Pearson Correlation

 1. The program code

import pandas as pd

from scipy.stats import pearsonr


# Load dataset

df = pd.read_csv("gapminder.csv")


# Convert variables to numeric

df["incomeperperson"] = pd.to_numeric(df["incomeperperson"], errors="coerce")

df["internetuserate"] = pd.to_numeric(df["internetuserate"], errors="coerce")


# Drop missing values

df_clean = df.dropna(subset=["incomeperperson", "internetuserate"])


# Calculate Pearson correlation

r, p = pearsonr(df_clean["incomeperperson"], df_clean["internetuserate"])


print("Correlation Coefficient (r):", r)

print("p-value:", p)

print("R-squared:", r**2)


2. Output


3. Interpretation
 The Pearson correlation between income per person and internet use rate was r = 0.75, p < .0001. This indicates a strong positive linear relationship: as countries’ income per person increases, their internet usage rate also tends to increase.

When squared, R² = 0.56, meaning that about 56% of the variability in internet use rates can be explained by differences in income per person. This provides strong evidence that economic status is closely associated with digital adoption worldwide.

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