
  • # Code for finding correlation between 2 variables using Kendall's method

    import pandas as pd
    from scipy.stats import kendalltau

    data = {
    "Variable1": [10, 20, 30, 40, 50],
    "Variable2": [15, 25, 35, 45, 55]
    }
    df = pd.DataFrame(data)

    # Calculate Kendall's Tau correlation between Variable1 and Variable2
    tau, p_value = kendalltau(df['Variable1'], df['Variable2'])

    print(f"Kendall's Tau: {tau}")
    print(f"P-value: {p_value}")
Screenshots
  • Kendall Tau correlation output

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