Numerical vs Numerical: Correlation Analysis

While scatter plots give us a visual sense of the relationship between two numerical variables, we often need a quantitative measure to describe the strength and direction of this association. Correlation analysis provides precisely this: a statistical metric summarizing how closely two variables move together.

Understanding Correlation Coefficients

A correlation coefficient is a numerical value ranging from $-1$ to $+1$. It tells us two things about the relationship between two variables:

1. Direction: A positive sign indicates a positive relationship (as one variable increases, the other tends to increase). A negative sign indicates a negative relationship (as one variable increases, the other tends to decrease).
2. Strength: The absolute value of the coefficient indicates the strength of the relationship. A value closer to $1$ (or $-1$) shows a stronger linear association, while a value closer to $0$ suggests a weaker linear association or no linear relationship at all.

Pearson's Correlation Coefficient ($r$)

The most commonly used correlation coefficient is Pearson's product-moment correlation coefficient, often denoted as $r$. It specifically measures the strength and direction of a linear relationship between two continuous variables.

The value of Pearson's $r$ can be interpreted as follows:

• $r = +1$: Perfect positive linear relationship.
• $r = -1$: Perfect negative linear relationship.
• $r = 0$: No linear relationship. Values near zero indicate a very weak or non-existent linear association.
• $0 < r < 1$: Positive linear relationship of varying strength.
• $-1 < r < 0$: Negative linear relationship of varying strength.

Pearson's $r$ assumes that the variables are approximately normally distributed and that the relationship between them is linear. It can also be sensitive to outliers.

Calculating Pearson's $r$ with Pandas

Pandas makes calculating Pearson's correlation straightforward using the .corr() method on a DataFrame. By default, it computes the pairwise correlation of columns.

import pandas as pd

# Sample DataFrame
data = {'Temperature': [20, 22, 25, 18, 23, 28],
        'Ice_Cream_Sales': [150, 170, 200, 130, 180, 230],
        'Umbrella_Sales': [50, 45, 30, 60, 40, 20]}
df = pd.DataFrame(data)

# Calculate the pairwise Pearson correlation matrix
correlation_matrix = df.corr(method='pearson')

print(correlation_matrix)

This will output a matrix where each cell $(i, j)$ contains the Pearson correlation coefficient between column $i$ and column $j$. The diagonal elements will always be $1$, as a variable is perfectly correlated with itself.

                 Temperature  Ice_Cream_Sales  Umbrella_Sales
Temperature         1.000000         0.993399       -0.976701
Ice_Cream_Sales     0.993399         1.000000       -0.954676
Umbrella_Sales     -0.976701        -0.954676        1.000000

From this output, we see a strong positive correlation ($r \approx 0.99$) between Temperature and Ice_Cream_Sales, and a strong negative correlation ($r \approx -0.98$) between Temperature and Umbrella_Sales.

To calculate the correlation between just two specific columns (Series), you can use the .corr() method on one Series and pass the other as an argument:

# Correlation between Temperature and Ice_Cream_Sales
temp_sales_corr = df['Temperature'].corr(df['Ice_Cream_Sales'])
print(f"Correlation between Temperature and Ice Cream Sales: {temp_sales_corr:.4f}")
# Output: Correlation between Temperature and Ice Cream Sales: 0.9934

Spearman's Rank Correlation Coefficient ($\rho$)

What if the relationship between variables isn't linear, but still generally increasing or decreasing (monotonic)? Or what if the data includes significant outliers or doesn't follow a normal distribution? In such cases, Pearson's $r$ might not be the best measure.

Spearman's rank correlation coefficient, denoted as $\rho$ (rho), is a non-parametric alternative. It assesses how well the relationship between two variables can be described using a monotonic function. Instead of using the actual values, Spearman's correlation calculates Pearson's correlation on the ranks of the data.

• $\rho = +1$: Perfect positive monotonic relationship.
• $\rho = -1$: Perfect negative monotonic relationship.
• $\rho = 0$: No monotonic relationship.

It's less sensitive to outliers than Pearson's $r$ and doesn't assume linearity or normality.

Calculating Spearman's $\rho$ with Pandas

You can calculate Spearman's correlation by specifying method='spearman' in the .corr() method:

# Calculate the pairwise Spearman correlation matrix
spearman_corr_matrix = df.corr(method='spearman')

print(spearman_corr_matrix)

                 Temperature  Ice_Cream_Sales  Umbrella_Sales
Temperature         1.000000         1.000000       -0.942857
Ice_Cream_Sales     1.000000         1.000000       -0.942857
Umbrella_Sales     -0.942857        -0.942857        1.000000

Notice the values might differ slightly from Pearson's, especially if non-linear monotonic relationships or outliers are present. In this specific example, the relationship is very close to linear, so the Spearman results are similar but not identical to Pearson's for the Umbrella_Sales correlations.

Interpretation and Caveats

• Strength Guidelines: While context is important, general guidelines sometimes classify correlations:
  • $|r|$ or $|\rho|$ between $0.7$ and $1.0$: Strong correlation.
  • $|r|$ or $|\rho|$ between $0.4$ and $0.7$: Moderate correlation.
  • $|r|$ or $|\rho|$ between $0.1$ and $0.4$: Weak correlation.
  • $|r|$ or $|\rho|$ below $0.1$: Very weak or negligible correlation.
• Correlation is NOT Causation: This is a fundamental principle. A strong correlation between two variables does not automatically mean that one causes the other. There might be a third, unobserved variable (a confounder) influencing both, or the relationship might be purely coincidental. For instance, ice cream sales and crime rates might both increase during hot summer months, correlating strongly, but eating ice cream doesn't cause crime.
• Linearity vs. Monotonicity: Remember Pearson measures linear association, while Spearman measures monotonic association. A correlation coefficient near zero doesn't necessarily mean no relationship exists, only that there isn't a linear (for Pearson) or monotonic (for Spearman) one. Complex, non-linear relationships might still be present, which is why visual inspection with scatter plots remains essential.

Correlation analysis provides a compact numerical summary of the relationship between pairs of numerical variables, complementing the visual insights gained from scatter plots and guiding further investigation or feature selection.