Da Pearson Correlation — Free Data Analytics Tutorial
Learn Da Pearson Correlation in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Pearson Correlation in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Da Pearson Correlation in Data Analytics
Pearson correlation (r) measures linear relationship between two variables. Ranges from -1 (perfect negative) to +1 (perfect positive).
r = 1: Perfect positive (as one increases, other increases proportionally). r = -1: Perfect negative. r = 0: No linear relationship.
Interpretation: r > 0.7 is strong, 0.3-0.7 is moderate, < 0.3 is weak.
Important: Correlation ≠ Causation. Just because ice cream sales and drowning are correlated doesn't mean ice cream causes drowning.
Da Pearson Correlation — Syntax
# Pearson correlation formula: # r = Σ((x - mean_x)(y - mean_y)) / (n * σx * σy) # # Pandas: df['col1'].corr(df['col2']) # NumPy: np.corrcoef(x, y)
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